xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

d99a20277c32bb1e28312f42ab6d732f38323169

241

py

Python

quick_search/admin.py

naman1901/django-quick-search

7b93554ed9fa4721e52372f9fd1a395d94cc04a7

[ "MIT" ]

null

quick_search/admin.py

naman1901/django-quick-search

7b93554ed9fa4721e52372f9fd1a395d94cc04a7

[ "MIT" ]

2

2020-02-11T23:28:22.000Z

2020-06-05T19:27:40.000Z

quick_search/admin.py

HereWithoutPermission/django-quick-search

7b93554ed9fa4721e52372f9fd1a395d94cc04a7

[ "MIT" ]

null

from django.contrib import admin from .models import SearchResult # Register your models here. class SearchResultAdmin(admin.ModelAdmin): fields = ["query", "heading", "url", "text"] admin.site.register(SearchResult, SearchResultAdmin)

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d9a88e74a4ac032ae6e8218d9ec1ed42e6092d32

375

py

Python

app/views/web/homestack.py

geudrik/hautomation

0baae29e85cd68658a0f8578de2e36e42945053f

[ "MIT" ]

null

app/views/web/homestack.py

geudrik/hautomation

0baae29e85cd68658a0f8578de2e36e42945053f

[ "MIT" ]

null

app/views/web/homestack.py

geudrik/hautomation

0baae29e85cd68658a0f8578de2e36e42945053f

[ "MIT" ]

null

#! /usr/bin/env python2.7 # -*- coding: latin-1 -*- from flask import Blueprint from flask import current_app from flask import render_template from flask_login import login_required homestack = Blueprint("homestack", __name__, url_prefix="/homestack") @homestack.route("/", methods=["GET"]) @login_required def home(): return render_template("homestack/home.html")

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d9b55a7ee025f94a0ef3f125fa9c30f974dd7d6e

211

py

Python

abc/abc165/abc165e.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc165/abc165e.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc165/abc165e.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

N, M = map(int, input().split()) for i in range(1, M + 1): if i % 2 == 1: j = (i - 1) // 2 print(1 + j, M + 1 - j) else: j = (i - 2) // 2 print(M + 2 + j, 2 * M + 1 - j)

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d9b8d42e905cba910e6a30f7d6f38e82d05ab46c

2,110

py

Python

graphdb/transformer.py

muggat0n/graphdb

56dfd5ef8a3321abc6a919faee47494bbe059080

[ "MIT" ]

2

2020-08-28T13:42:38.000Z

2020-09-05T03:13:45.000Z

graphdb/transformer.py

muggat0n/graphdb

56dfd5ef8a3321abc6a919faee47494bbe059080

[ "MIT" ]

null

graphdb/transformer.py

muggat0n/graphdb

56dfd5ef8a3321abc6a919faee47494bbe059080

[ "MIT" ]

null

""" A query transformer is a function that accepts a program and returns a program, plus a priority level. Higher priority transformers are placed closer to the front of the list. We’re ensuring is a function, because we’re going to evaluate it later 31 . We’ll assume there won’t be an enormous number of transformer additions, and walk the list linearly to add a new one. We’ll leave a note in case this assumption turns out to be false — a binary search is much more time-optimal for long lists, but adds a little complexity and doesn’t really speed up short lists. """ class Transformer: def __init__(self): self.T = [] def transform(self, program): return program """ Dagoba.T = [] # transformers (more than meets the eye) """ """ Dagoba.addTransformer = function(fun, priority) { if(typeof fun != 'function') return Dagoba.error('Invalid transformer function') for(var i = 0; i < Dagoba.T.length; i++) # OPT: binary search if(priority > Dagoba.T[i].priority) break Dagoba.T.splice(i, 0, {priority: priority, fun: fun}) } """ """ Dagoba.transform = function(program) { return Dagoba.T.reduce(function(acc, transformer) { return transformer.fun(acc) }, program) } """ """ Dagoba.addAlias = function(newname, oldname, defaults) { defaults = defaults || [] # default arguments for the alias Dagoba.addPipetype(newname, function() {}) # because there's no method catchall in js Dagoba.addTransformer(function(program) { return program.map(function(step) { if(step[0] != newname) return step return [oldname, Dagoba.extend(step[1], defaults)] }) }, 100) # these need to run early, so they get a high priority } """ """ Dagoba.extend = function(list, defaults) { return Object.keys(defaults).reduce(function(acc, key) { if(typeof list[key] != 'undefined') return acc acc[key] = defaults[key] return acc }, list) } """

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d9b9563b7aae9c46b0fbd98073d96eeedfaec4aa

91

py

Python

Courses/1 month/2 week/day 6/Formula.py

emir-naiz/first_git_lesson

1fecf712290f6da3ef03deff518870d91638eb69

[ "MIT" ]

null

Courses/1 month/2 week/day 6/Formula.py

emir-naiz/first_git_lesson

1fecf712290f6da3ef03deff518870d91638eb69

[ "MIT" ]

null

Courses/1 month/2 week/day 6/Formula.py

emir-naiz/first_git_lesson

1fecf712290f6da3ef03deff518870d91638eb69

[ "MIT" ]

null

summary = 0 i = 0 while i < 5: summary = summary + i print(summary) i = i + 1

11.375

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null

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d9b9af3bd25b0d2f9357446b0ff43e3ab614b141

243

py

Python

tests/image_saver/image_saver_7.py

Vicken-Ghoubiguian/Imtreat

1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0

[ "MIT" ]

null

tests/image_saver/image_saver_7.py

Vicken-Ghoubiguian/Imtreat

1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0

[ "MIT" ]

null

tests/image_saver/image_saver_7.py

Vicken-Ghoubiguian/Imtreat

1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0

[ "MIT" ]

null

import imtreat img = imtreat.imageManagerClass.openImageFunction("../images/soleil.png", 0) img = imtreat.definedModesClass.detailEnhanceFunction(img) imtreat.imageManagerClass.saveImageFunction("/Téléchargements/", "image_1", ".png", img)

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3

d9c389b63a2c9720abef56190237f31a2306da19

1,972

py

Python

src/biotite/copyable.py

danijoo/biotite

22072e64676e4e917236eac8493eed4c6a22cc33

[ "BSD-3-Clause" ]

208

2018-04-20T15:59:42.000Z

2022-03-22T07:47:12.000Z

src/biotite/copyable.py

danielmuthama/biotite

cb238a8d8d7dc82b3bcea274d7d91d5c876badcd

[ "BSD-3-Clause" ]

121

2017-11-15T14:52:07.000Z

2022-03-30T16:31:41.000Z

src/biotite/copyable.py

danielmuthama/biotite

cb238a8d8d7dc82b3bcea274d7d91d5c876badcd

[ "BSD-3-Clause" ]

49

2018-07-19T09:06:24.000Z

2022-03-23T17:21:34.000Z

# This source code is part of the Biotite package and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ = "biotite" __author__ = "Patrick Kunzmann" __all__ = ["Copyable"] import abc class Copyable(metaclass=abc.ABCMeta): """ Base class for all objects, that should be copyable. The public method `copy()` first creates a fresh instance of the class of the instance, that is copied via the `__copy_create__()` method. All variables, that could not be set via the constructor, are then copied via `__copy_fill__()`, starting with the method in the uppermost base class and ending with the class of the instance to be copied. This approach solves the problem of encapsulated variables in superclasses. """ def copy(self): """ Create a deep copy of this object. Returns ------- copy A copy of this object. """ clone = self.__copy_create__() self.__copy_fill__(clone) return clone def __copy_create__(self): """ Instantiate a new object of this class. Only the constructor should be called in this method. All further attributes, that need to be copied are handled in `__copy_fill__()` Do not call the `super()` method here. This method must be overridden, if the constructor takes parameters. Returns ------- copy A freshly instantiated copy of *self*. """ return type(self)() def __copy_fill__(self, clone): """ Copy all necessary attributes to the new object. Always call the `super()` method as first statement. Parameters ---------- clone The freshly instantiated copy of *self*. """ pass

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d9f1f15178cb9e26d9b4f91695b333a07eaa59d6

74,778

py

Python

sqlova/model/nl2sql/wikisql_models.py

guotong1988/Rule-SQL

e826c0d659c8b35a72b64aa2b50d4d943fdd70f1

[ "Apache-2.0" ]

15

2019-07-25T12:13:31.000Z

2020-10-17T13:42:58.000Z

sqlova/model/nl2sql/wikisql_models.py

guotong1988/Rule-SQL

e826c0d659c8b35a72b64aa2b50d4d943fdd70f1

[ "Apache-2.0" ]

1

2020-01-07T05:49:15.000Z

2020-04-22T01:22:00.000Z

sqlova/model/nl2sql/wikisql_models.py

guotong1988/Rule-SQL

e826c0d659c8b35a72b64aa2b50d4d943fdd70f1

[ "Apache-2.0" ]

3

2019-10-01T09:14:35.000Z

2020-07-18T08:39:48.000Z

# Copyright 2019-present NAVER Corp. # Apache License v2.0 # Wonseok Hwang import os, json from copy import deepcopy from matplotlib.pylab import * import torch import torch.nn as nn import torch.nn.functional as F device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from sqlova.utils.utils import topk_multi_dim from sqlova.utils.utils_wikisql import * class Seq2SQL_v1(nn.Module): def __init__(self, input_size, hidden_size, num_layer, dropout, number_cond_ops, number_agg_ops, old=False): super(Seq2SQL_v1, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.max_where_number = 4 self.number_cond_ops = number_cond_ops self.number_agg_ops = number_agg_ops self.select_column_predict = SelectColumnPredict(input_size, hidden_size, num_layer, dropout) self.select_agg_predict = SelectAggPredict(input_size, hidden_size, num_layer, dropout, number_agg_ops, old=old) self.where_number_predict = WhereNumberPredict(input_size, hidden_size, num_layer, dropout) self.wcp = WhereColumnPredict(input_size, hidden_size, num_layer, dropout) self.wop = WhereOpPredict(input_size, hidden_size, num_layer, dropout, number_cond_ops) self.wvp = WhereValuePredict_startend(input_size, hidden_size, num_layer, dropout, number_cond_ops, old=old) # start-end-search-discriminative model # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # sc s_sc,s_sc_softmax = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=show_p_sc) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # sa s_sa,s_sa_softmax = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, number_header, pr_sc, show_p_sa=show_p_sa) if g_sa: # it's not necessary though. pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # wn s_wn,s_wn_softmax = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wn=show_p_wn) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc s_wc,s_wc_softmax = self.wcp(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wc=show_p_wc, penalty=True) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo s_wo,s_wo_softmax = self.wop(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, show_p_wo=show_p_wo) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv s_wv,s_wv_softmax = self.wvp(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, wo=pr_wo, show_p_wv=show_p_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, s_sc_softmax, s_sa_softmax, s_wn_softmax, s_wc_softmax, s_wo_softmax, s_wv_softmax def beam_forward(self, emb_question, len_question, emb_header, len_header_token, l_header, engine, tb, nlu_t, nlu_wp_t, wp_to_wh_index, nlu, beam_size=4, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): """ Execution-guided beam decoding. """ # sc s_sc,_ = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_sc=show_p_sc) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.number_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. for i_beam in range(beam_size): pr_sc = list( array(pr_sc_beam)[:,i_beam] ) s_sa,_ = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=show_p_sa) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:,i_beam,:] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) # First flatten to 1-d idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS),beam_idx_sca,0] pr_sa = idxs_arr[range(bS),beam_idx_sca,1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # Now, Where-clause beam search. s_wn,_ = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=show_p_wn) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc s_wc,_ = self.wcp(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc=show_p_wc, penalty=True) prob_wc = F.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.max_where_number] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.max_where_number]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b,:] = prob_wc[b,pr_wc_max1] # get most probable max_wn where-clouses # wo s_wo_max,_ = self.wop(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, show_p_wo=show_p_wo) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, max_wn, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] for i_op in range(self.number_cond_ops - 1): pr_wo_temp = [[i_op] * self.max_where_number] * bS # wv s_wv,_ = self.wvp(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, wo=pr_wo_temp, show_p_wv=show_p_wv) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam = pred_wvi_se_beam(self.max_where_number, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) # pr_wvi_beam = [B, max_wn, k_logit**2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, max_wn, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.max_where_number, self.number_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.max_where_number): for i_op in range(self.number_cond_ops - 1): # do not use final one for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wc = prob_wc_max[b, i_wn] p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc * p_wo * p_wv # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.max_wn: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1 for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [wp_to_wh_index[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc[b], pr_sa[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] pr_wn_based_on_prob = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len( conds_max[b] ) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn+1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_sql_i.append(pr_sql_i1) # s_wv = [B, max_wn, max_nlu_tokens, 2] return prob_sca, prob_w, prob_wn_w, pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_sql_i class SelectColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(SelectColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.sc_out = nn.Sequential(nn.Tanh(), nn.Linear(2 * hidden_size, 1)) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, number_header) # [b, header, dim] bS = len(number_header) mL_n = max(len_question) # [bS, max_len_header, 100] * [bS, 100, mL_n] -> [bS, max_len_header, mL_n] att_h = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att_h[b, :, l_n1:] = -10000000000 p_n = self.softmax_dim2(att_h) if show_p_sc: # p = [b, header, n] if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001, figsize=(12,3.5)) # subplot(6,2,7) subplot2grid((7,2), (3, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(number_header[0]): color_idx = i_h % len(_color) plot(p_n[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('sc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # p_n [ bS, max_len_header, mL_n] -> [ bS, max_len_header, mL_n, 1] # wenc_n [ bS, mL_n, 100] -> [ bS, 1, mL_n, 100] # -> [bS, max_len_header, mL_n, 100] -> [bS, max_len_header, 100] c_n = torch.mul(p_n.unsqueeze(3), encoded_question.unsqueeze(1)).sum(dim=2) vec = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) score_select_column = self.sc_out(vec).squeeze(2) # [bS, max_len_header, 1] -> [bS, max_len_header] score_select_column_softmax = self.softmax_dim_1(score_select_column) # Penalty max_len_header = max(number_header) for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column[b, l_header1:] = -10000000000 for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column_softmax[b, l_header1:] = 0 return score_select_column,score_select_column_softmax class SelectAggPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_agg_ops=-1, old=False): super(SelectAggPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.sa_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_agg_ops)) # Fixed number of aggregation operator. self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) if old: # for backwoard compatibility self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) mL_n = max(len_question) wenc_header_ob = encoded_header[list(range(bS)), pr_sc] # list, so one sample for each batch. # [bS, question_len, 100] * [bS, 100, 1] -> [bS, question_len] att = torch.bmm(self.W_att(encoded_question), wenc_header_ob.unsqueeze(2)).squeeze(2) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, l_n1:] = -10000000000 # [bS, question_len] p = self.softmax_dim1(att) if show_p_sa: if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,3) cla() plot(p[0].data.numpy(), '--rs', ms=7) title('sa: nlu_weight') grid(True) fig.tight_layout() fig.canvas.draw() show() # [bS, question_len, 100] * ( [bS, question_len, 1] -> [bS, question_len, 100]) # -> [bS, question_len, 100] -> [bS, 100] c_n = torch.mul(encoded_question, p.unsqueeze(2).expand_as(encoded_question)).sum(dim=1) s_sa = self.sa_out(c_n) s_sa_softmax = self.softmax_dim_1(s_sa) return s_sa,s_sa_softmax class WhereNumberPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, ): super(WhereNumberPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att_h = nn.Linear(hidden_size, 1) self.W_hidden = nn.Linear(hidden_size, num_layer * hidden_size) self.W_cell = nn.Linear(hidden_size, num_layer * hidden_size) self.W_att_n = nn.Linear(hidden_size, 1) self.wn_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, self.mL_w + 1)) # max number (4 + 1) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=False): # Encode encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, max_len_header, dim] bS = len(l_header) max_len_question = max(len_question) max_len_header = max(l_header) # mL_h = max(len_header_token) # (self-attention?) column Embedding? # [B, max_len_header, 100] -> [B, max_len_header, 1] -> [B, max_len_header] att_h = self.W_att_h(encoded_header).squeeze(2) # Penalty for b, l_header1 in enumerate(l_header): if l_header1 < max_len_header: att_h[b, l_header1:] = -10000000000 p_h = self.softmax_dim1(att_h) if show_p_wn: if p_h.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,5) cla() plot(p_h[0].data.numpy(), '--rs', ms=7) title('wn: header_weight') grid(True) fig.canvas.draw() show() # input('Type Eenter to continue.') # [B, max_len_header, 100] * [ B, max_len_header, 1] -> [B, max_len_header, 100] -> [B, 100] c_header = torch.mul(encoded_header, p_h.unsqueeze(2)).sum(1) # [B, 100] --> [B, 2*100] Enlarge because there are two layers. hidden = self.W_hidden(c_header) # [B, 4, 200/2] hidden = hidden.view(bS, self.num_layer * 2, int( self.hidden_size / 2)) # [4, B, 100/2] # number_of_layer_layer * (bi-direction) # lstm input convention. hidden = hidden.transpose(0, 1).contiguous() cell = self.W_cell(c_header) # [B, 4, 100/2] cell = cell.view(bS, self.num_layer * 2, int(self.hidden_size / 2)) # [4, B, 100/2] cell = cell.transpose(0, 1).contiguous() wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=(hidden, cell), last_only=False) # [b, n, dim] att_n = self.W_att_n(wenc_n).squeeze(2) # [B, max_len, 100] -> [B, max_len, 1] -> [B, max_len] # Penalty for b, l_n1 in enumerate(len_question): if l_n1 < max_len_question: att_n[b, l_n1:] = -10000000000 p_n = self.softmax_dim1(att_n) if show_p_wn: if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,6) cla() plot(p_n[0].data.numpy(), '--rs', ms=7) title('wn: nlu_weight') grid(True) fig.canvas.draw() show() # input('Type Enter to continue.') # [B, mL_n, 100] *([B, mL_n] -> [B, mL_n, 1] -> [B, mL_n, 100] ) -> [B, 100] c_n = torch.mul(wenc_n, p_n.unsqueeze(2).expand_as(wenc_n)).sum(dim=1) s_wn = self.wn_out(c_n) s_wn_softmax = self.softmax_dim_1(s_wn) return s_wn,s_wn_softmax # where column predict class WhereColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(WhereColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_out = nn.Sequential( nn.Tanh(), nn.Linear(2 * hidden_size, 1) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc, penalty=True): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] # attention # wenc = [bS, mL, hidden_size] # att = [bS, max_len_header, mL_n] # att[b, i_h, j_n] = p(j_n| i_h) att = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # penalty to blank part. mL_n = max(len_question) for b_n, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b_n, :, l_n1:] = -10000000000 # make p(j_n | i_h) p = self.softmax_dim2(att) if show_p_wc: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); # subplot(6,2,7) subplot2grid((7,2), (3, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(l_header[0]): color_idx = i_h % len(_color) plot(p[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # max nlu context vectors # [bS, max_len_header, mL_n]*[bS, max_len_header, mL_n] encoded_question = encoded_question.unsqueeze(1) # [ b, n, dim] -> [b, 1, n, dim] p = p.unsqueeze(3) # [b, header, n] -> [b, header, n, 1] c_n = torch.mul(encoded_question, p).sum(2) # -> [b, header, dim], c_n for each header. y = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) # [b, header, 2*dim] score = self.W_out(y).squeeze(2) # [b, header] score[torch.isnan(score)] = 0 score_softmax = self.softmax_dim_1(score) if penalty: for b, l_header1 in enumerate(l_header): score[b, l_header1:] = -1e+10 for b, l_header1 in enumerate(l_header): score_softmax[b, l_header1:] = 0 return score,score_softmax # where op predict class WhereOpPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=3): super(WhereOpPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.wo_out = nn.Sequential( nn.Linear(2*hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_cond_ops) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wenc_n=None, show_p_wo=False): # Encode if not wenc_n: wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) # wn wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) * [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n| selected_col ) if show_p_wo: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wo: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # [bS, 5-1, dim] -> [bS, 5-1, 3] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob)], dim=2) s_wo = self.wo_out(vec) s_wo_softmax = self.softmax_dim_1(s_wo) return s_wo,s_wo_softmax class WhereValuePredict_startend(nn.Module): """ Discriminative model Get start and end. Here, classifier for [ [투수], [팀1], [팀2], [연도], ...] Input: Encoded nlu & selected column. Algorithm: Encoded nlu & selected column. -> classifier -> mask scores -> ... """ def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=4, old=False): super(WhereValuePredict_startend, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_op = nn.Linear(n_cond_ops, hidden_size) # self.W_n = nn.Linear(hidden_size, hidden_size) if old: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, 2) ) else: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 2) ) # self.wv_out = nn.Sequential( # nn.Linear(3 * hidden_size, hidden_size), # nn.Tanh(), # nn.Linear(hidden_size, self.gdkL) # ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wo, wenc_n=None, show_p_wv=False): # Encode if not wenc_n: wenc_n, hout, cout = encode(self.enc_n, emb_question, len_question, return_hidden=True, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) * [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # Column attention # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n| selected_col ) if show_p_wv: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wv: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # Select observed headers only. # Also generate one_hot vector encoding info of the operator # [B, 4, dim] wenc_op = [] for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections wenc_op1 = torch.zeros(self.mL_w, self.n_cond_ops) wo1 = wo[b] idx_scatter = [] l_wo1 = len(wo1) for i_wo11 in range(self.mL_w): if i_wo11 < l_wo1: wo11 = wo1[i_wo11] idx_scatter.append([int(wo11)]) else: idx_scatter.append([0]) # not used anyway wenc_op1 = wenc_op1.scatter(1, torch.tensor(idx_scatter), 1) wenc_op.append(wenc_op1) # list to [B, 4, dim] tensor. wenc_op = torch.stack(wenc_op) # list to tensor. wenc_op = wenc_op.to(device) # Now after concat, calculate logits for each token # [bS, 5-1, 3*hidden_size] = [bS, 4, 300] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob), self.W_op(wenc_op)], dim=2) # Make extended vector based on encoded nl token containing column and operator information. # wenc_n = [bS, mL, 100] # vec2 = [bS, 4, mL, 400] vec1e = vec.unsqueeze(2).expand(-1,-1, mL_n, -1) # [bS, 4, 1, 300] -> [bS, 4, mL, 300] wenc_ne = wenc_n.unsqueeze(1).expand(-1, 4, -1, -1) # [bS, 1, mL, 100] -> [bS, 4, mL, 100] vec2 = torch.cat( [vec1e, wenc_ne], dim=3) # now make logits s_wv = self.wv_out(vec2) # [bS, 4, mL, 400] -> [bS, 4, mL, 2] s_wv_softmax = self.softmax_dim_1(s_wv) # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv[b, :, l_n1:, :] = -10000000000 for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv_softmax[b, :, l_n1:, :] = 0 return s_wv,s_wv_softmax def Loss_selectwhere_startend_v2(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 # loss += Loss_sc(score_select_column, ground_truth_select_column) # loss += Loss_sa(s_sa, g_sa) # loss += Loss_wn(s_wn, g_wn) # loss += Loss_wc(s_wc, g_wc) # loss += Loss_wo(s_wo, g_wn, g_wo) # loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sw_se(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 loss += Loss_sc(score_select_column, ground_truth_select_column) loss += Loss_sa(s_sa, g_sa) loss += Loss_wn(s_wn, g_wn) loss += Loss_wc(s_wc, g_wc) loss += Loss_wo(s_wo, g_wn, g_wo) loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sc(s_sc, g_sc): loss = F.cross_entropy(s_sc, torch.tensor(g_sc).to(device)) return loss def Loss_sa(s_sa, g_sa): loss = F.cross_entropy(s_sa, torch.tensor(g_sa).to(device)) return loss def Loss_wn(s_wn, g_wn): loss = F.cross_entropy(s_wn, torch.tensor(g_wn).to(device)) return loss def Loss_wc(s_wc, g_wc): # Construct index matrix bS, max_h_len = s_wc.shape im = torch.zeros([bS, max_h_len]).to(device) for b, g_wc1 in enumerate(g_wc): for g_wc11 in g_wc1: im[b, g_wc11] = 1.0 # Construct prob. p = F.sigmoid(s_wc) loss = F.binary_cross_entropy(p, im) return loss def Loss_wo(s_wo, g_wn, g_wo): # Construct index matrix loss = 0 for b, g_wn1 in enumerate(g_wn): if g_wn1 == 0: continue g_wo1 = g_wo[b] s_wo1 = s_wo[b] loss += F.cross_entropy(s_wo1[:g_wn1], torch.tensor(g_wo1).to(device)) return loss def Loss_wv_se(s_wv, g_wn, g_wvi): """ s_wv: [bS, 4, mL, 2], 4 stands for maximum # of condition, 2 tands for start & end logits. g_wvi: [ [1, 3, 2], [4,3] ] (when B=2, wn(b=1) = 3, wn(b=2) = 2). """ loss = 0 # g_wvi = torch.tensor(g_wvi).to(device) for b, g_wvi1 in enumerate(g_wvi): # for i_wn, g_wvi11 in enumerate(g_wvi1): g_wn1 = len(g_wvi1) # 有改动 # g_wn1 = g_wn[b] # 有改动 if g_wn1 == 0: continue g_wvi1 = torch.tensor(g_wvi1)[:g_wn1].to(device) # 有改动 g_st1 = g_wvi1[:,0] g_ed1 = g_wvi1[:,1] # loss from the start position loss += F.cross_entropy(s_wv[b,:g_wn1,:,0], g_st1) # print("st_login: ", s_wv[b,:g_wn1,:,0], g_st1, loss) # loss from the end position loss += F.cross_entropy(s_wv[b,:g_wn1,:,1], g_ed1) # print("ed_login: ", s_wv[b,:g_wn1,:,1], g_ed1, loss) return loss # ========= Decoder-Layer =========== class FT_s2s_1(nn.Module): """ Decoder-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, max_seq_length, n_cond_ops, n_agg_ops, old=False): super(FT_s2s_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size # hidden_size self.ls = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 self.decoder_s2s = Decoder_s2s(input_size, hidden_size, num_layer, dropout, max_seq_length) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None): score = self.decoder_s2s(wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs) return score def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size=4, beam_only=True): """ EG-guided beam-search """ score = self.decoder_s2s.EG_forward(wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only) return score class Decoder_s2s(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, max_seq_length=222, n_cond_ops=3): super(Decoder_s2s, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL = max_seq_length self.Tmax = 200 self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.decode_pn = nn.LSTM(input_size=max_seq_length, hidden_size=hidden_size, num_layers=num_layer, batch_first=True, dropout=dropout) self.W_s2s = nn.Linear(input_size, hidden_size) self.W_pnt = nn.Linear(hidden_size, hidden_size) self.wv_out = nn.Sequential(nn.Tanh(), nn.Linear(hidden_size, 1)) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None,): # Encode bS, mL_input, input_size = wenc_s2s.shape # Now, pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme # initial (current) pointer cpnt = ipnt # reshape wenc_s2s to incorporate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2*i_layer)*self.hidden_size h_ed = h_st + self.hidden_size c_st = (2*i_layer+1)*self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] if g_pnt_idxs: pnt_n = torch.zeros(bS, self.Tmax, mL_input).to(device) # one hot # assign index for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): for t, g_pnt_idx in enumerate(g_pnt_idxs1): pnt_n[b, t, g_pnt_idx] = 1 # Encode dec_pn, _ = self.decode_pn(pnt_n, (h_0, c_0)) dec_pn = dec_pn.contiguous() # [bS, T, input_size] dec_pn = dec_pn.unsqueeze(2) # Calculate score s_wv = self.wv_out( self.W_s2s(wenc_s2s) + self.W_pnt(dec_pn) ).squeeze(3) # [B, T, mL_input, dim] -> [B, T, mL_input, 1] -> [B, T, mL_input] # s_wv = [B, 4, T, mL_n] = [batch, conds, token idx, score] # penalty for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv[b, :, l_input1:] = -10000000000 else: t = 0 s_wv_list = [] cpnt_h = (h_0, c_0) while t < self.Tmax: dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Collect score-- s_wv_list.append(s_wv1) # [B, 1, mL_input] -> [B, mL_n] -> [bS*(5-1)] # (max_val, max_indices) _val, pnt_n = s_wv1.view(bS, -1).max(dim=1) # formatting pnt_n as a one-hot input. cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B * 4, 1, 200] t += 1 s_wv = torch.stack(s_wv_list, 1) # [B, s_wv = s_wv.squeeze(2) # # # Following lines seems to be unnecessary. # # Penalty to blank parts # for b, l_input1 in enumerate(l_input): # if l_input1 < mL_input: # s_wv[b, :, l_input1:] = -10000000000 return s_wv def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only=True): # Encode bS, mL_input, input_size = wenc_s2s.shape # reshape wenc_s2s to incorperate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2*i_layer)*self.hidden_size h_ed = h_st + self.hidden_size c_st = (2*i_layer+1)*self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] # initial (current) pointer pnt_list_beam = [] cpnt_beam = [] cpnt_h_beam = [] for i_beam in range(beam_size): pnt_list_beam1 = [] for b in range(bS): pnt_list_beam1.append( [ [pnt_start_tok], 0] ) pnt_list_beam.append(pnt_list_beam1) # initisl cpnt # Now, initialize pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] # Distort ipnt by i_bam on purpose to avoid initial duplication of beam-search ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme cpnt_beam.append(ipnt) cpnt_h_beam.append( (h_0, c_0) ) t = 0 while t < self.Tmax: # s_wv1_beam = [] candidates = [ [] for b in range(bS) ] # [bS] # Generate beam for i_beam, cpnt in enumerate(cpnt_beam): cpnt_h = cpnt_h_beam[i_beam] pnt_list_beam1 = pnt_list_beam[i_beam] dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm cpnt_h_beam[i_beam] = cpnt_h # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Get the candidates only among the input space. prob, idxs = F.softmax(s_wv1.view(bS, -1), dim=1).topk(dim=1, k=max(l_input)) log_prob = torch.log(prob) # [bS, beam_size] for b, log_prob1 in enumerate(log_prob): pnt_list11, score = pnt_list_beam1[b] for i_can, log_prob11 in enumerate(log_prob1): # no update if last token was the end-token previous_pnt = pnt_list11[-1] if previous_pnt== pnt_end_tok: new_seq = pnt_list11 new_score = score else: new_seq = pnt_list11 + [idxs[b][i_can].item()] new_score = score + log_prob11.item() _candidate = [new_seq, new_score] candidates[b].append(_candidate) # Execution-guided beam filtering for b, candidates1 in enumerate(candidates): new_pnt_list_batch1 = sorted(candidates1, key=lambda list1: list1[-1], reverse=True) count = 0 selected_candidates1 = [] for new_pnt_list_batch11 in new_pnt_list_batch1: if new_pnt_list_batch11 not in selected_candidates1: if beam_only: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count +=1 else: # Need to be modified here. executable = False testable = False pr_i_vg_list, pr_i_vg_sub_list = gen_i_vg_from_pnt_idxs([new_pnt_list_batch11[0]], [i_sql_vocab[b]], [i_nlu[b]], [i_hds[b]]) pr_sql_q_s2s, pr_sql_i = gen_sql_q_from_i_vg([tokens[b]], [nlu[b]], [nlu_t[b]], [hds[b]], [tt_to_t_idx[b]], pnt_start_tok, pnt_end_tok, [new_pnt_list_batch11[0]], pr_i_vg_list, pr_i_vg_sub_list) # check testability from select-clause try: # check whether basic elements presents in pr_sql_i # If so, it is testable. idx_agg = pr_sql_i[0]["agg"] idx_sel = pr_sql_i[0]["sel"] testable = True except: testable = False pass # check the presence of conds if testable: try: conds = pr_sql_i[0]["conds"] except: conds = [] try: pr_ans1 = engine.execute(tb[b]['id'], idx_sel, idx_agg, conds) executable = bool(pr_ans1) except: executable = False # if testable: if executable: add_candidate = True else: add_candidate = False else: add_candidate = True if add_candidate: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count += 1 if count == beam_size: break if count < beam_size: # not executable at all.. # add junk sequence. for i_junk in range(count, beam_size): pnt_list_beam[i_junk][b] = [[pnt_end_tok],-9999999] # generate cpnt # formatting pnt_n as a one-hot input. for i_beam in range(beam_size): cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) idx_batch = [seq_score[0][-1] for seq_score in pnt_list_beam[i_beam]] pnt_n = torch.tensor(idx_batch).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B, t=1, mL_input] cpnt_beam[i_beam] = cpnt t += 1 # Generate best pr_pnt_list, p_tot pr_pnt_idxs = [] p_list = [] for b in range(bS): pnt_list_beam_best = pnt_list_beam[0] pr_pnt_idxs.append(pnt_list_beam_best[b][0]) p_list.append( pnt_list_beam_best[b][1]) return pr_pnt_idxs, p_list, pnt_list_beam # ============= Shallow-Layer =============== class FT_Scalar_1(nn.Module): """ Shallow-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, n_cond_ops, n_agg_ops, old=False): super(FT_Scalar_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 def scp(self, wemb_h, l_header): bS, max_header_len, _ = wemb_h.shape # s_sc s_sc = torch.zeros(bS, max_header_len).to(device) s_sc[:, :] = wemb_h[:, :, 0] # s_sc = [B, max_header length, 1] # s_sc[:,:] = F.tanh(wemb_h[:,:,0]) # s_sc = [B, max_header length, 1] # s_sc = s_sc.squeeze(2) # masking # print(f"s_sc {s_sc}") for b, l_header1 in enumerate(l_header): s_sc[b, l_header1:] = -9999999999.0 return s_sc def sap(self, wemb_h, pr_sc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape # select of aggregation operator s_sa = torch.zeros([bS, self.n_agg_ops]).to(device) for b, pr_sc1 in enumerate(pr_sc): s_sa[b,:] = wemb_h[b,pr_sc1,idx_st:idx_ed] return s_sa def wnp(self, cls_vec): bS = cls_vec.shape[0] # [B,hidden_size] -> [B, n_where_num+1] s_wn = torch.zeros(bS, (self.n_where_num + 1)).to(device) s_wn[:, :] = cls_vec[:, 0:(self.n_where_num + 1)] return s_wn def wcp(self, wemb_h, l_header, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wc = torch.zeros(bS, max_header_len, 1).to(device) s_wc[:, :, :] = wemb_h[:, :, idx_st:idx_ed] s_wc = s_wc.squeeze(2) # [B, max_header_length] # masking for b, l_header1 in enumerate(l_header): s_wc[b, l_header1:] = -99999999999.0 return s_wc def wop(self, wemb_h, pr_wc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wo = torch.zeros([bS, self.n_where_num, self.n_cond_ops]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: s_wo[b, 0:len(pr_wc1), :] = wemb_h[b, pr_wc1, idx_st:idx_ed] else: pass return s_wo def wvp(self, emb_question, len_question, pr_wc): bS, _, _ = emb_question.shape s_wv = torch.zeros([bS, self.n_where_num, max(len_question), 2]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: # start logit s_wv[b, 0:len(pr_wc1), :, 0] = emb_question[b, :, pr_wc1].transpose(0, 1) # end logit s_wv[b, 0:len(pr_wc1), :, 1] = emb_question[b, :, [pr_wc11 + 100 for pr_wc11 in pr_wc1]].transpose(0, 1) else: pass # masking # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < max(len_question): s_wv[b, :, l_n1:, :] = -1e+11 return s_wv def forward(self, emb_question, len_question, wemb_h, l_header, cls_vec, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # emb_question = [B, max_nlu_token_length, hidden_size] # here, # of target_layer is fixed to 1. # wemb_h = [B, max_header #, hidden_size] s_sc = self.scp(wemb_h, l_header) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # s_sa idx_st = 1 idx_ed = 1 + self.n_agg_ops s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) if g_sa: pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # where_number s_wn = self.wnp(cls_vec) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc idx_st = idx_ed+1 idx_ed = idx_st+1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo idx_st = idx_ed+1 idx_ed = idx_st + self.n_cond_ops s_wo = self.wop(wemb_h, pr_wc, idx_st, idx_ed) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv # s_wv = [bS, 4, mL, 2] s_wv = self.wvp(emb_question, len_question, pr_wc) # print(s_wv) # s_wv = F.tanh(s_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv def forward_EG(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, beam_size=4): """ Execution-guided beam decoding. Essentially identical with that of NL2SQL Layer. """ # Select-clause prob_sca, pr_sc_best, pr_sa_best, \ p_sc_best, p_sa_best, p_select \ = self.EG_decoding_select(wemb_h, l_header, tb, beam_size=beam_size) # Where-clause prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn_best, p_wc_best, p_wo_best, p_wvi_best \ = self.EG_decoding_where(emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4) p_tot = cal_prob_tot(p_select, p_where) return pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_wvi_best, \ pr_sql_i, p_tot, p_select, p_where, p_sc_best, p_sa_best, \ p_wn_best, p_wc_best, p_wo_best, p_wvi_best def EG_decoding_select(self, wemb_h, l_header, tb, beam_size=4, show_p_sc=False, show_p_sa=False): # sc s_sc = self.scp(wemb_h, l_header) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) score_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. idx_st = 1 idx_ed = 1 + self.n_agg_ops for i_beam in range(beam_size): pr_sc = list(array(pr_sc_beam)[:, i_beam]) s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa score_sc_sa[:, i_beam, :] = s_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:, i_beam, :] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS), beam_idx_sca, 0] pr_sa = idxs_arr[range(bS), beam_idx_sca, 1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # output for later analysis. p_sc_best = cal_prob_sc(s_sc, pr_sc_best) p_sa_best = cal_prob_sa(score_sc_sa[range(bS), beam_idx_sca, :].squeeze(1), pr_sa_best) p_select = cal_prob_select(p_sc_best, p_sa_best) # p_select = prob_sca[range(bS),beam_idx_sca,pr_sa_best].detach().to('cpu').numpy() return prob_sca, pr_sc_best, pr_sa_best, p_sc_best, p_sa_best, p_select def EG_decoding_where(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_wp_t, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): bS, max_header_len, _ = wemb_h.shape # Now, Where-clause beam search. idx_st = 1 idx_ed = 1 + self.n_agg_ops s_wn = self.wnp(cls_vec) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc idx_st = idx_ed + 1 idx_ed = idx_st + 1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) prob_wc = torch.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.n_where_num] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.n_where_num]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b, :] = prob_wc[b, pr_wc_max1] # get most probable n_where_num where-clouses # wo idx_st = idx_ed + 1 idx_ed = idx_st + self.n_cond_ops s_wo_max = self.wop(wemb_h, pr_wc_max, idx_st, idx_ed) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, n_where_num, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] prob_wvi_beam_st_op_list = [] prob_wvi_beam_ed_op_list = [] # To re-use code, repeat the calculation unnecessarily. for i_op in range(self.n_cond_ops - 1): pr_wo_temp = [[i_op] * self.n_where_num] * bS # wv s_wv = self.wvp(emb_question, len_question, pr_wc_max) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam, prob_wvi_beam_st, prob_wvi_beam_ed = pred_wvi_se_beam(self.n_where_num, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) prob_wvi_beam_st_op_list.append(prob_wvi_beam_st) prob_wvi_beam_ed_op_list.append(prob_wvi_beam_ed) # pr_wvi_beam = [B, n_where_num, k_logit**2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, n_where_num, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wc_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wo_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_st_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_ed_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.n_where_num): for i_op in range(self.n_cond_ops - 1): # do not use final one p_wc = prob_wc_max[b, i_wn] for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc * p_wo * p_wv prob_wc_dupl[b, i_wn, i_op, i_wv_beam] = p_wc prob_wo_dupl[b, i_wn, i_op, i_wv_beam] = p_wo p_wv_st = prob_wvi_beam_st_op_list[i_op][b, i_wn, i_wv_beam] p_wv_ed = prob_wvi_beam_ed_op_list[i_op][b, i_wn, i_wv_beam] prob_wvi_st_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_st prob_wvi_ed_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_ed # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.n_where_num: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1. Collect only executable one. It is descending order of the probability. pr_wvi_max = [] p_wc_max = [] p_wo_max = [] p_wvi_max = [] for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] pr_wvi1_max = [] p_wc1_max = [] p_wo1_max = [] p_wvi1_max = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # idx11[0] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [tt_to_t_idx[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2]] p_wc11_max = prob_wc_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wo11_max = prob_wo_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wvi11_max = [ prob_wvi_st_dupl[b, idxs11[0], idxs11[1], idxs11[2]], prob_wvi_ed_dupl[b, idxs11[0], idxs11[1], idxs11[2]] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc_best[b], pr_sa_best[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) pr_wvi1_max.append(wvi) p_wc1_max.append(p_wc11_max) p_wo1_max.append(p_wo11_max) p_wvi1_max.append(p_wvi11_max) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) pr_wvi_max.append(pr_wvi1_max) p_wc_max.append(p_wc1_max) p_wo_max.append(p_wo1_max) p_wvi_max.append(p_wvi1_max) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] # total where-clause probability pr_wn_based_on_prob = [] pr_wvi_best = [] p_wc = [] p_wo = [] p_wvi = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len(conds_max[b]) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn + 1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_wvi_best1 = pr_wvi_max[b][:pr_wn_based_on_prob[b]] pr_sql_i.append(pr_sql_i1) pr_wvi_best.append(pr_wvi_best1) p_wc.append( p_wc_max[b][:pr_wn_based_on_prob[b]] ) p_wo.append( p_wo_max[b][:pr_wn_based_on_prob[b]] ) p_wvi.append( p_wvi_max[b][:pr_wn_based_on_prob[b]] ) # s_wv = [B, n_where_num, max_nlu_tokens, 2] p_wn = cal_prob_wn(s_wn, pr_wn_based_on_prob) p_where = cal_prob_where(p_wn, p_wc, p_wo, p_wvi) return prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn, p_wc, p_wo, p_wvi def Loss_s2s(score, g_pnt_idxs): """ score = [B, T, max_seq_length] """ # WHERE string part loss = 0 for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): ed = len(g_pnt_idxs1) - 1 score_part = score[b, :ed] loss += F.cross_entropy(score_part, torch.tensor(g_pnt_idxs1[1:]).to(device)) # +1 shift. return loss

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161

0.555163

10,973

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0.039187

0.02187

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0.766522

0.732075

0.704671

0.683595

0.661115

0.641284

0

0.030277

0.333948

74,778

1,896

162

39.439873

0.728025

0.168378

0

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0

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0

1

0.035375

false

0.002588

0.006903

0

0.077653

0

null

0

1

0

1

0

null

0

3

d9f32d2b9e677d6893c7269bf23bcedaa4e7f68a

363

py

Python

chia/components/sample_transformers/__init__.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

null

chia/components/sample_transformers/__init__.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

2

2021-10-06T13:19:09.000Z

2021-10-20T17:32:36.000Z

chia/components/sample_transformers/__init__.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

null

from chia import components from chia.components.sample_transformers import identity from chia.components.sample_transformers.sample_transformer import SampleTransformer class SampleTransformerFactory(components.Factory): name_to_class_mapping = {"identity": identity.IdentitySampleTransformer} __all__ = ["SampleTransformer", "SampleTransformerFactory"]

33

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0.85124

34

363

8.794118

0.5

0.080268

0.120401

0.160535

0.240803

0

0.082645

363

10

85

36.3

0.897898

0

0.134986

0.066116

0

1

0

false

0

0.5

0

0.833333

0

null

0

1

0

null

0

1

0

1

0

3

d9f3cb72d610ec30e4ecf05d60ba2025dc849112

416

py

Python

3/3.6/add_guest.py

singi2016cn/python-scaffold

274e508d1919da67e599aa73be139800c043bce4

[ "MIT" ]

null

3/3.6/add_guest.py

singi2016cn/python-scaffold

274e508d1919da67e599aa73be139800c043bce4

[ "MIT" ]

null

3/3.6/add_guest.py

singi2016cn/python-scaffold

274e508d1919da67e599aa73be139800c043bce4

[ "MIT" ]

null

# 添加嘉宾 names = [] names.append('singi') names.append('lily') names.append('sam') print('I find a big dining-table,I can invite more friends.') names.insert(0, 'xiaoling') names.insert(2, 'fangsi') names.append('zhangqing') greets = ',would you like to have dinner with me ?' print(names[0]+greets) print(names[1]+greets) print(names[2]+greets) print(names[3]+greets) print(names[4]+greets) print(names[5]+greets)

20.8

61

0.711538

67

416

4.41791

0.537313

0.202703

0.27027

0

0.02139

0.100962

416

20

62

20.8

0.770053

0.009615

0

0.309002

0

1

0

false

0

0.466667

0

null

1

0

null

0

1

0

3

8a03248b6fead646cb68e7a6a935435de664969c

14,492

py

Python

anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py

space-scl/emacs.d

6285c38714023b72a023fe24cbcb5e4fcdcdb949

[ "Apache-2.0" ]

4

2019-07-26T11:32:22.000Z

2019-09-11T05:34:59.000Z

anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py

space-scl/emacs.d

6285c38714023b72a023fe24cbcb5e4fcdcdb949

[ "Apache-2.0" ]

10

2020-05-11T20:29:28.000Z

2022-01-13T01:41:27.000Z

anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py

space-scl/emacs.d

6285c38714023b72a023fe24cbcb5e4fcdcdb949

[ "Apache-2.0" ]

2

2019-08-28T14:57:54.000Z

2019-11-26T16:18:30.000Z

""" Contexts are the "values" that Python would return. However Contexts are at the same time also the "contexts" that a user is currently sitting in. A ContextSet is typically used to specify the return of a function or any other static analysis operation. In jedi there are always multiple returns and not just one. """ from functools import reduce from operator import add from parso.python.tree import ExprStmt, SyncCompFor from jedi import debug from jedi._compatibility import zip_longest, unicode from jedi.parser_utils import clean_scope_docstring from jedi.common import BaseContextSet, BaseContext from jedi.evaluate.helpers import SimpleGetItemNotFound from jedi.evaluate.utils import safe_property from jedi.evaluate.cache import evaluator_as_method_param_cache from jedi.cache import memoize_method _sentinel = object() class HelperContextMixin(object): def get_root_context(self): context = self while True: if context.parent_context is None: return context context = context.parent_context @classmethod @evaluator_as_method_param_cache() def create_cached(cls, *args, **kwargs): return cls(*args, **kwargs) def execute(self, arguments): return self.evaluator.execute(self, arguments=arguments) def execute_evaluated(self, *value_list): from jedi.evaluate.arguments import ValuesArguments arguments = ValuesArguments([ContextSet([value]) for value in value_list]) return self.evaluator.execute(self, arguments) def execute_annotation(self): return self.execute_evaluated() def gather_annotation_classes(self): return ContextSet([self]) def merge_types_of_iterate(self, contextualized_node=None, is_async=False): return ContextSet.from_sets( lazy_context.infer() for lazy_context in self.iterate(contextualized_node, is_async) ) def py__getattribute__(self, name_or_str, name_context=None, position=None, search_global=False, is_goto=False, analysis_errors=True): """ :param position: Position of the last statement -> tuple of line, column """ if name_context is None: name_context = self from jedi.evaluate import finder f = finder.NameFinder(self.evaluator, self, name_context, name_or_str, position, analysis_errors=analysis_errors) filters = f.get_filters(search_global) if is_goto: return f.filter_name(filters) return f.find(filters, attribute_lookup=not search_global) def py__await__(self): await_context_set = self.py__getattribute__(u"__await__") if not await_context_set: debug.warning('Tried to run __await__ on context %s', self) return await_context_set.execute_evaluated() def eval_node(self, node): return self.evaluator.eval_element(self, node) def create_context(self, node, node_is_context=False, node_is_object=False): return self.evaluator.create_context(self, node, node_is_context, node_is_object) def iterate(self, contextualized_node=None, is_async=False): debug.dbg('iterate %s', self) if is_async: from jedi.evaluate.lazy_context import LazyKnownContexts # TODO if no __aiter__ contexts are there, error should be: # TypeError: 'async for' requires an object with __aiter__ method, got int return iter([ LazyKnownContexts( self.py__getattribute__('__aiter__').execute_evaluated() .py__getattribute__('__anext__').execute_evaluated() .py__getattribute__('__await__').execute_evaluated() .py__stop_iteration_returns() ) # noqa ]) return self.py__iter__(contextualized_node) def is_sub_class_of(self, class_context): for cls in self.py__mro__(): if cls.is_same_class(class_context): return True return False def is_same_class(self, class2): # Class matching should prefer comparisons that are not this function. if type(class2).is_same_class != HelperContextMixin.is_same_class: return class2.is_same_class(self) return self == class2 class Context(HelperContextMixin, BaseContext): """ Should be defined, otherwise the API returns empty types. """ predefined_names = {} """ To be defined by subclasses. """ tree_node = None @property def api_type(self): # By default just lower name of the class. Can and should be # overwritten. return self.__class__.__name__.lower() def py__getitem__(self, index_context_set, contextualized_node): from jedi.evaluate import analysis # TODO this context is probably not right. analysis.add( contextualized_node.context, 'type-error-not-subscriptable', contextualized_node.node, message="TypeError: '%s' object is not subscriptable" % self ) return NO_CONTEXTS def py__iter__(self, contextualized_node=None): if contextualized_node is not None: from jedi.evaluate import analysis analysis.add( contextualized_node.context, 'type-error-not-iterable', contextualized_node.node, message="TypeError: '%s' object is not iterable" % self) return iter([]) def get_signatures(self): return [] def is_class(self): return False def is_instance(self): return False def is_function(self): return False def is_module(self): return False def is_namespace(self): return False def is_compiled(self): return False def is_bound_method(self): return False def py__bool__(self): """ Since Wrapper is a super class for classes, functions and modules, the return value will always be true. """ return True def py__doc__(self): try: self.tree_node.get_doc_node except AttributeError: return '' else: return clean_scope_docstring(self.tree_node) return None def get_safe_value(self, default=_sentinel): if default is _sentinel: raise ValueError("There exists no safe value for context %s" % self) return default def py__call__(self, arguments): debug.warning("no execution possible %s", self) return NO_CONTEXTS def py__stop_iteration_returns(self): debug.warning("Not possible to return the stop iterations of %s", self) return NO_CONTEXTS def get_qualified_names(self): # Returns Optional[Tuple[str, ...]] return None def is_stub(self): # The root context knows if it's a stub or not. return self.parent_context.is_stub() def iterate_contexts(contexts, contextualized_node=None, is_async=False): """ Calls `iterate`, on all contexts but ignores the ordering and just returns all contexts that the iterate functions yield. """ return ContextSet.from_sets( lazy_context.infer() for lazy_context in contexts.iterate(contextualized_node, is_async=is_async) ) class _ContextWrapperBase(HelperContextMixin): predefined_names = {} @safe_property def name(self): from jedi.evaluate.names import ContextName wrapped_name = self._wrapped_context.name if wrapped_name.tree_name is not None: return ContextName(self, wrapped_name.tree_name) else: from jedi.evaluate.compiled import CompiledContextName return CompiledContextName(self, wrapped_name.string_name) @classmethod @evaluator_as_method_param_cache() def create_cached(cls, evaluator, *args, **kwargs): return cls(*args, **kwargs) def __getattr__(self, name): assert name != '_wrapped_context', 'Problem with _get_wrapped_context' return getattr(self._wrapped_context, name) class LazyContextWrapper(_ContextWrapperBase): @safe_property @memoize_method def _wrapped_context(self): with debug.increase_indent_cm('Resolve lazy context wrapper'): return self._get_wrapped_context() def __repr__(self): return '<%s>' % (self.__class__.__name__) def _get_wrapped_context(self): raise NotImplementedError class ContextWrapper(_ContextWrapperBase): def __init__(self, wrapped_context): self._wrapped_context = wrapped_context def __repr__(self): return '%s(%s)' % (self.__class__.__name__, self._wrapped_context) class TreeContext(Context): def __init__(self, evaluator, parent_context, tree_node): super(TreeContext, self).__init__(evaluator, parent_context) self.predefined_names = {} self.tree_node = tree_node def __repr__(self): return '<%s: %s>' % (self.__class__.__name__, self.tree_node) class ContextualizedNode(object): def __init__(self, context, node): self.context = context self.node = node def get_root_context(self): return self.context.get_root_context() def infer(self): return self.context.eval_node(self.node) def __repr__(self): return '<%s: %s in %s>' % (self.__class__.__name__, self.node, self.context) class ContextualizedName(ContextualizedNode): # TODO merge with TreeNameDefinition?! @property def name(self): return self.node def assignment_indexes(self): """ Returns an array of tuple(int, node) of the indexes that are used in tuple assignments. For example if the name is ``y`` in the following code:: x, (y, z) = 2, '' would result in ``[(1, xyz_node), (0, yz_node)]``. When searching for b in the case ``a, *b, c = [...]`` it will return:: [(slice(1, -1), abc_node)] """ indexes = [] is_star_expr = False node = self.node.parent compare = self.node while node is not None: if node.type in ('testlist', 'testlist_comp', 'testlist_star_expr', 'exprlist'): for i, child in enumerate(node.children): if child == compare: index = int(i / 2) if is_star_expr: from_end = int((len(node.children) - i) / 2) index = slice(index, -from_end) indexes.insert(0, (index, node)) break else: raise LookupError("Couldn't find the assignment.") is_star_expr = False elif node.type == 'star_expr': is_star_expr = True elif isinstance(node, (ExprStmt, SyncCompFor)): break compare = node node = node.parent return indexes def _getitem(context, index_contexts, contextualized_node): from jedi.evaluate.context.iterable import Slice # The actual getitem call. simple_getitem = getattr(context, 'py__simple_getitem__', None) result = NO_CONTEXTS unused_contexts = set() for index_context in index_contexts: if simple_getitem is not None: index = index_context if isinstance(index_context, Slice): index = index.obj try: method = index.get_safe_value except AttributeError: pass else: index = method(default=None) if type(index) in (float, int, str, unicode, slice, bytes): try: result |= simple_getitem(index) continue except SimpleGetItemNotFound: pass unused_contexts.add(index_context) # The index was somehow not good enough or simply a wrong type. # Therefore we now iterate through all the contexts and just take # all results. if unused_contexts or not index_contexts: result |= context.py__getitem__( ContextSet(unused_contexts), contextualized_node ) debug.dbg('py__getitem__ result: %s', result) return result class ContextSet(BaseContextSet): def py__class__(self): return ContextSet(c.py__class__() for c in self._set) def iterate(self, contextualized_node=None, is_async=False): from jedi.evaluate.lazy_context import get_merged_lazy_context type_iters = [c.iterate(contextualized_node, is_async=is_async) for c in self._set] for lazy_contexts in zip_longest(*type_iters): yield get_merged_lazy_context( [l for l in lazy_contexts if l is not None] ) def execute(self, arguments): return ContextSet.from_sets(c.evaluator.execute(c, arguments) for c in self._set) def execute_evaluated(self, *args, **kwargs): return ContextSet.from_sets(c.execute_evaluated(*args, **kwargs) for c in self._set) def py__getattribute__(self, *args, **kwargs): if kwargs.get('is_goto'): return reduce(add, [c.py__getattribute__(*args, **kwargs) for c in self._set], []) return ContextSet.from_sets(c.py__getattribute__(*args, **kwargs) for c in self._set) def get_item(self, *args, **kwargs): return ContextSet.from_sets(_getitem(c, *args, **kwargs) for c in self._set) def try_merge(self, function_name): context_set = self.__class__([]) for c in self._set: try: method = getattr(c, function_name) except AttributeError: pass else: context_set |= method() return context_set def gather_annotation_classes(self): return ContextSet.from_sets([c.gather_annotation_classes() for c in self._set]) def get_signatures(self): return [sig for c in self._set for sig in c.get_signatures()] NO_CONTEXTS = ContextSet([]) def iterator_to_context_set(func): def wrapper(*args, **kwargs): return ContextSet(func(*args, **kwargs)) return wrapper

33.162471

94

0.637524

1,718

14,492

5.089639

0.185681

0.030878

0.021958

0.011436

0.237077

0.180009

0.141468

0.087374

0.064959

0.042543

0

0.001151

0.280638

14,492

436

95

33.238532

0.837602

0.110199

0

0.248322

0

0.045142

0.004025

0

0.006881

0.003356

1

0.204698

false

0.010067

0.067114

0.110738

0.526846

0

null

0

null

0

1

0

1

0

3

8a13575cd76b03c2660c0f973dca2598509c1205

34,179

py

Python

sdk/lusid/models/lusid_instrument.py

rizwansaeed/lusid-sdk-python-preview

52d092d6d4099b8526f0318f3fe1ddc0b943da6a

[ "MIT" ]

null

sdk/lusid/models/lusid_instrument.py

rizwansaeed/lusid-sdk-python-preview

52d092d6d4099b8526f0318f3fe1ddc0b943da6a

[ "MIT" ]

null

sdk/lusid/models/lusid_instrument.py

rizwansaeed/lusid-sdk-python-preview

52d092d6d4099b8526f0318f3fe1ddc0b943da6a

[ "MIT" ]

null

# coding: utf-8 """ LUSID API # Introduction This page documents the [LUSID APIs](https://www.lusid.com/api/swagger), which allows authorised clients to query and update their data within the LUSID platform. SDKs to interact with the LUSID APIs are available in the following languages : * [C#](https://github.com/finbourne/lusid-sdk-csharp) * [Java](https://github.com/finbourne/lusid-sdk-java) * [JavaScript](https://github.com/finbourne/lusid-sdk-js) * [Python](https://github.com/finbourne/lusid-sdk-python) # Data Model The LUSID API has a relatively lightweight but extremely powerful data model. One of the goals of LUSID was not to enforce on clients a single rigid data model but rather to provide a flexible foundation onto which clients can map their own data models. The core entities in LUSID provide a minimal structure and set of relationships, and the data model can be extended using Properties. The LUSID data model is exposed through the LUSID APIs. The APIs provide access to both business objects and the meta data used to configure the systems behaviours. The key business entities are: - * **Portfolios** A portfolio is a container for transactions and holdings (a **Transaction Portfolio**) or constituents (a **Reference Portfolio**). * **Derived Portfolios**. Derived Portfolios allow Portfolios to be created based on other Portfolios, by overriding or adding specific items. * **Holdings** A Holding is a quantity of an Instrument or a balance of cash within a Portfolio. Holdings can only be adjusted via Transactions. * **Transactions** A Transaction is an economic event that occurs in a Portfolio, causing its holdings to change. * **Corporate Actions** A corporate action is a market event which occurs to an Instrument and thus applies to all portfolios which holding the instrument. Examples are stock splits or mergers. * **Constituents** A constituent is a record in a Reference Portfolio containing an Instrument and an associated weight. * **Instruments** An instrument represents a currency, tradable instrument or OTC contract that is attached to a transaction and a holding. * **Properties** All major entities allow additional user defined properties to be associated with them. For example, a Portfolio manager may be associated with a portfolio. Meta data includes: - * **Transaction Types** Transactions are booked with a specific transaction type. The types are client defined and are used to map the Transaction to a series of movements which update the portfolio holdings. * **Properties Types** Types of user defined properties used within the system. ## Scope All data in LUSID is segregated at the client level. Entities in LUSID are identifiable by a unique code. Every entity lives within a logical data partition known as a Scope. Scope is an identity namespace allowing two entities with the same unique code to co-exist within individual address spaces. For example, prices for equities from different vendors may be uploaded into different scopes such as `client/vendor1` and `client/vendor2`. A portfolio may then be valued using either of the price sources by referencing the appropriate scope. LUSID Clients cannot access scopes of other clients. ## Instruments LUSID has its own built-in instrument master which you can use to master your own instrument universe. Every instrument must be created with one or more unique market identifiers, such as [FIGI](https://openfigi.com/). For any non-listed instruments (eg OTCs), you can upload an instrument against a custom ID of your choosing. In addition, LUSID will allocate each instrument a unique 'LUSID instrument identifier'. The LUSID instrument identifier is what is used when uploading transactions, holdings, prices, etc. The API exposes an `instrument/lookup` endpoint which can be used to lookup these LUSID identifiers using their market identifiers. Cash can be referenced using the ISO currency code prefixed with \"`CCY_`\" e.g. `CCY_GBP` ## Instrument Data Instrument data can be uploaded to the system using the [Instrument Properties](#tag/InstrumentProperties) endpoint. | Field|Type|Description | | ---|---|--- | | Key|propertykey|The key of the property. This takes the format {domain}/{scope}/{code} e.g. 'Instrument/system/Name' or 'Transaction/strategy/quantsignal'. | | Value|string|The value of the property. | | EffectiveFrom|datetimeoffset|The effective datetime from which the property is valid. | | EffectiveUntil|datetimeoffset|The effective datetime until which the property is valid. If not supplied this will be valid indefinitely, potentially overwriting values with EffectiveFrom's in the future. | ## Transaction Portfolios Portfolios are the top-level entity containers within LUSID, containing transactions, corporate actions and holdings. The transactions build up the portfolio holdings on which valuations, analytics profit & loss and risk can be calculated. Properties can be associated with Portfolios to add in additional data. Portfolio properties can be changed over time, for example to allow a Portfolio Manager to be linked with a Portfolio. Additionally, portfolios can be securitised and held by other portfolios, allowing LUSID to perform \"drill-through\" into underlying fund holdings ### Derived Portfolios LUSID also allows for a portfolio to be composed of another portfolio via derived portfolios. A derived portfolio can contain its own transactions and also inherits any transactions from its parent portfolio. Any changes made to the parent portfolio are automatically reflected in derived portfolio. Derived portfolios in conjunction with scopes are a powerful construct. For example, to do pre-trade what-if analysis, a derived portfolio could be created a new namespace linked to the underlying live (parent) portfolio. Analysis can then be undertaken on the derived portfolio without affecting the live portfolio. ### Transactions A transaction represents an economic activity against a Portfolio. Transactions are processed according to a configuration. This will tell the LUSID engine how to interpret the transaction and correctly update the holdings. LUSID comes with a set of transaction types you can use out of the box, or you can configure your own set(s) of transactions. For more details see the [LUSID Getting Started Guide for transaction configuration.](https://support.lusid.com/configuring-transaction-types) | Field|Type|Description | | ---|---|--- | | TransactionId|string|The unique identifier for the transaction. | | Type|string|The type of the transaction e.g. 'Buy', 'Sell'. The transaction type should have been pre-configured via the System Configuration API endpoint. If it hasn't been pre-configured the transaction will still be updated or inserted however you will be unable to generate the resultant holdings for the portfolio that contains this transaction as LUSID does not know how to process it. | | InstrumentIdentifiers|map|A set of instrument identifiers to use to resolve the transaction to a unique instrument. | | TransactionDate|dateorcutlabel|The date of the transaction. | | SettlementDate|dateorcutlabel|The settlement date of the transaction. | | Units|decimal|The number of units transacted in the associated instrument. | | TransactionPrice|transactionprice|The price for each unit of the transacted instrument in the transaction currency. | | TotalConsideration|currencyandamount|The total value of the transaction in the settlement currency. | | ExchangeRate|decimal|The exchange rate between the transaction and settlement currency. For example if the transaction currency is in USD and the settlement currency is in GBP this this the USD/GBP rate. | | TransactionCurrency|currency|The transaction currency. | | Properties|map|Set of unique transaction properties and associated values to store with the transaction. Each property must be from the 'Transaction' domain. | | CounterpartyId|string|The identifier for the counterparty of the transaction. | | Source|string|The source of the transaction. This is used to look up the appropriate transaction group set in the transaction type configuration. | From these fields, the following values can be calculated * **Transaction value in Transaction currency**: TotalConsideration / ExchangeRate * **Transaction value in Portfolio currency**: Transaction value in Transaction currency * TradeToPortfolioRate #### Example Transactions ##### A Common Purchase Example Three example transactions are shown in the table below. They represent a purchase of USD denominated IBM shares within a Sterling denominated portfolio. * The first two transactions are for separate buy and fx trades * Buying 500 IBM shares for $71,480.00 * A spot foreign exchange conversion to fund the IBM purchase. (Buy $71,480.00 for £54,846.60) * The third transaction is an alternate version of the above trades. Buying 500 IBM shares and settling directly in Sterling. | Column | Buy Trade | Fx Trade | Buy Trade with foreign Settlement | | ----- | ----- | ----- | ----- | | TransactionId | FBN00001 | FBN00002 | FBN00003 | | Type | Buy | FxBuy | Buy | | InstrumentIdentifiers | { \"figi\", \"BBG000BLNNH6\" } | { \"CCY\", \"CCY_USD\" } | { \"figi\", \"BBG000BLNNH6\" } | | TransactionDate | 2018-08-02 | 2018-08-02 | 2018-08-02 | | SettlementDate | 2018-08-06 | 2018-08-06 | 2018-08-06 | | Units | 500 | 71480 | 500 | | TransactionPrice | 142.96 | 1 | 142.96 | | TradeCurrency | USD | USD | USD | | ExchangeRate | 1 | 0.7673 | 0.7673 | | TotalConsideration.Amount | 71480.00 | 54846.60 | 54846.60 | | TotalConsideration.Currency | USD | GBP | GBP | | Trade/default/TradeToPortfolioRate&ast; | 0.7673 | 0.7673 | 0.7673 | [&ast; This is a property field] ##### A Forward FX Example LUSID has a flexible transaction modelling system, meaning there are a number of different ways of modelling forward fx trades. The default LUSID transaction types are FwdFxBuy and FwdFxSell. Using these transaction types, LUSID will generate two holdings for each Forward FX trade, one for each currency in the trade. An example Forward Fx trade to sell GBP for USD in a JPY-denominated portfolio is shown below: | Column | Forward 'Sell' Trade | Notes | | ----- | ----- | ---- | | TransactionId | FBN00004 | | | Type | FwdFxSell | | | InstrumentIdentifiers | { \"Instrument/default/Currency\", \"GBP\" } | | | TransactionDate | 2018-08-02 | | | SettlementDate | 2019-02-06 | Six month forward | | Units | 10000.00 | Units of GBP | | TransactionPrice | 1 | | | TradeCurrency | GBP | Currency being sold | | ExchangeRate | 1.3142 | Agreed rate between GBP and USD | | TotalConsideration.Amount | 13142.00 | Amount in the settlement currency, USD | | TotalConsideration.Currency | USD | Settlement currency | | Trade/default/TradeToPortfolioRate | 142.88 | Rate between trade currency, GBP and portfolio base currency, JPY | Please note that exactly the same economic behaviour could be modelled using the FwdFxBuy Transaction Type with the amounts and rates reversed. ### Holdings A holding represents a position in an instrument or cash on a given date. | Field|Type|Description | | ---|---|--- | | InstrumentUid|string|The unqiue Lusid Instrument Id (LUID) of the instrument that the holding is in. | | SubHoldingKeys|map|The sub-holding properties which identify the holding. Each property will be from the 'Transaction' domain. These are configured when a transaction portfolio is created. | | Properties|map|The properties which have been requested to be decorated onto the holding. These will be from the 'Instrument' or 'Holding' domain. | | HoldingType|string|The type of the holding e.g. Position, Balance, CashCommitment, Receivable, ForwardFX etc. | | Units|decimal|The total number of units of the holding. | | SettledUnits|decimal|The total number of settled units of the holding. | | Cost|currencyandamount|The total cost of the holding in the transaction currency. | | CostPortfolioCcy|currencyandamount|The total cost of the holding in the portfolio currency. | | Transaction|transaction|The transaction associated with an unsettled holding. | ## Corporate Actions Corporate actions are represented within LUSID in terms of a set of instrument-specific 'transitions'. These transitions are used to specify the participants of the corporate action, and the effect that the corporate action will have on holdings in those participants. ### Corporate Action | Field|Type|Description | | ---|---|--- | | CorporateActionCode|code|The unique identifier of this corporate action | | Description|string| | | AnnouncementDate|datetimeoffset|The announcement date of the corporate action | | ExDate|datetimeoffset|The ex date of the corporate action | | RecordDate|datetimeoffset|The record date of the corporate action | | PaymentDate|datetimeoffset|The payment date of the corporate action | | Transitions|corporateactiontransition[]|The transitions that result from this corporate action | ### Transition | Field|Type|Description | | ---|---|--- | | InputTransition|corporateactiontransitioncomponent|Indicating the basis of the corporate action - which security and how many units | | OutputTransitions|corporateactiontransitioncomponent[]|What will be generated relative to the input transition | ### Example Corporate Action Transitions #### A Dividend Action Transition In this example, for each share of IBM, 0.20 units (or 20 pence) of GBP are generated. | Column | Input Transition | Output Transition | | ----- | ----- | ----- | | Instrument Identifiers | { \"figi\" : \"BBG000BLNNH6\" } | { \"ccy\" : \"CCY_GBP\" } | | Units Factor | 1 | 0.20 | | Cost Factor | 1 | 0 | #### A Split Action Transition In this example, for each share of IBM, we end up with 2 units (2 shares) of IBM, with total value unchanged. | Column | Input Transition | Output Transition | | ----- | ----- | ----- | | Instrument Identifiers | { \"figi\" : \"BBG000BLNNH6\" } | { \"figi\" : \"BBG000BLNNH6\" } | | Units Factor | 1 | 2 | | Cost Factor | 1 | 1 | #### A Spinoff Action Transition In this example, for each share of IBM, we end up with 1 unit (1 share) of IBM and 3 units (3 shares) of Celestica, with 85% of the value remaining on the IBM share, and 5% in each Celestica share (15% total). | Column | Input Transition | Output Transition 1 | Output Transition 2 | | ----- | ----- | ----- | ----- | | Instrument Identifiers | { \"figi\" : \"BBG000BLNNH6\" } | { \"figi\" : \"BBG000BLNNH6\" } | { \"figi\" : \"BBG000HBGRF3\" } | | Units Factor | 1 | 1 | 3 | | Cost Factor | 1 | 0.85 | 0.15 | ## Reference Portfolios Reference portfolios are portfolios that contain constituents with weights. They are designed to represent entities such as indices and benchmarks. ### Constituents | Field|Type|Description | | ---|---|--- | | InstrumentIdentifiers|map|Unique instrument identifiers | | InstrumentUid|string|LUSID's internal unique instrument identifier, resolved from the instrument identifiers | | Currency|decimal| | | Weight|decimal| | | FloatingWeight|decimal| | ## Portfolio Groups Portfolio groups allow the construction of a hierarchy from portfolios and groups. Portfolio operations on the group are executed on an aggregated set of portfolios in the hierarchy. For example: * Global Portfolios _(group)_ * APAC _(group)_ * Hong Kong _(portfolio)_ * Japan _(portfolio)_ * Europe _(group)_ * France _(portfolio)_ * Germany _(portfolio)_ * UK _(portfolio)_ In this example **Global Portfolios** is a group that consists of an aggregate of **Hong Kong**, **Japan**, **France**, **Germany** and **UK** portfolios. ## Properties Properties are key-value pairs that can be applied to any entity within a domain (where a domain is `trade`, `portfolio`, `security` etc). Properties must be defined before use with a `PropertyDefinition` and can then subsequently be added to entities. ## Schema A detailed description of the entities used by the API and parameters for endpoints which take a JSON document can be retrieved via the `schema` endpoint. ## Meta data The following headers are returned on all responses from LUSID | Name | Purpose | | --- | --- | | lusid-meta-duration | Duration of the request | | lusid-meta-success | Whether or not LUSID considered the request to be successful | | lusid-meta-requestId | The unique identifier for the request | | lusid-schema-url | Url of the schema for the data being returned | | lusid-property-schema-url | Url of the schema for any properties | # Error Codes | Code|Name|Description | | ---|---|--- | | <a name=\"-10\">-10</a>|Server Configuration Error| | | <a name=\"-1\">-1</a>|Unknown error|An unexpected error was encountered on our side. | | <a name=\"102\">102</a>|Version Not Found| | | <a name=\"103\">103</a>|Api Rate Limit Violation| | | <a name=\"104\">104</a>|Instrument Not Found| | | <a name=\"105\">105</a>|Property Not Found| | | <a name=\"106\">106</a>|Portfolio Recursion Depth| | | <a name=\"108\">108</a>|Group Not Found| | | <a name=\"109\">109</a>|Portfolio Not Found| | | <a name=\"110\">110</a>|Property Schema Not Found| | | <a name=\"111\">111</a>|Portfolio Ancestry Not Found| | | <a name=\"112\">112</a>|Portfolio With Id Already Exists| | | <a name=\"113\">113</a>|Orphaned Portfolio| | | <a name=\"119\">119</a>|Missing Base Claims| | | <a name=\"121\">121</a>|Property Not Defined| | | <a name=\"122\">122</a>|Cannot Delete System Property| | | <a name=\"123\">123</a>|Cannot Modify Immutable Property Field| | | <a name=\"124\">124</a>|Property Already Exists| | | <a name=\"125\">125</a>|Invalid Property Life Time| | | <a name=\"126\">126</a>|Property Constraint Style Excludes Properties| | | <a name=\"127\">127</a>|Cannot Modify Default Data Type| | | <a name=\"128\">128</a>|Group Already Exists| | | <a name=\"129\">129</a>|No Such Data Type| | | <a name=\"130\">130</a>|Undefined Value For Data Type| | | <a name=\"131\">131</a>|Unsupported Value Type Defined On Data Type| | | <a name=\"132\">132</a>|Validation Error| | | <a name=\"133\">133</a>|Loop Detected In Group Hierarchy| | | <a name=\"134\">134</a>|Undefined Acceptable Values| | | <a name=\"135\">135</a>|Sub Group Already Exists| | | <a name=\"138\">138</a>|Price Source Not Found| | | <a name=\"139\">139</a>|Analytic Store Not Found| | | <a name=\"141\">141</a>|Analytic Store Already Exists| | | <a name=\"143\">143</a>|Client Instrument Already Exists| | | <a name=\"144\">144</a>|Duplicate In Parameter Set| | | <a name=\"147\">147</a>|Results Not Found| | | <a name=\"148\">148</a>|Order Field Not In Result Set| | | <a name=\"149\">149</a>|Operation Failed| | | <a name=\"150\">150</a>|Elastic Search Error| | | <a name=\"151\">151</a>|Invalid Parameter Value| | | <a name=\"153\">153</a>|Command Processing Failure| | | <a name=\"154\">154</a>|Entity State Construction Failure| | | <a name=\"155\">155</a>|Entity Timeline Does Not Exist| | | <a name=\"156\">156</a>|Concurrency Conflict Failure| | | <a name=\"157\">157</a>|Invalid Request| | | <a name=\"158\">158</a>|Event Publish Unknown| | | <a name=\"159\">159</a>|Event Query Failure| | | <a name=\"160\">160</a>|Blob Did Not Exist| | | <a name=\"162\">162</a>|Sub System Request Failure| | | <a name=\"163\">163</a>|Sub System Configuration Failure| | | <a name=\"165\">165</a>|Failed To Delete| | | <a name=\"166\">166</a>|Upsert Client Instrument Failure| | | <a name=\"167\">167</a>|Illegal As At Interval| | | <a name=\"168\">168</a>|Illegal Bitemporal Query| | | <a name=\"169\">169</a>|Invalid Alternate Id| | | <a name=\"170\">170</a>|Cannot Add Source Portfolio Property Explicitly| | | <a name=\"171\">171</a>|Entity Already Exists In Group| | | <a name=\"173\">173</a>|Entity With Id Already Exists| | | <a name=\"174\">174</a>|Derived Portfolio Details Do Not Exist| | | <a name=\"176\">176</a>|Portfolio With Name Already Exists| | | <a name=\"177\">177</a>|Invalid Transactions| | | <a name=\"178\">178</a>|Reference Portfolio Not Found| | | <a name=\"179\">179</a>|Duplicate Id| | | <a name=\"180\">180</a>|Command Retrieval Failure| | | <a name=\"181\">181</a>|Data Filter Application Failure| | | <a name=\"182\">182</a>|Search Failed| | | <a name=\"183\">183</a>|Movements Engine Configuration Key Failure| | | <a name=\"184\">184</a>|Fx Rate Source Not Found| | | <a name=\"185\">185</a>|Accrual Source Not Found| | | <a name=\"186\">186</a>|Access Denied| | | <a name=\"187\">187</a>|Invalid Identity Token| | | <a name=\"188\">188</a>|Invalid Request Headers| | | <a name=\"189\">189</a>|Price Not Found| | | <a name=\"190\">190</a>|Invalid Sub Holding Keys Provided| | | <a name=\"191\">191</a>|Duplicate Sub Holding Keys Provided| | | <a name=\"192\">192</a>|Cut Definition Not Found| | | <a name=\"193\">193</a>|Cut Definition Invalid| | | <a name=\"194\">194</a>|Time Variant Property Deletion Date Unspecified| | | <a name=\"195\">195</a>|Perpetual Property Deletion Date Specified| | | <a name=\"196\">196</a>|Time Variant Property Upsert Date Unspecified| | | <a name=\"197\">197</a>|Perpetual Property Upsert Date Specified| | | <a name=\"200\">200</a>|Invalid Unit For Data Type| | | <a name=\"201\">201</a>|Invalid Type For Data Type| | | <a name=\"202\">202</a>|Invalid Value For Data Type| | | <a name=\"203\">203</a>|Unit Not Defined For Data Type| | | <a name=\"204\">204</a>|Units Not Supported On Data Type| | | <a name=\"205\">205</a>|Cannot Specify Units On Data Type| | | <a name=\"206\">206</a>|Unit Schema Inconsistent With Data Type| | | <a name=\"207\">207</a>|Unit Definition Not Specified| | | <a name=\"208\">208</a>|Duplicate Unit Definitions Specified| | | <a name=\"209\">209</a>|Invalid Units Definition| | | <a name=\"210\">210</a>|Invalid Instrument Identifier Unit| | | <a name=\"211\">211</a>|Holdings Adjustment Does Not Exist| | | <a name=\"212\">212</a>|Could Not Build Excel Url| | | <a name=\"213\">213</a>|Could Not Get Excel Version| | | <a name=\"214\">214</a>|Instrument By Code Not Found| | | <a name=\"215\">215</a>|Entity Schema Does Not Exist| | | <a name=\"216\">216</a>|Feature Not Supported On Portfolio Type| | | <a name=\"217\">217</a>|Quote Not Found| | | <a name=\"218\">218</a>|Invalid Quote Identifier| | | <a name=\"219\">219</a>|Invalid Metric For Data Type| | | <a name=\"220\">220</a>|Invalid Instrument Definition| | | <a name=\"221\">221</a>|Instrument Upsert Failure| | | <a name=\"222\">222</a>|Reference Portfolio Request Not Supported| | | <a name=\"223\">223</a>|Transaction Portfolio Request Not Supported| | | <a name=\"224\">224</a>|Invalid Property Value Assignment| | | <a name=\"230\">230</a>|Transaction Type Not Found| | | <a name=\"231\">231</a>|Transaction Type Duplication| | | <a name=\"232\">232</a>|Portfolio Does Not Exist At Given Date| | | <a name=\"233\">233</a>|Query Parser Failure| | | <a name=\"234\">234</a>|Duplicate Constituent| | | <a name=\"235\">235</a>|Unresolved Instrument Constituent| | | <a name=\"236\">236</a>|Unresolved Instrument In Transition| | | <a name=\"237\">237</a>|Missing Side Definitions| | | <a name=\"299\">299</a>|Invalid Recipe| | | <a name=\"300\">300</a>|Missing Recipe| | | <a name=\"301\">301</a>|Dependencies| | | <a name=\"304\">304</a>|Portfolio Preprocess Failure| | | <a name=\"310\">310</a>|Valuation Engine Failure| | | <a name=\"311\">311</a>|Task Factory Failure| | | <a name=\"312\">312</a>|Task Evaluation Failure| | | <a name=\"313\">313</a>|Task Generation Failure| | | <a name=\"314\">314</a>|Engine Configuration Failure| | | <a name=\"315\">315</a>|Model Specification Failure| | | <a name=\"320\">320</a>|Market Data Key Failure| | | <a name=\"321\">321</a>|Market Resolver Failure| | | <a name=\"322\">322</a>|Market Data Failure| | | <a name=\"330\">330</a>|Curve Failure| | | <a name=\"331\">331</a>|Volatility Surface Failure| | | <a name=\"332\">332</a>|Volatility Cube Failure| | | <a name=\"350\">350</a>|Instrument Failure| | | <a name=\"351\">351</a>|Cash Flows Failure| | | <a name=\"352\">352</a>|Reference Data Failure| | | <a name=\"360\">360</a>|Aggregation Failure| | | <a name=\"361\">361</a>|Aggregation Measure Failure| | | <a name=\"370\">370</a>|Result Retrieval Failure| | | <a name=\"371\">371</a>|Result Processing Failure| | | <a name=\"372\">372</a>|Vendor Result Processing Failure| | | <a name=\"373\">373</a>|Vendor Result Mapping Failure| | | <a name=\"374\">374</a>|Vendor Library Unauthorised| | | <a name=\"375\">375</a>|Vendor Connectivity Error| | | <a name=\"376\">376</a>|Vendor Interface Error| | | <a name=\"377\">377</a>|Vendor Pricing Failure| | | <a name=\"378\">378</a>|Vendor Translation Failure| | | <a name=\"379\">379</a>|Vendor Key Mapping Failure| | | <a name=\"380\">380</a>|Vendor Reflection Failure| | | <a name=\"390\">390</a>|Attempt To Upsert Duplicate Quotes| | | <a name=\"391\">391</a>|Corporate Action Source Does Not Exist| | | <a name=\"392\">392</a>|Corporate Action Source Already Exists| | | <a name=\"393\">393</a>|Instrument Identifier Already In Use| | | <a name=\"394\">394</a>|Properties Not Found| | | <a name=\"395\">395</a>|Batch Operation Aborted| | | <a name=\"400\">400</a>|Invalid Iso4217 Currency Code| | | <a name=\"401\">401</a>|Cannot Assign Instrument Identifier To Currency| | | <a name=\"402\">402</a>|Cannot Assign Currency Identifier To Non Currency| | | <a name=\"403\">403</a>|Currency Instrument Cannot Be Deleted| | | <a name=\"404\">404</a>|Currency Instrument Cannot Have Economic Definition| | | <a name=\"405\">405</a>|Currency Instrument Cannot Have Lookthrough Portfolio| | | <a name=\"406\">406</a>|Cannot Create Currency Instrument With Multiple Identifiers| | | <a name=\"407\">407</a>|Specified Currency Is Undefined| | | <a name=\"410\">410</a>|Index Does Not Exist| | | <a name=\"411\">411</a>|Sort Field Does Not Exist| | | <a name=\"413\">413</a>|Negative Pagination Parameters| | | <a name=\"414\">414</a>|Invalid Search Syntax| | | <a name=\"415\">415</a>|Filter Execution Timeout| | | <a name=\"420\">420</a>|Side Definition Inconsistent| | | <a name=\"450\">450</a>|Invalid Quote Access Metadata Rule| | | <a name=\"451\">451</a>|Access Metadata Not Found| | | <a name=\"452\">452</a>|Invalid Access Metadata Identifier| | | <a name=\"460\">460</a>|Standard Resource Not Found| | | <a name=\"461\">461</a>|Standard Resource Conflict| | | <a name=\"462\">462</a>|Calendar Not Found| | | <a name=\"463\">463</a>|Date In A Calendar Not Found| | | <a name=\"464\">464</a>|Invalid Date Source Data| | | <a name=\"465\">465</a>|Invalid Timezone| | | <a name=\"601\">601</a>|Person Identifier Already In Use| | | <a name=\"602\">602</a>|Person Not Found| | | <a name=\"603\">603</a>|Cannot Set Identifier| | | <a name=\"617\">617</a>|Invalid Recipe Specification In Request| | | <a name=\"618\">618</a>|Inline Recipe Deserialisation Failure| | | <a name=\"619\">619</a>|Identifier Types Not Set For Entity| | | <a name=\"620\">620</a>|Cannot Delete All Client Defined Identifiers| | | <a name=\"650\">650</a>|The Order requested was not found.| | | <a name=\"654\">654</a>|The Allocation requested was not found.| | | <a name=\"655\">655</a>|Cannot build the fx forward target with the given holdings.| | | <a name=\"656\">656</a>|Group does not contain expected entities.| | | <a name=\"667\">667</a>|Relation definition already exists| | | <a name=\"673\">673</a>|Missing entitlements for entities in Group| | | <a name=\"674\">674</a>|Next Best Action not found| | | <a name=\"676\">676</a>|Relation definition not defined| | | <a name=\"677\">677</a>|Invalid entity identifier for relation| | | <a name=\"681\">681</a>|Sorting by specified field not supported|One or more of the provided fields to order by were either invalid or not supported. | | <a name=\"682\">682</a>|Too many fields to sort by|The number of fields to sort the data by exceeds the number allowed by the endpoint | | <a name=\"684\">684</a>|Sequence Not Found| | | <a name=\"685\">685</a>|Sequence Already Exists| | | <a name=\"686\">686</a>|Non-cycling sequence has been exhausted| | | <a name=\"687\">687</a>|Legal Entity Identifier Already In Use| | | <a name=\"688\">688</a>|Legal Entity Not Found| | | <a name=\"689\">689</a>|The supplied pagination token is invalid| | | <a name=\"690\">690</a>|Property Type Is Not Supported| | | <a name=\"691\">691</a>|Multiple Tax-lots For Currency Type Is Not Supported| | # noqa: E501 The version of the OpenAPI document: 0.11.2275 Contact: [email protected] Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six class LusidInstrument(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. required_map (dict): The key is attribute name and the value is whether it is 'required' or 'optional'. """ openapi_types = { 'instrument_type': 'str' } attribute_map = { 'instrument_type': 'instrumentType' } required_map = { 'instrument_type': 'required' } discriminator_value_class_map = { 'EquityOption': 'EquityOption', 'InstrumentLeg': 'InstrumentLeg', 'InterestRateSwaption': 'InterestRateSwaption', 'FxForward': 'FxForward', 'InterestRateSwap': 'InterestRateSwap', 'ExoticInstrument': 'ExoticInstrument', 'FxOption': 'FxOption', 'Bond': 'Bond', 'TermDeposit': 'TermDeposit', 'CreditDefaultSwap': 'CreditDefaultSwap', 'Future': 'Future' } def __init__(self, instrument_type=None): # noqa: E501 """ LusidInstrument - a model defined in OpenAPI :param instrument_type: The available values are: QuotedSecurity, InterestRateSwap, FxForward, Future, ExoticInstrument, FxOption, CreditDefaultSwap, InterestRateSwaption, Bond, EquityOption, FixedLeg, FloatingLeg, BespokeCashflowLeg, Unknown, TermDeposit (required) :type instrument_type: str """ # noqa: E501 self._instrument_type = None self.discriminator = 'instrument_type' self.instrument_type = instrument_type @property def instrument_type(self): """Gets the instrument_type of this LusidInstrument. # noqa: E501 The available values are: QuotedSecurity, InterestRateSwap, FxForward, Future, ExoticInstrument, FxOption, CreditDefaultSwap, InterestRateSwaption, Bond, EquityOption, FixedLeg, FloatingLeg, BespokeCashflowLeg, Unknown, TermDeposit # noqa: E501 :return: The instrument_type of this LusidInstrument. # noqa: E501 :rtype: str """ return self._instrument_type @instrument_type.setter def instrument_type(self, instrument_type): """Sets the instrument_type of this LusidInstrument. The available values are: QuotedSecurity, InterestRateSwap, FxForward, Future, ExoticInstrument, FxOption, CreditDefaultSwap, InterestRateSwaption, Bond, EquityOption, FixedLeg, FloatingLeg, BespokeCashflowLeg, Unknown, TermDeposit # noqa: E501 :param instrument_type: The instrument_type of this LusidInstrument. # noqa: E501 :type: str """ if instrument_type is None: raise ValueError("Invalid value for `instrument_type`, must not be `None`") # noqa: E501 allowed_values = ["QuotedSecurity", "InterestRateSwap", "FxForward", "Future", "ExoticInstrument", "FxOption", "CreditDefaultSwap", "InterestRateSwaption", "Bond", "EquityOption", "FixedLeg", "FloatingLeg", "BespokeCashflowLeg", "Unknown", "TermDeposit"] # noqa: E501 if instrument_type not in allowed_values: raise ValueError( "Invalid value for `instrument_type` ({0}), must be one of {1}" # noqa: E501 .format(instrument_type, allowed_values) ) self._instrument_type = instrument_type def get_real_child_model(self, data): """Returns the real base class specified by the discriminator""" discriminator_key = self.attribute_map[self.discriminator] discriminator_value = data[discriminator_key] return self.discriminator_value_class_map.get(discriminator_value) def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, LusidInstrument): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

221.941558

28,647

0.692179

4,703

34,179

5.007229

0.218371

0.042465

0.019873

0.016009

0.145569

0.0989

0.070704

0.064759

0.055841

0.04565

0

0.053849

0.170339

34,179

153

28,648

223.392157

0.776598

0.875128

0

0.025641

0

0.205901

0

1

0.115385

false

0

0.038462

0

0.320513

0.025641

0

null

0

1

0

1

0

null

0

3

8a1c71c22813d34b18261a3c040c83b4a288d938

1,272

py

Python

caravan_search_engine/test/test_task.py

crest-cassia/caravan

0a8e606e31d2d36a9379bdc00fafe55cf9144da6

[ "MIT" ]

4

2017-12-27T06:04:46.000Z

2018-04-27T04:07:49.000Z

caravan_search_engine/test/test_task.py

crest-cassia/caravan

0a8e606e31d2d36a9379bdc00fafe55cf9144da6

[ "MIT" ]

null

caravan_search_engine/test/test_task.py

crest-cassia/caravan

0a8e606e31d2d36a9379bdc00fafe55cf9144da6

[ "MIT" ]

null

import unittest from caravan.task import Task from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t = Task(1234, "echo hello world") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), "echo hello world") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10): t = Task.create("echo %d" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create("echo %d" % i) for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create("echo %d" % i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__': unittest.main()

30.285714

63

0.589623

180

1,272

4.061111

0.305556

0.266758

0.19699

0.045144

0.361149

0.281806

0.155951

0

0.044257

0.253931

1,272

41

64

31.02439

0.726027

0

0.121212

0

0.047956

0

0.424242

1

0.151515

false

0

0.090909

0

0.272727

0

null

1

0

null

0

1

0

3

8a2561d549e0edb64456facf130fd386d46356d5

96,782

py

Python

.infra/setup/playbooks/roles/ansible.kubernetes-modules/library/openshift_v1_build_config_list.py

cvicens/lab-knative

ef98aa111e566c6d33fd72c61f9c0d93a2c05b2f

[ "Apache-2.0" ]

null

.infra/setup/playbooks/roles/ansible.kubernetes-modules/library/openshift_v1_build_config_list.py

cvicens/lab-knative

ef98aa111e566c6d33fd72c61f9c0d93a2c05b2f

[ "Apache-2.0" ]

null

.infra/setup/playbooks/roles/ansible.kubernetes-modules/library/openshift_v1_build_config_list.py

cvicens/lab-knative

ef98aa111e566c6d33fd72c61f9c0d93a2c05b2f

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a list of build_configs. List operations provide a snapshot read of the underlying objects, returning a resource_version representing a consistent version of the listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description: - Token used to connect to the API. cert_file: description: - Path to a certificate used to authenticate with the API. type: path context: description: - The name of a context found in the Kubernetes config file. debug: description: - Enable debug output from the OpenShift helper. Logging info is written to KubeObjHelper.log default: false type: bool force: description: - If set to C(True), and I(state) is C(present), an existing object will updated, and lists will be replaced, rather than merged. default: false type: bool host: description: - Provide a URL for acessing the Kubernetes API. key_file: description: - Path to a key file used to authenticate with the API. type: path kubeconfig: description: - Path to an existing Kubernetes config file. If not provided, and no other connection options are provided, the openshift client will attempt to load the default configuration file from I(~/.kube/config.json). type: path password: description: - Provide a password for connecting to the API. Use in conjunction with I(username). resource_definition: description: - Provide the YAML definition for the object, bypassing any modules parameters intended to define object attributes. type: dict src: description: - Provide a path to a file containing the YAML definition of the object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description: - Path to a CA certificate used to authenticate with the API. type: path state: description: - Determines if an object should be created, patched, or deleted. When set to C(present), the object will be created, if it does not exist, or patched, if parameter values differ from the existing object's attributes, and deleted, if set to C(absent). A patch operation results in merging lists and updating dictionaries, with lists being merged into a unique set of values. If a list contains a dictionary with a I(name) or I(type) attribute, a strategic merge is performed, where individual elements with a matching I(name_) or I(type) are merged. To force the replacement of lists, set the I(force) option to C(True). default: present choices: - present - absent username: description: - Provide a username for connecting to the API. verify_ssl: description: - Whether or not to verify the API server's SSL certificates. type: bool requirements: - openshift == 0.3.3 ''' EXAMPLES = ''' ''' RETURN = ''' api_version: type: string description: Requested API version build_config_list: type: complex returned: when I(state) = C(present) contains: api_version: description: - APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. type: str items: description: - items is a list of build configs type: list contains: api_version: description: - APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. type: str kind: description: - Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. type: str metadata: description: - metadata for BuildConfig. type: complex contains: annotations: description: - Annotations is an unstructured key value map stored with a resource that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects. type: complex contains: str, str cluster_name: description: - The name of the cluster which the object belongs to. This is used to distinguish resources with same name and namespace in different clusters. This field is not set anywhere right now and apiserver is going to ignore it if set in create or update request. type: str creation_timestamp: description: - CreationTimestamp is a timestamp representing the server time when this object was created. It is not guaranteed to be set in happens-before order across separate operations. Clients may not set this value. It is represented in RFC3339 form and is in UTC. Populated by the system. Read-only. Null for lists. type: complex contains: {} deletion_grace_period_seconds: description: - Number of seconds allowed for this object to gracefully terminate before it will be removed from the system. Only set when deletionTimestamp is also set. May only be shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date and time at which this resource will be deleted. This field is set by the server when a graceful deletion is requested by the user, and is not directly settable by a client. The resource is expected to be deleted (no longer visible from resource lists, and not reachable by name) after the time in this field. Once set, this value may not be unset or be set further into the future, although it may be shortened or the resource may be deleted prior to this time. For example, a user may request that a pod is deleted in 30 seconds. The Kubelet will react by sending a graceful termination signal to the containers in the pod. After that 30 seconds, the Kubelet will send a hard termination signal (SIGKILL) to the container and after cleanup, remove the pod from the API. In the presence of network partitions, this object may still exist after this timestamp, until an administrator or automated process can determine the resource is fully terminated. If not set, graceful deletion of the object has not been requested. Populated by the system when a graceful deletion is requested. Read-only. type: complex contains: {} finalizers: description: - Must be empty before the object is deleted from the registry. Each entry is an identifier for the responsible component that will remove the entry from the list. If the deletionTimestamp of the object is non-nil, entries in this list can only be removed. type: list contains: str generate_name: description: - GenerateName is an optional prefix, used by the server, to generate a unique name ONLY IF the Name field has not been provided. If this field is used, the name returned to the client will be different than the name passed. This value will also be combined with a unique suffix. The provided value has the same validation rules as the Name field, and may be truncated by the length of the suffix required to make the value unique on the server. If this field is specified and the generated name exists, the server will NOT return a 409 - instead, it will either return 201 Created or 500 with Reason ServerTimeout indicating a unique name could not be found in the time allotted, and the client should retry (optionally after the time indicated in the Retry-After header). Applied only if Name is not specified. type: str generation: description: - A sequence number representing a specific generation of the desired state. Populated by the system. Read-only. type: int initializers: description: - An initializer is a controller which enforces some system invariant at object creation time. This field is a list of initializers that have not yet acted on this object. If nil or empty, this object has been completely initialized. Otherwise, the object is considered uninitialized and is hidden (in list/watch and get calls) from clients that haven't explicitly asked to observe uninitialized objects. When an object is created, the system will populate this list with the current set of initializers. Only privileged users may set or modify this list. Once it is empty, it may not be modified further by any user. type: complex contains: pending: description: - Pending is a list of initializers that must execute in order before this object is visible. When the last pending initializer is removed, and no failing result is set, the initializers struct will be set to nil and the object is considered as initialized and visible to all clients. type: list contains: name: description: - name of the process that is responsible for initializing this object. type: str result: description: - If result is set with the Failure field, the object will be persisted to storage and then deleted, ensuring that other clients can observe the deletion. type: complex contains: api_version: description: - APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. type: str code: description: - Suggested HTTP return code for this status, 0 if not set. type: int details: description: - Extended data associated with the reason. Each reason may define its own extended details. This field is optional and the data returned is not guaranteed to conform to any schema except that defined by the reason type. type: complex contains: causes: description: - The Causes array includes more details associated with the StatusReason failure. Not all StatusReasons may provide detailed causes. type: list contains: field: description: - 'The field of the resource that has caused this error, as named by its JSON serialization. May include dot and postfix notation for nested attributes. Arrays are zero-indexed. Fields may appear more than once in an array of causes due to fields having multiple errors. Optional. Examples: "name" - the field "name" on the current resource "items[0].name" - the field "name" on the first array entry in "items"' type: str message: description: - A human-readable description of the cause of the error. This field may be presented as-is to a reader. type: str reason: description: - A machine-readable description of the cause of the error. If this value is empty there is no information available. type: str group: description: - The group attribute of the resource associated with the status StatusReason. type: str kind: description: - The kind attribute of the resource associated with the status StatusReason. On some operations may differ from the requested resource Kind. type: str name: description: - The name attribute of the resource associated with the status StatusReason (when there is a single name which can be described). type: str retry_after_seconds: description: - If specified, the time in seconds before the operation should be retried. type: int uid: description: - UID of the resource. (when there is a single resource which can be described). type: str kind: description: - Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. type: str message: description: - A human-readable description of the status of this operation. type: str metadata: description: - Standard list metadata. type: complex contains: resource_version: description: - String that identifies the server's internal version of this object that can be used by clients to determine when objects have changed. Value must be treated as opaque by clients and passed unmodified back to the server. Populated by the system. Read-only. type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str reason: description: - A machine-readable description of why this operation is in the "Failure" status. If this value is empty there is no information available. A Reason clarifies an HTTP status code but does not override it. type: str status: description: - 'Status of the operation. One of: "Success" or "Failure".' type: str labels: description: - Map of string keys and values that can be used to organize and categorize (scope and select) objects. May match selectors of replication controllers and services. type: complex contains: str, str name: description: - Name must be unique within a namespace. Is required when creating resources, although some resources may allow a client to request the generation of an appropriate name automatically. Name is primarily intended for creation idempotence and configuration definition. Cannot be updated. type: str namespace: description: - Namespace defines the space within each name must be unique. An empty namespace is equivalent to the "default" namespace, but "default" is the canonical representation. Not all objects are required to be scoped to a namespace - the value of this field for those objects will be empty. Must be a DNS_LABEL. Cannot be updated. type: str owner_references: description: - List of objects depended by this object. If ALL objects in the list have been deleted, this object will be garbage collected. If this object is managed by a controller, then an entry in this list will point to this controller, with the controller field set to true. There cannot be more than one managing controller. type: list contains: api_version: description: - API version of the referent. type: str block_owner_deletion: description: - If true, AND if the owner has the "foregroundDeletion" finalizer, then the owner cannot be deleted from the key-value store until this reference is removed. Defaults to false. To set this field, a user needs "delete" permission of the owner, otherwise 422 (Unprocessable Entity) will be returned. type: bool controller: description: - If true, this reference points to the managing controller. type: bool kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str uid: description: - UID of the referent. type: str resource_version: description: - An opaque value that represents the internal version of this object that can be used by clients to determine when objects have changed. May be used for optimistic concurrency, change detection, and the watch operation on a resource or set of resources. Clients must treat these values as opaque and passed unmodified back to the server. They may only be valid for a particular resource or set of resources. Populated by the system. Read-only. Value must be treated as opaque by clients and . type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str uid: description: - UID is the unique in time and space value for this object. It is typically generated by the server on successful creation of a resource and is not allowed to change on PUT operations. Populated by the system. Read-only. type: str spec: description: - spec holds all the input necessary to produce a new build, and the conditions when to trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in seconds, counted from the time when a build pod gets scheduled in the system, that the build may be active on a node before the system actively tries to terminate the build; value must be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old failed builds to retain. If not specified, all failed builds are retained. type: int node_selector: description: - nodeSelector is a selector which must be true for the build pod to fit on a node If nil, it can be overridden by default build nodeselector values for the cluster. If set to an empty map or a map with any values, default build nodeselector values are ignored. type: complex contains: str, str output: description: - output describes the Docker image the Strategy should produce. type: complex contains: image_labels: description: - imageLabels define a list of labels that are applied to the resulting image. If there are multiple labels with the same name then the last one in the list is used. type: list contains: name: description: - name defines the name of the label. It must have non-zero length. type: str value: description: - value defines the literal value of the label. type: str push_secret: description: - PushSecret is the name of a Secret that would be used for setting up the authentication for executing the Docker push to authentication enabled Docker Registry (or Docker Hub). type: complex contains: name: description: - Name of the referent. type: str to: description: - to defines an optional location to push the output of this build to. Kind must be one of 'ImageStreamTag' or 'DockerImage'. This value will be used to look up a Docker image repository to push to. In the case of an ImageStreamTag, the ImageStreamTag will be looked for in the namespace of the build unless Namespace is specified. type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str post_commit: description: - postCommit is a build hook executed after the build output image is committed, before it is pushed to a registry. type: complex contains: args: description: - args is a list of arguments that are provided to either Command, Script or the Docker image's default entrypoint. The arguments are placed immediately after the command to be run. type: list contains: str command: description: - command is the command to run. It may not be specified with Script. This might be needed if the image doesn't have `/bin/sh`, or if you do not want to use a shell. In all other cases, using Script might be more convenient. type: list contains: str script: description: - script is a shell script to be run with `/bin/sh -ic`. It may not be specified with Command. Use Script when a shell script is appropriate to execute the post build hook, for example for running unit tests with `rake test`. If you need control over the image entrypoint, or if the image does not have `/bin/sh`, use Command and/or Args. The `-i` flag is needed to support CentOS and RHEL images that use Software Collections (SCL), in order to have the appropriate collections enabled in the shell. E.g., in the Ruby image, this is necessary to make `ruby`, `bundle` and other binaries available in the PATH. type: str resources: description: - resources computes resource requirements to execute the build. type: complex contains: limits: description: - Limits describes the maximum amount of compute resources allowed. type: complex contains: str, str requests: description: - Requests describes the minimum amount of compute resources required. If Requests is omitted for a container, it defaults to Limits if that is explicitly specified, otherwise to an implementation-defined value. type: complex contains: str, str revision: description: - revision is the information from the source for a specific repo snapshot. This is optional. type: complex contains: git: description: - Git contains information about git-based build source type: complex contains: author: description: - author is the author of a specific commit type: complex contains: email: description: - email of the source control user type: str name: description: - name of the source control user type: str commit: description: - commit is the commit hash identifying a specific commit type: str committer: description: - committer is the committer of a specific commit type: complex contains: email: description: - email of the source control user type: str name: description: - name of the source control user type: str message: description: - message is the description of a specific commit type: str type: description: - type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy: description: - RunPolicy describes how the new build created from this build configuration will be scheduled for execution. This is optional, if not specified we default to "Serial". type: str service_account: description: - serviceAccount is the name of the ServiceAccount to use to run the pod created by this build. The pod will be allowed to use secrets referenced by the ServiceAccount type: str source: description: - source describes the SCM in use. type: complex contains: binary: description: - binary builds accept a binary as their input. The binary is generally assumed to be a tar, gzipped tar, or zip file depending on the strategy. For Docker builds, this is the build context and an optional Dockerfile may be specified to override any Dockerfile in the build context. For Source builds, this is assumed to be an archive as described above. For Source and Docker builds, if binary.asFile is set the build will receive a directory with a single file. contextDir may be used when an archive is provided. Custom builds will receive this binary as input on STDIN. type: complex contains: as_file: description: - asFile indicates that the provided binary input should be considered a single file within the build input. For example, specifying "webapp.war" would place the provided binary as `/webapp.war` for the builder. If left empty, the Docker and Source build strategies assume this file is a zip, tar, or tar.gz file and extract it as the source. The custom strategy receives this binary as standard input. This filename may not contain slashes or be '..' or '.'. type: str context_dir: description: - contextDir specifies the sub-directory where the source code for the application exists. This allows to have buildable sources in directory other than root of repository. type: str dockerfile: description: - dockerfile is the raw contents of a Dockerfile which should be built. When this option is specified, the FROM may be modified based on your strategy base image and additional ENV stanzas from your strategy environment will be added after the FROM, but before the rest of your Dockerfile stanzas. The Dockerfile source type may be used with other options like git - in those cases the Git repo will have any innate Dockerfile replaced in the context dir. type: str git: description: - git contains optional information about git build source type: complex contains: http_proxy: description: - httpProxy is a proxy used to reach the git repository over http type: str https_proxy: description: - httpsProxy is a proxy used to reach the git repository over https type: str no_proxy: description: - noProxy is the list of domains for which the proxy should not be used type: str ref: description: - ref is the branch/tag/ref to build. type: str uri: description: - uri points to the source that will be built. The structure of the source will depend on the type of build to run type: str images: description: - images describes a set of images to be used to provide source for the build type: list contains: _from: description: - from is a reference to an ImageStreamTag, ImageStreamImage, or DockerImage to copy source from. type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str paths: description: - paths is a list of source and destination paths to copy from the image. type: list contains: destination_dir: description: - destinationDir is the relative directory within the build directory where files copied from the image are placed. type: str source_path: description: - sourcePath is the absolute path of the file or directory inside the image to copy to the build directory. If the source path ends in /. then the content of the directory will be copied, but the directory itself will not be created at the destination. type: str pull_secret: description: - pullSecret is a reference to a secret to be used to pull the image from a registry If the image is pulled from the OpenShift registry, this field does not need to be set. type: complex contains: name: description: - Name of the referent. type: str secrets: description: - secrets represents a list of secrets and their destinations that will be used only for the build. type: list contains: destination_dir: description: - destinationDir is the directory where the files from the secret should be available for the build time. For the Source build strategy, these will be injected into a container where the assemble script runs. Later, when the script finishes, all files injected will be truncated to zero length. For the Docker build strategy, these will be copied into the build directory, where the Dockerfile is located, so users can ADD or COPY them during docker build. type: str secret: description: - secret is a reference to an existing secret that you want to use in your build. type: complex contains: name: description: - Name of the referent. type: str source_secret: description: - "sourceSecret is the name of a Secret that would be used for setting\ \ up the authentication for cloning private repository. The secret\ \ contains valid credentials for remote repository, where the\ \ data's key represent the authentication method to be used and\ \ value is the base64 encoded credentials. Supported auth methods\ \ are: ssh-privatekey." type: complex contains: name: description: - Name of the referent. type: str type: description: - type of build input to accept type: str strategy: description: - strategy defines how to perform a build. type: complex contains: custom_strategy: description: - customStrategy holds the parameters to the Custom build strategy type: complex contains: _from: description: - from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image should be pulled type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str build_api_version: description: - buildAPIVersion is the requested API version for the Build object serialized and passed to the custom builder type: str env: description: - env contains additional environment variables you want to pass into a builder container. type: list contains: name: description: - Name of the environment variable. Must be a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".' type: str value_from: description: - Source for the environment variable's value. Cannot be used if value is not empty. type: complex contains: config_map_key_ref: description: - Selects a key of a ConfigMap. type: complex contains: key: description: - The key to select. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the ConfigMap or it's key must be defined type: bool field_ref: description: - 'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of the schema the FieldPath is written in terms of, defaults to "v1". type: str field_path: description: - Path of the field to select in the specified API version. type: str resource_field_ref: description: - 'Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name: description: - 'Container name: required for volumes, optional for env vars' type: str divisor: description: - Specifies the output format of the exposed resources, defaults to "1" type: str resource: description: - 'Required: resource to select' type: str secret_key_ref: description: - Selects a key of a secret in the pod's namespace type: complex contains: key: description: - The key of the secret to select from. Must be a valid secret key. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the Secret or it's key must be defined type: bool expose_docker_socket: description: - exposeDockerSocket will allow running Docker commands (and build Docker images) from inside the Docker container. type: bool force_pull: description: - forcePull describes if the controller should configure the build pod to always pull the images for the builder or only pull if it is not present locally type: bool pull_secret: description: - pullSecret is the name of a Secret that would be used for setting up the authentication for pulling the Docker images from the private Docker registries type: complex contains: name: description: - Name of the referent. type: str secrets: description: - secrets is a list of additional secrets that will be included in the build pod type: list contains: mount_path: description: - mountPath is the path at which to mount the secret type: str secret_source: description: - secretSource is a reference to the secret type: complex contains: name: description: - Name of the referent. type: str docker_strategy: description: - dockerStrategy holds the parameters to the Docker build strategy. type: complex contains: _from: description: - from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image should be pulled the resulting image will be used in the FROM line of the Dockerfile for this build. type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str build_args: description: - buildArgs contains build arguments that will be resolved in the Dockerfile. See type: list contains: name: description: - Name of the environment variable. Must be a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".' type: str value_from: description: - Source for the environment variable's value. Cannot be used if value is not empty. type: complex contains: config_map_key_ref: description: - Selects a key of a ConfigMap. type: complex contains: key: description: - The key to select. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the ConfigMap or it's key must be defined type: bool field_ref: description: - 'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of the schema the FieldPath is written in terms of, defaults to "v1". type: str field_path: description: - Path of the field to select in the specified API version. type: str resource_field_ref: description: - 'Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name: description: - 'Container name: required for volumes, optional for env vars' type: str divisor: description: - Specifies the output format of the exposed resources, defaults to "1" type: str resource: description: - 'Required: resource to select' type: str secret_key_ref: description: - Selects a key of a secret in the pod's namespace type: complex contains: key: description: - The key of the secret to select from. Must be a valid secret key. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the Secret or it's key must be defined type: bool dockerfile_path: description: - dockerfilePath is the path of the Dockerfile that will be used to build the Docker image, relative to the root of the context (contextDir). type: str env: description: - env contains additional environment variables you want to pass into a builder container. type: list contains: name: description: - Name of the environment variable. Must be a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".' type: str value_from: description: - Source for the environment variable's value. Cannot be used if value is not empty. type: complex contains: config_map_key_ref: description: - Selects a key of a ConfigMap. type: complex contains: key: description: - The key to select. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the ConfigMap or it's key must be defined type: bool field_ref: description: - 'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of the schema the FieldPath is written in terms of, defaults to "v1". type: str field_path: description: - Path of the field to select in the specified API version. type: str resource_field_ref: description: - 'Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name: description: - 'Container name: required for volumes, optional for env vars' type: str divisor: description: - Specifies the output format of the exposed resources, defaults to "1" type: str resource: description: - 'Required: resource to select' type: str secret_key_ref: description: - Selects a key of a secret in the pod's namespace type: complex contains: key: description: - The key of the secret to select from. Must be a valid secret key. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the Secret or it's key must be defined type: bool force_pull: description: - forcePull describes if the builder should pull the images from registry prior to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the system can use when building images to reduce the final size or time spent building the image. The default policy is 'None' which means the final build image will be equivalent to an image created by the Docker build API. The experimental policy 'SkipLayers' will avoid commiting new layers in between each image step, and will fail if the Dockerfile cannot provide compatibility with the 'None' policy. An additional experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but simply warns if compatibility cannot be preserved. type: str no_cache: description: - noCache if set to true indicates that the docker build must be executed with the --no-cache=true flag type: bool pull_secret: description: - pullSecret is the name of a Secret that would be used for setting up the authentication for pulling the Docker images from the private Docker registries type: complex contains: name: description: - Name of the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline build strategy. This strategy is in tech preview. type: complex contains: env: description: - env contains additional environment variables you want to pass into a build pipeline. type: list contains: name: description: - Name of the environment variable. Must be a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".' type: str value_from: description: - Source for the environment variable's value. Cannot be used if value is not empty. type: complex contains: config_map_key_ref: description: - Selects a key of a ConfigMap. type: complex contains: key: description: - The key to select. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the ConfigMap or it's key must be defined type: bool field_ref: description: - 'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of the schema the FieldPath is written in terms of, defaults to "v1". type: str field_path: description: - Path of the field to select in the specified API version. type: str resource_field_ref: description: - 'Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name: description: - 'Container name: required for volumes, optional for env vars' type: str divisor: description: - Specifies the output format of the exposed resources, defaults to "1" type: str resource: description: - 'Required: resource to select' type: str secret_key_ref: description: - Selects a key of a secret in the pod's namespace type: complex contains: key: description: - The key of the secret to select from. Must be a valid secret key. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the Secret or it's key must be defined type: bool jenkinsfile: description: - Jenkinsfile defines the optional raw contents of a Jenkinsfile which defines a Jenkins pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath is the optional path of the Jenkinsfile that will be used to configure the pipeline relative to the root of the context (contextDir). If both JenkinsfilePath & Jenkinsfile are both not specified, this defaults to Jenkinsfile in the root of the specified contextDir. type: str source_strategy: description: - sourceStrategy holds the parameters to the Source build strategy. type: complex contains: _from: description: - from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image should be pulled type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str env: description: - env contains additional environment variables you want to pass into a builder container. type: list contains: name: description: - Name of the environment variable. Must be a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. Defaults to "".' type: str value_from: description: - Source for the environment variable's value. Cannot be used if value is not empty. type: complex contains: config_map_key_ref: description: - Selects a key of a ConfigMap. type: complex contains: key: description: - The key to select. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the ConfigMap or it's key must be defined type: bool field_ref: description: - 'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of the schema the FieldPath is written in terms of, defaults to "v1". type: str field_path: description: - Path of the field to select in the specified API version. type: str resource_field_ref: description: - 'Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name: description: - 'Container name: required for volumes, optional for env vars' type: str divisor: description: - Specifies the output format of the exposed resources, defaults to "1" type: str resource: description: - 'Required: resource to select' type: str secret_key_ref: description: - Selects a key of a secret in the pod's namespace type: complex contains: key: description: - The key of the secret to select from. Must be a valid secret key. type: str name: description: - Name of the referent. type: str optional: description: - Specify whether the Secret or it's key must be defined type: bool force_pull: description: - forcePull describes if the builder should pull the images from registry prior to building. type: bool incremental: description: - incremental flag forces the Source build to do incremental builds if true. type: bool pull_secret: description: - pullSecret is the name of a Secret that would be used for setting up the authentication for pulling the Docker images from the private Docker registries type: complex contains: name: description: - Name of the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a list of source/destination pairs that will be copied from the builder to the runtime image. sourcePath can be a file or directory. destinationDir must be a directory. destinationDir can also be empty or equal to ".", in this case it just refers to the root of WORKDIR. Deprecated: This feature will be removed in a future release. Use ImageSource to copy binary artifacts created from one build into a separate runtime image.' type: list contains: destination_dir: description: - destinationDir is the relative directory within the build directory where files copied from the image are placed. type: str source_path: description: - sourcePath is the absolute path of the file or directory inside the image to copy to the build directory. If the source path ends in /. then the content of the directory will be copied, but the directory itself will not be created at the destination. type: str runtime_image: description: - 'runtimeImage is an optional image that is used to run an application without unneeded dependencies installed. The building of the application is still done in the builder image but, post build, you can copy the needed artifacts in the runtime image for use. Deprecated: This feature will be removed in a future release. Use ImageSource to copy binary artifacts created from one build into a separate runtime image.' type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str scripts: description: - scripts is the location of Source scripts type: str type: description: - type is the kind of build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of old successful builds to retain. If not specified, all successful builds are retained. type: int triggers: description: - triggers determine how new Builds can be launched from a BuildConfig. If no triggers are defined, a new build can only occur as a result of an explicit client build creation. type: list contains: bitbucket: description: - BitbucketWebHook contains the parameters for a Bitbucket webhook type of trigger type: complex contains: allow_env: description: - allowEnv determines whether the webhook can set environment variables; can only be set to true for GenericWebHook. type: bool secret: description: - secret used to validate requests. type: str generic: description: - generic contains the parameters for a Generic webhook type of trigger type: complex contains: allow_env: description: - allowEnv determines whether the webhook can set environment variables; can only be set to true for GenericWebHook. type: bool secret: description: - secret used to validate requests. type: str github: description: - github contains the parameters for a GitHub webhook type of trigger type: complex contains: allow_env: description: - allowEnv determines whether the webhook can set environment variables; can only be set to true for GenericWebHook. type: bool secret: description: - secret used to validate requests. type: str gitlab: description: - GitLabWebHook contains the parameters for a GitLab webhook type of trigger type: complex contains: allow_env: description: - allowEnv determines whether the webhook can set environment variables; can only be set to true for GenericWebHook. type: bool secret: description: - secret used to validate requests. type: str image_change: description: - imageChange contains parameters for an ImageChange type of trigger type: complex contains: _from: description: - from is a reference to an ImageStreamTag that will trigger a build when updated It is optional. If no From is specified, the From image from the build strategy will be used. Only one ImageChangeTrigger with an empty From reference is allowed in a build configuration. type: complex contains: api_version: description: - API version of the referent. type: str field_path: description: - 'If referring to a piece of an object instead of an entire object, this string should contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if the object reference is to a container within a pod, this would take on a value like: "spec.containers{name}" (where "name" refers to the name of the container that triggered the event) or if no container name is specified "spec.containers[2]" (container with index 2 in this pod). This syntax is chosen only to have some well-defined way of referencing a part of an object.' type: str kind: description: - Kind of the referent. type: str name: description: - Name of the referent. type: str namespace: description: - Namespace of the referent. type: str resource_version: description: - Specific resourceVersion to which this reference is made, if any. type: str uid: description: - UID of the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is used internally by the ImageChangeController to save last used image ID for build type: str type: description: - type is the type of build trigger type: str status: description: - status holds any relevant information about a build config type: complex contains: last_version: description: - lastVersion is used to inform about number of last triggered build. type: int kind: description: - Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. type: str metadata: description: - metadata for BuildConfigList. type: complex contains: resource_version: description: - String that identifies the server's internal version of this object that can be used by clients to determine when objects have changed. Value must be treated as opaque by clients and passed unmodified back to the server. Populated by the system. Read-only. type: str self_link: description: - SelfLink is a URL representing this object. Populated by the system. Read-only. type: str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The helper failed to init, so there is no module object. All we can do is raise the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg="Module failed!", error=str(exc)) if __name__ == '__main__': main()

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96,782

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0.113293

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96,782

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1

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null

0

3

8a6c803544f7e0d285bc37ff4aefd197349a5940

456

py

Python

src/trw/reporting/__init__.py

civodlu/trw

b9a1cf045f61d6df9c65c014ef63b4048972dcdc

[ "MIT" ]

3

2019-07-04T01:20:41.000Z

2020-01-27T02:36:12.000Z

src/trw/reporting/__init__.py

civodlu/trw

b9a1cf045f61d6df9c65c014ef63b4048972dcdc

[ "MIT" ]

null

src/trw/reporting/__init__.py

civodlu/trw

b9a1cf045f61d6df9c65c014ef63b4048972dcdc

[ "MIT" ]

2

2020-10-19T13:46:06.000Z

2021-12-27T02:18:10.000Z

#from trw.utils import collect_hierarchical_module_name, collect_hierarchical_parameter_name, get_batch_n, to_value, \ # safe_lookup, len_batch from .export import as_image_ui8, as_rgb_image, export_image, export_sample, export_as_image from .table_sqlite import TableStream, SQLITE_TYPE_PATTERN, get_table_number_of_rows from .reporting_bokeh import report, create_default_reporting_options from .reporting_bokeh_samples import PanelDataSamplesTabular

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0

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456

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1

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null

0

1

0

1

0

3

8a6d637336ee5d703603ebc196b3672612c215ab

1,976

py

Python

src/learndash/api_resources/user.py

MarkMacDon/learndash-python

a3fbfc45567a524b80c732d735f2ae101119f2e4

[ "MIT" ]

null

src/learndash/api_resources/user.py

MarkMacDon/learndash-python

a3fbfc45567a524b80c732d735f2ae101119f2e4

[ "MIT" ]

1

2021-05-06T19:01:24.000Z

src/learndash/api_resources/user.py

MarkMacDon/learndash-python

a3fbfc45567a524b80c732d735f2ae101119f2e4

[ "MIT" ]

2

2021-05-05T22:45:04.000Z

2021-07-24T08:47:02.000Z

import learndash from learndash.api_resources.abstract import ListableAPIResource from learndash.api_resources.abstract import RetrievableAPIResource from learndash.api_resources.abstract import UpdateableAPIResource from learndash.api_resources.abstract import NestedAPIResource from learndash.api_resources.typing import UserDict from learndash.api_resources.typing import UserCourseProgressDict from learndash.api_resources.typing import UserCourseDict from learndash.api_resources.typing import UserGroupDict from learndash.api_resources.typing import UserQuizProgressDict class User(RetrievableAPIResource[UserDict], ListableAPIResource[UserDict]): api_path = learndash.path_users def course_progress(self, id=None): return UserCourseProgress(id, parent=self) def courses(self, id=None): return UserCourse(id, parent=self) def groups(self, id=None): return UserGroup(id, parent=self) def quiz_progress(self, id=None): return UserQuizProgress(id, parent=self) class UserCourseProgress(ListableAPIResource[UserCourseProgressDict], NestedAPIResource): api_path = learndash.path_user_course_progress # class UserCourseProgressSteps(ListableAPIResource, NestedAPIResource): class UserCourse(ListableAPIResource[UserCourseDict], UpdateableAPIResource, NestedAPIResource): # also deletable api_path = learndash.path_user_courses def instance_url(self): # This endpoint accepts updates and deletions at it's base endpoint return self.class_url() class UserGroup(ListableAPIResource[UserGroupDict], UpdateableAPIResource, NestedAPIResource): # also deleteable api_path = learndash.path_user_groups def instance_url(self): # This endpoint accepts updates and deletions at it's base endpoint return self.class_url() class UserQuizProgress(ListableAPIResource[UserQuizProgressDict], NestedAPIResource): api_path = learndash.path_user_quiz_progress

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1

0

null

0

null

0

1

0

3

8a80483513e593a3c49ee46795ac3b8d601f6b9a

416

py

Python

main/SimulationSettings/ScreenshotsSteppable/Simulation/screenshots_steppables.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

null

main/SimulationSettings/ScreenshotsSteppable/Simulation/screenshots_steppables.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

null

main/SimulationSettings/ScreenshotsSteppable/Simulation/screenshots_steppables.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

1

2021-02-26T21:50:29.000Z

from cc3d.core.PySteppables import * from cc3d import CompuCellSetup from random import random class ScreenshotSteppable(SteppableBasePy): def __init__(self, frequency=10): SteppableBasePy.__init__(self, frequency) def step(self, mcs): if mcs in [3, 5, 19,20, 23, 29, 31]: self.request_screenshot(mcs=mcs, screenshot_label='Cell_Field_CellField_2D_XY_0')

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0.225962

416

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94

27.733333

0.785714

0

0.067146

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1

0.222222

false

0

0.333333

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0

null

0

null

0

1

0

1

0

1

0

3

8a894222f80aae1db1ccdaaadeb6288f55d6b62f

267

py

Python

compliance_suite/exceptions/user_config_exception.py

alextsaihi/rnaget-compliance-suite

a3accae431b9e4f7791dfa5ae867e70da2dd6278

[ "Apache-2.0" ]

1

2019-09-18T14:38:55.000Z

compliance_suite/exceptions/user_config_exception.py

alextsaihi/rnaget-compliance-suite

a3accae431b9e4f7791dfa5ae867e70da2dd6278

[ "Apache-2.0" ]

14

2019-05-24T18:55:23.000Z

2022-02-25T16:56:28.000Z

compliance_suite/exceptions/user_config_exception.py

alextsaihi/rnaget-compliance-suite

a3accae431b9e4f7791dfa5ae867e70da2dd6278

[ "Apache-2.0" ]

8

2019-04-08T14:48:35.000Z

2022-02-04T16:59:59.000Z

# -*- coding: utf-8 -*- """Module compliance_suite.exceptions.user_config_exception.py This module contains class definition for user config file exceptions. """ class UserConfigException(Exception): """Exception for user config file-related errors""" pass

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0.151515

0.131313

0.171717

0

0.004348

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267

10

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true

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0.5

0

null

0

1

0

null

0

1

0

3

8a928ed1a44855a651b9670429234df930921f0a

125

py

Python

api/services/http.py

takos22/API-1

261ecd34648d610169caf27b3712256f757b100d

[ "MIT" ]

null

api/services/http.py

takos22/API-1

261ecd34648d610169caf27b3712256f757b100d

[ "MIT" ]

null

api/services/http.py

takos22/API-1

261ecd34648d610169caf27b3712256f757b100d

[ "MIT" ]

null

from aiohttp import ClientSession from typing import Optional session: Optional[ClientSession] = None __all__ = (session,)

17.857143

39

0.8

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125

6.857143

0.642857

0

0.136

125

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20.833333

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0

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false

0

0.5

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0.5

0

1

0

null

0

1

0

null

0

1

0

3

8aad8de20813d57dc973493fe2b63ad495089392

549

py

Python

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

[ "BSD-3-Clause" ]

null

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

[ "BSD-3-Clause" ]

null

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

[ "BSD-3-Clause" ]

null

"""TODO.""" from setuptools import setup setup( name='nginx-access-tailer', version='0.1', author='swfrench', url='https://github.com/swfrench/nginx-tailer', packages=['nginx_access_tailer',], license='BSD three-clause license', entry_points={ 'console_scripts': ['nginx-access-tailer = nginx_access_tailer.__main__:main'], }, install_requires=[ 'python-gflags >= 3.1.1', 'google-cloud-monitoring >= 0.25.0', ], test_suite='nose.collector', tests_require=['nose', 'mock'], )

24.954545

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64

549

5.171875

0.6875

0.132931

0.205438

0

0.020316

0.193078

549

21

88

26.142857

0.726862

0.009107

0

0.483271

0.104089

0

0.047619

0

1

0

true

0

0.055556

0

0.055556

0

null

0

1

0

null

0

1

0

1

0

3

8ab7c4d71edafc2000970ee8f5e485db6a4fa978

872

py

Python

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/s/super/super_with_arguments.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

463

2015-01-15T08:17:42.000Z

2022-03-28T15:10:20.000Z

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/s/super/super_with_arguments.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

52

2015-01-06T02:43:59.000Z

2022-03-14T11:15:21.000Z

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/s/super/super_with_arguments.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

249

2015-01-07T22:49:49.000Z

2022-03-18T02:32:06.000Z

class Foo: pass class Bar(Foo): def __init__(self): super(Bar, self).__init__() # [super-with-arguments] class Baz(Foo): def __init__(self): super().__init__() class Qux(Foo): def __init__(self): super(Bar, self).__init__() class NotSuperCall(Foo): def __init__(self): super.test(Bar, self).__init__() class InvalidSuperCall(Foo): def __init__(self): super(InvalidSuperCall.__class__, self).__init__() def method_accepting_cls(cls, self): # Using plain `super()` is not valid here, since there's no `__class__` cell found # (Exact exception would be 'RuntimeError: super(): __class__ cell not found') # Instead, we expect to *not* see a warning about `super-with-arguments`. # Explicitly passing `cls`, and `self` to `super()` is what's required. super(cls, self).__init__()

24.222222

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0.65711

113

872

4.557522

0.424779

0.058252

0.097087

0.135922

0.227184

0.116505

0

0.209862

872

35

87

24.914286

0.74746

0.369266

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0.368421

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0.315789

false

0.052632

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null

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null

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1

0

1

0

1

0

3

8abd39aa48321431318051d54854247571fa2704

311

py

Python

betterloader/standard_transforms.py

BinItAI/BetterLoader

29ebcc22b53db6417a4b14d95f0a1e7f5afe7af8

[ "MIT" ]

39

2020-08-11T09:58:08.000Z

2022-02-24T19:22:42.000Z

betterloader/standard_transforms.py

BinItAI/BetterLoader

29ebcc22b53db6417a4b14d95f0a1e7f5afe7af8

[ "MIT" ]

21

2020-08-11T09:58:46.000Z

2021-05-10T12:50:12.000Z

betterloader/standard_transforms.py

BinItAI/BetterLoader

29ebcc22b53db6417a4b14d95f0a1e7f5afe7af8

[ "MIT" ]

2

2020-10-29T14:51:01.000Z

2021-01-08T09:40:34.000Z

import numpy as np from torchvision import transforms np.random.seed(1) class TransformWhileSampling(object): def __init__(self, transform): self.transform = transform def __call__(self, sample): x1 = self.transform(sample) x2 = self.transform(sample) return x1, x2

19.4375

37

0.678457

37

311

5.486486

0.594595

0.256158

0.187192

0

0.021097

0.237942

311

16

38

19.4375

0.835443

0

1

0.2

false

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0.2

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0.6

0

null

1

0

null

0

1

0

3

8aca0af3be9ee2ea88050772027c439546656c4a

3,651

py

Python

tests/test_EdiblesSpectrum.py

jancami/edibles

51263b24c5e8aef786692011289b906a810ad2f7

[ "MIT" ]

8

2020-04-15T10:44:48.000Z

2021-06-21T15:58:19.000Z

tests/test_EdiblesSpectrum.py

jancami/edibles

51263b24c5e8aef786692011289b906a810ad2f7

[ "MIT" ]

100

2020-05-08T13:20:41.000Z

2022-01-11T20:04:52.000Z

tests/test_EdiblesSpectrum.py

jancami/edibles

51263b24c5e8aef786692011289b906a810ad2f7

[ "MIT" ]

8

2020-05-27T00:39:39.000Z

2021-06-23T14:07:16.000Z

import astropy import datetime import numpy as np from edibles.utils.edibles_spectrum import EdiblesSpectrum def testEdiblesSpectrum(filename="tests/HD170740_w860_redl_20140915_O12.fits"): # Spectrum information sp = EdiblesSpectrum(filename=filename, fully_featured=True, noDATADIR=True) assert isinstance(sp.header, astropy.io.fits.header.Header) assert isinstance(sp.target, str) assert isinstance(sp.date, str) assert isinstance(sp.datetime, datetime.datetime) assert isinstance(sp.v_bary, float) assert isinstance(sp.wave_units, str) assert isinstance(sp.flux_units, str) # Raw assert isinstance(sp.raw_wave, np.ndarray) assert isinstance(sp.raw_bary_wave, np.ndarray) assert isinstance(sp.raw_flux, np.ndarray) assert len(sp.raw_wave) == len(sp.raw_bary_wave) assert len(sp.raw_wave) == len(sp.raw_flux) assert isinstance(sp.raw_grid, np.ndarray) assert len(sp.raw_grid) == 200443 # print(len(sp.raw_grid)) assert isinstance(sp.raw_sky_wave, np.ndarray) assert isinstance(sp.raw_sky_flux, np.ndarray) assert len(sp.raw_sky_wave) == len(sp.raw_sky_flux) assert isinstance(sp.wave, np.ndarray) assert isinstance(sp.bary_wave, np.ndarray) assert isinstance(sp.flux, np.ndarray) # getSpectrum xmin = 7660 xmax = 7680 sp.getSpectrum(xmin=xmin, xmax=xmax) assert xmin == sp.xmin assert xmax == sp.xmax assert isinstance(sp.wave, np.ndarray) assert isinstance(sp.flux, np.ndarray) assert len(sp.wave) == len(sp.flux) assert np.min(sp.wave) > sp.xmin assert np.max(sp.wave) < sp.xmax assert isinstance(sp.bary_wave, np.ndarray) assert isinstance(sp.bary_flux, np.ndarray) assert len(sp.bary_wave) == len(sp.bary_flux) assert np.min(sp.bary_wave) > sp.xmin assert np.max(sp.bary_wave) < sp.xmax assert isinstance(sp.grid, np.ndarray) assert isinstance(sp.interp_flux, np.ndarray) assert isinstance(sp.interp_bary_flux, np.ndarray) assert len(sp.grid) == len(sp.interp_flux) assert len(sp.grid) == len(sp.interp_bary_flux) assert np.min(sp.grid) > sp.xmin assert np.max(sp.grid) < sp.xmax assert isinstance(sp.sky_wave, np.ndarray) assert isinstance(sp.sky_flux, np.ndarray) assert len(sp.sky_wave) == len(sp.sky_flux) assert np.min(sp.sky_wave) > sp.xmin assert np.max(sp.sky_wave) < sp.xmax # shift zoom_xmin = 7661 zoom_xmax = 7679 shift = 0.05 sp.shift(shift=shift, zoom_xmin=zoom_xmin, zoom_xmax=zoom_xmax) assert isinstance(sp.wave, np.ndarray) assert isinstance(sp.flux, np.ndarray) assert len(sp.wave) == len(sp.flux) assert np.min(sp.wave) > sp.xmin assert np.max(sp.wave) < sp.xmax assert isinstance(sp.bary_wave, np.ndarray) assert isinstance(sp.bary_flux, np.ndarray) assert len(sp.bary_wave) == len(sp.bary_flux) assert np.min(sp.bary_wave) > sp.xmin assert np.max(sp.bary_wave) < sp.xmax assert isinstance(sp.grid, np.ndarray) assert isinstance(sp.interp_flux, np.ndarray) assert isinstance(sp.interp_bary_flux, np.ndarray) assert len(sp.grid) == len(sp.interp_flux) assert len(sp.grid) == len(sp.interp_bary_flux) assert np.min(sp.grid) > sp.xmin assert np.max(sp.grid) < sp.xmax assert isinstance(sp.sky_wave, np.ndarray) assert isinstance(sp.sky_flux, np.ndarray) assert len(sp.sky_wave) == len(sp.sky_flux) assert np.min(sp.sky_wave) > sp.xmin assert np.max(sp.sky_wave) < sp.xmax if __name__ == "__main__": filename = "HD170740_w860_redl_20140915_O12.fits" testEdiblesSpectrum(filename=filename)

34.443396

80

0.707751

565

3,651

4.424779

0.113274

0.2176

0.2448

0.15

0.6924

0.6832

0.6492

0.5884

0.5676

0.55

0

0.02084

0.172008

3,651

105

81

34.771429

0.806153

0.018077

0

0.573171

0

0.024029

0.021794

0

0.804878

1

0.012195

false

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0.04878

0

0.060976

0

null

1

0

null

0

1

0

3

76e2fbbb9481d029109c5c955ed7a3309fc9c83a

117

py

Python

extract.py

rmalav15/voice-data-extract

e021428afe2706cae0e5339e96bba7f8b033117d

[ "MIT" ]

null

extract.py

rmalav15/voice-data-extract

e021428afe2706cae0e5339e96bba7f8b033117d

[ "MIT" ]

null

extract.py

rmalav15/voice-data-extract

e021428afe2706cae0e5339e96bba7f8b033117d

[ "MIT" ]

null

from srtvoiceext import extract if __name__ == '__main__': ext = extract('video.mkv', 'subtitles.srt', 'outdir')

29.25

57

0.700855

14

117

5.285714

0.928571

0

0.145299

117

4

57

29.25

0.74

0

0.305085

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

76e301801e70d562cc3a1d9777a610e89dc8d94b

632

py

Python

bacon/readonly_collections.py

aholkner/bacon

edf3810dcb211942d392a8637945871399b0650d

[ "MIT" ]

37

2015-01-29T17:42:11.000Z

2021-12-14T22:11:33.000Z

bacon/readonly_collections.py

aholkner/bacon

edf3810dcb211942d392a8637945871399b0650d

[ "MIT" ]

3

2015-08-13T17:38:05.000Z

2020-09-25T17:21:31.000Z

bacon/readonly_collections.py

aholkner/bacon

edf3810dcb211942d392a8637945871399b0650d

[ "MIT" ]

7

2015-02-12T17:54:35.000Z

2022-01-31T14:50:09.000Z

import collections class ReadOnlyDict(collections.MutableMapping): def __init__(self, store): self.store = store def __getitem__(self, key): return self.store[key] def __setitem__(self, key, value): raise TypeError('Cannot modify ReadOnlyDict') def __delitem__(self, key): raise TypeError('Cannot modify ReadOnlyDict') def __iter__(self): return iter(self.store) def __len__(self): return len(self.store) def __str__(self): return 'ReadOnlyDict(%s)' % self.store def __repr__(self): return 'ReadOnlyDict(%r)' % self.store

25.28

53

0.642405

70

632

5.342857

0.357143

0.168449

0.096257

0.139037

0.219251

0

0.253165

632

25

54

25.28

0.792373

0

0.111111

0

0.132701

0

1

0.444444

false

0

0.055556

0.277778

0.833333

0

null

0

null

0

1

0

1

0

3

0a0c72972354861b109e6305d555a377963ca24f

63

py

Python

python/testData/stubs/FullyQualifiedTypingNamedTuple.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/stubs/FullyQualifiedTypingNamedTuple.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/stubs/FullyQualifiedTypingNamedTuple.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import typing nt = typing.NamedTuple("name", [("field", str)])

21

48

0.666667

8

63

5.25

0.875

0

0.111111

63

3

48

21

0.75

0

0.140625

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

0a1e494933ae306f17bb20205df33acd66dcd6cb

3,713

py

Python

src/genotypes.py

k8lion/admmdarts

4953e401cb74ba9f8da3ed0b9d4c5e88da9fc776

[ "Apache-2.0" ]

null

src/genotypes.py

k8lion/admmdarts

4953e401cb74ba9f8da3ed0b9d4c5e88da9fc776

[ "Apache-2.0" ]

null

src/genotypes.py

k8lion/admmdarts

4953e401cb74ba9f8da3ed0b9d4c5e88da9fc776

[ "Apache-2.0" ]

null

from collections import namedtuple Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') PRIMITIVES = [ 'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] CRBPRIMITIVES = [ 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5' ] NASNet = Genotype( normal=[ ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 1), ], normal_concat=[2, 3, 4, 5, 6], reduce=[ ('sep_conv_5x5', 1), ('sep_conv_7x7', 0), ('max_pool_3x3', 1), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('sep_conv_5x5', 0), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 1), ], reduce_concat=[4, 5, 6], ) AmoebaNet = Genotype( normal=[ ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 2), ('sep_conv_3x3', 0), ('avg_pool_3x3', 3), ('sep_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ], normal_concat=[4, 5, 6], reduce=[ ('avg_pool_3x3', 0), ('sep_conv_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_7x7', 2), ('sep_conv_7x7', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('conv_7x1_1x7', 0), ('sep_conv_3x3', 5), ], reduce_concat=[3, 4, 6] ) DARTS_V1 = Genotype( normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('avg_pool_3x3', 0)], reduce_concat=[2, 3, 4, 5]) DARTS_V2 = Genotype( normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0), ('skip_connect', 0), ('dil_conv_3x3', 2)], normal_concat=[2, 3, 4, 5], reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2), ('skip_connect', 2), ('max_pool_3x3', 1)], reduce_concat=[2, 3, 4, 5]) DARTS = DARTS_V2 BATH = Genotype( normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 2), ('dil_conv_5x5', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 2), ('sep_conv_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 2), ('skip_connect', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('dil_conv_5x5', 1)], reduce_concat=range(2, 6)) BATH2 = Genotype( normal=[('max_pool_3x3', 1), ('skip_connect', 0), ('skip_connect', 2), ('max_pool_3x3', 1), ('skip_connect', 1), ('skip_connect', 2), ('max_pool_3x3', 1), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 0), ('dil_conv_3x3', 1), ('skip_connect', 1), ('skip_connect', 0), ('dil_conv_5x5', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2, 6))

34.700935

116

0.546458

543

3,713

3.3186

0.071823

0.170921

0.166482

0.091565

0.831299

0.772475

0.718091

0.54939

0.45283

0.398446

0

0.116984

0.235659

3,713

106

117

35.028302

0.51797

0

0.55102

0

0.395637

0

1

0

false

0

0.010204

0

0.010204

0

null

0

1

0

null

0

3

0a1ed95ecf3a94b0314f7b8f523edacf4c486e8a

275

py

Python

pyccel/ast/basic.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

pyccel/ast/basic.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

pyccel/ast/basic.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

from sympy.core.basic import Basic as sp_Basic class Basic(sp_Basic): """Basic class for Pyccel AST.""" _fst = None def set_fst(self, fst): """Sets the redbaron fst.""" self._fst = fst @property def fst(self): return self._fst

18.333333

46

0.6

39

275

4.076923

0.538462

0.132075

0.125786

0

0.287273

275

14

47

19.642857

0.811224

0.181818

0

1

0.25

false

0

0.125

0.75

0

null

0

null

0

1

0

1

0

3

0a448d09286de882fe626777f47593a108a44caa

628

py

Python

test_app/models.py

alissonmuller/django-group-by

645c36ad2c3ab1f4691de6fcc04fed8b5d7ef78d

[ "MIT" ]

25

2016-09-29T15:25:16.000Z

2021-09-19T14:20:58.000Z

test_app/models.py

alissonmuller/django-group-by

645c36ad2c3ab1f4691de6fcc04fed8b5d7ef78d

[ "MIT" ]

22

2016-05-29T00:14:47.000Z

2019-06-08T13:24:21.000Z

test_app/models.py

alissonmuller/django-group-by

645c36ad2c3ab1f4691de6fcc04fed8b5d7ef78d

[ "MIT" ]

2

2018-09-24T07:28:39.000Z

2019-02-12T14:09:18.000Z

from django.db import models from .query import BookQuerySet class Book(models.Model): objects = BookQuerySet.as_manager() title = models.CharField(max_length=50) publication_date = models.DateTimeField() author = models.ForeignKey('Author') genres = models.ManyToManyField('Genre') class Author(models.Model): name = models.CharField(max_length=50) nationality = models.ForeignKey('Nation', null=True) class Genre(models.Model): name = models.CharField(max_length=50) class Nation(models.Model): name = models.CharField(max_length=50) demonym = models.CharField(max_length=50)

23.259259

56

0.732484

77

628

5.883117

0.415584

0.165563

0.198676

0.264901

0.386313

0.271523

0

0.018832

0.154459

628

26

57

24.153846

0.834275

0

0.1875

0

0.02707

0

1

0

false

0

0.125

0

1

0

null

0

1

0

null

0

1

0

3

0a515a3d5abf09db1a4745bebd807a1a69030c04

219

py

Python

Introductions/The Rust Programming Language/embed/bindings/embed.py

uqtimes/Rust-SampleCodes

f9d7a040d8198acd30bf3423e7c6cf52bc9c7b6e

[ "MIT" ]

null

Introductions/The Rust Programming Language/embed/bindings/embed.py

uqtimes/Rust-SampleCodes

f9d7a040d8198acd30bf3423e7c6cf52bc9c7b6e

[ "MIT" ]

null

Introductions/The Rust Programming Language/embed/bindings/embed.py

uqtimes/Rust-SampleCodes

f9d7a040d8198acd30bf3423e7c6cf52bc9c7b6e

[ "MIT" ]

null

# $ python embed.py from ctypes import cdll lib = cdll.LoadLibrary("../target/release/libembed.dylib") #=> for Mac #lib = cdll.LoadLibrary("../target/release/libembed.so") #=> for Linux lib.process() print("done!")

19.909091

70

0.689498

29

219

5.206897

0.689655

0.092715

0.238411

0.317881

0.516556

0

0.118721

219

10

71

21.9

0.782383

0.43379

0

0.310924

0.268908

0

1

0

false

0

0.25

0

0.25

1

0

null

0

1

0

null

0

3

0a58933890bb698e85d3cfefe359ee1effd69d83

1,050

py

Python

models/node.py

AlonsoReyes/t-intersection-graph

68bab234cd6e334edcec27bfee3e019f08997945

[ "MIT" ]

null

models/node.py

AlonsoReyes/t-intersection-graph

68bab234cd6e334edcec27bfee3e019f08997945

[ "MIT" ]

null

models/node.py

AlonsoReyes/t-intersection-graph

68bab234cd6e334edcec27bfee3e019f08997945

[ "MIT" ]

null

class Node(object): def __init__(self, name, follow_list, intention, lane): self.name = name self.follow_list = follow_list self.intention = intention self.lane = lane def __eq__(self, other): if isinstance(other, Node): if self.name == other.get_name() and self.follow_list == other.get_follow_list() \ and self.intention == other.get_intention() and self.lane == other.get_lane(): return True return False def get_name(self): return self.name def set_name(self, name): self.name = name def get_follow_list(self): return self.follow_list def set_follow_list(self, follow_list): self.follow_list = follow_list def get_intention(self): return self.intention def set_intention(self, intention): self.intention = intention def get_lane(self): return self.lane def set_lane(self, lane): self.lane = lane

26.25

99

0.591429

129

1,050

4.573643

0.162791

0.186441

0.118644

0.067797

0.122034

0

0.320952

1,050

39

100

26.923077

0.827489

0

0.285714

0

1

0.357143

false

0

0.142857

0.607143

0

null

0

null

0

1

0

1

0

3

0a5f2c5e88f319fb43560833894661a1abbe9435

1,934

py

Python

pcat2py/class/20bdcef0-5cc5-11e4-af55-00155d01fe08.py

phnomcobra/PCAT2PY

937c3b365cdc5ac69b78f59070be0a21bdb53db0

[ "MIT" ]

null

pcat2py/class/20bdcef0-5cc5-11e4-af55-00155d01fe08.py

phnomcobra/PCAT2PY

937c3b365cdc5ac69b78f59070be0a21bdb53db0

[ "MIT" ]

null

pcat2py/class/20bdcef0-5cc5-11e4-af55-00155d01fe08.py

phnomcobra/PCAT2PY

937c3b365cdc5ac69b78f59070be0a21bdb53db0

[ "MIT" ]

null

#!/usr/bin/python ################################################################################ # 20bdcef0-5cc5-11e4-af55-00155d01fe08 # # Justin Dierking # [email protected] # [email protected] # # 10/24/2014 Original Construction ################################################################################ class Finding: def __init__(self): self.output = [] self.is_compliant = False self.uuid = "20bdcef0-5cc5-11e4-af55-00155d01fe08" def check(self, cli): # Initialize Compliance self.is_compliant = True # Get Registry MultiSZ multi_sz = cli.get_reg_multi_sz(r'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers\winreg\allowedExactPaths', 'Machine') # Output Lines self.output = [r'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers\winreg\allowedExactPaths', ('Machine=')] + multi_sz # Recommended MultiSZ rec_multi_sz = ("System\CurrentControlSet\Control\ProductOptions,System\CurrentControlSet\Control\Server Applications,Software\Microsoft\Windows NT\CurrentVersion") for sz in multi_sz: if sz.lower() not in rec_multi_sz.lower(): self.is_compliant = False return self.is_compliant def fix(self, cli): cli.powershell(r"New-Item -path 'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers'") cli.powershell(r"New-Item -path 'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers\winreg'") cli.powershell(r"New-Item -path 'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers\winreg\allowedExactPaths'") cli.powershell(r"Set-ItemProperty -path 'HKLM:\SYSTEM\CurrentControlSet\control\SecurePipeServers\winreg\allowedExactPaths' -name 'Machine' -Type MultiString -value System\CurrentControlSet\Control\ProductOptions,System\CurrentControlSet\Control\Server Applications,Software\Microsoft\Windows NT\CurrentVersion")

46.047619

320

0.682006

196

1,934

6.637755

0.397959

0.176787

0.230592

0.156802

0.635665

0.586472

0.513451

0.387394

0

0.027496

0.134953

1,934

41

321

47.170732

0.750149

0.122027

0

0.105263

0

0.105263

0.591088

0.484273

0

null

0

null

0

null

0

1

0

1

null

0

1

0

3

0a7052f7029ee061d74d603abefe9574ef7b3461

114

py

Python

DLA/__main__.py

StanczakDominik/DLA

bf63592a5ac96ffef639e7a0c80d7d52ff776322

[ "MIT" ]

null

DLA/__main__.py

StanczakDominik/DLA

bf63592a5ac96ffef639e7a0c80d7d52ff776322

[ "MIT" ]

null

DLA/__main__.py

StanczakDominik/DLA

bf63592a5ac96ffef639e7a0c80d7d52ff776322

[ "MIT" ]

null

from DLA import main_single d = main_single(1, gotosize=[1e4, 5e4]) d.plot_particles() d.plot_mass_distribution()

22.8

39

0.780702

19

114

4.421053

0.736842

0.238095

0

0.048544

0.096491

114

4

40

28.5

0.76699

0

1

0

false

0

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

3

6a5f51cf2ae3a67fb99172b7bd4214f43d0d42bc

269

py

Python

python/ordenacao.py

valdirsjr/learning.data

a4b72dfd27f55f2f04120644b73232bf343f71e3

[ "MIT" ]

null

python/ordenacao.py

valdirsjr/learning.data

a4b72dfd27f55f2f04120644b73232bf343f71e3

[ "MIT" ]

null

python/ordenacao.py

valdirsjr/learning.data

a4b72dfd27f55f2f04120644b73232bf343f71e3

[ "MIT" ]

null

numero1 = int(input("Digite o primeiro número: ")) numero2 = int(input("Digite o segundo número: ")) numero3 = int(input("Digite o terceiro número: ")) if (numero1 < numero2 and numero2 < numero3): print("crescente") else: print("não está em ordem crescente")

38.428571

50

0.69145

36

269

5.166667

0.555556

0.129032

0.225806

0.241935

0

0.03125

0.167286

269

7

51

38.428571

0.799107

0

0.418519

0

1

0

false

0

0.285714

0

null

0

1

0

1

0

null

0

3

6a60999063f76386f01b79b85ecc655ec0929c57

25,232

py

Python

csld/phonon/head.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

null

csld/phonon/head.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

null

csld/phonon/head.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

null

# to include all module here in order to cite from numpy import * from numpy.linalg import * import string import os import scipy import scipy.sparse #import rwposcar #import anaxdat import math #define touch file def touch(file):#input string if os.path.isfile(file): os.system(str("rm"+" "+file)) os.system(str("touch"+" "+file)) else: os.system(str("touch"+" "+file)) def mkdir(dir): if os.path.isdir(dir): os.system(str("rm"+" -r "+dir)) os.system(str("mkdir"+" "+dir)) else: os.system(str("mkdir"+" "+dir)) if False: mkdir("xixi/") #define rm file def rm(file): if os.path.isfile(file): os.system(str("rm"+" "+file)) else: print("No file found, dont need to rm") #define check file(1 exist; else0) def check(file): if os.path.isfile(file): return int(1) else: return int(0) #define check the file status (print the status) def checkfile(file): if os.path.isfile(file): print(str(file)+" exists :)") else: print(str(file)+" not found :(") #define readallline function def readinline(file): dataout=[] if check(file): fin=open(file,"r") for line in fin: dataout.append(line.split())#map(float,line.split())) fin.close() else: print(str(file)+" not found :(") return array(dataout) #define write1dmat def write1dmat(datain, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file, "w") fout.writelines("\n".join(map(str,datain))) fout.close() #define one number to file def writenumber(datain, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file,"w") fout.writelines(str(datain)) fout.close() #define write2dmat def write2dmat(datain, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file, "w") #cout line number fout.writelines(str(len(datain))+"\n") for i in datain: fout.writelines(" ".join(map(str,i))+"\n") fout.close() #define write2dMTX def write2dMTX(datain, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file, "w") fout.writelines("%%MatrixMarket matrix coordinate real general\n") fout.writelines("%Created by Wolfram Mathematica 9.0 : www.wolfram.com\n") print("Transfering to sparse matrix----") #get rid of small numbers #for i in range(len(datain)): # for j in range(len(datain[i])): # datain[i][j]=round(datain[i][j],3) BB=scipy.sparse.coo_matrix(datain) print("Spare matrix obtained!") # print BB.row # print BB.col # print BB.data fout.writelines(str(len(datain))+" "+str(len(datain[0]))+" "+str(len(BB.data))+"\n") for i in range(len(BB.data)): fout.writelines(str(BB.row[i]+1)+" "+str(BB.col[i]+1)+" "+str(BB.data[i])+"\n") #for i in range(len(datain)): #for j in range(len(datain[0])): #fout.writelines(str(i+1)+" "+str(j+1)+" "+str(datain[i][j])+"\n") fout.close() def read2dMTX(file): if check(file): counter=0 for line in open(file): counter=counter+1 if counter <=2: continue if counter ==3: inlist=list(map(int,line.split())) nrow=inlist[0] ncol=inlist[1] dataout=array([[0.0]*ncol]*nrow) continue if counter >=4: tmp=line.split() #print str(tmp)+", "+str(tmp[2]) dataout[int(tmp[0])-1][int(tmp[1])-1]=float(tmp[2]) #print "\n" return dataout.tolist() else: print(str(file)+" not found :(") #test if False: Amat=[[0,1],[2,0],[0,0],[0,16]] print(Amat) write2dMTX(Amat, "test.mtx") print(read2dMTX("test.mtx")) #define read1dmat #read float def read1dmat(file): mat=[] if check(file): for line in open(file): mat.append(float(line)) return mat else: print(str(file)+" not found :(") if False: haha=[1,2,3,4,5] write1dmat(haha, "haha") xixi=read1dmat("haha") print(xixi) #define read2dmat (this is a relatively fast way: iter or chunck read) def read2dmat(file,icomplex=False): mat=[] if check(file): print("Read matrix start") for line in open(file): if not icomplex: mat.append(list(map(float,line.split()))) else: mat.append(list(map(complex,line.split()))) print("Read matrix end") #delete line counter del mat[0] return mat else: print(str(file)+" not found :(") #test #mat=read2dmat("C-isoo.mat") #print len(mat) #print len(mat[0]) def clusstr(clus): dataout="" for item in clus: dataout=dataout+str(item[0])+" "+str(item[1])+" "+str(item[2])+"\n" return dataout def lptstr(lpt): dataout="" for item in lpt: dataout=dataout+str(item[0][0])+" "+str(item[0][1])+" "+str(item[0][2])+" "+str(item[1])+"\n" return dataout #define writeorb(orb) def writeorb(orbset, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file, "w") fout.write(str(len(orbset))+"\n\n") for orb in orbset: fout.write(str(len(orb))+"\n\n") for item in orb: npt=len(item[0]) fout.write(str(npt)+"\n") fout.write(clusstr(item[0])) fout.write(str(item[1])+"\n") fout.write(str(item[2])+"\n") fout.write(lptstr(item[3])) fout.write("\n") fout.close() def writeclus(clus, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file,"w") fout.write(str(len(clus))+"\n\n") for item in clus: fout.write(str(len(item))+"\n") fout.write(clusstr(item)) fout.write("\n") fout.close() def writeSCinfo(SCinfo, file): if check(file): rm(file) touch(file) else: touch(file) fout=open(file, "w") tmp=[SCinfo['SC'], SCinfo['invSC'], SCinfo['SCref'], SCinfo['SCpos'], SCinfo['SCmat'], SCinfo['invSCmat'], SCinfo['order']] lentmp=[len(i) for i in tmp] fout.write(" ".join(map(str,lentmp))+"\n") for i in tmp: if i==SCinfo['order']: fout.write("\n".join(map(str,i))+"\n") else: for j in i: fout.write(" ".join(map(str,j))+"\n") fout.close() def readSCinfo(file): SCinfo={} if check(file): fin=open(file, "r") lenlist=list(map(int,(fin.readline()).split())) # tmp=[SCinfo['SC'], SCinfo['invSC'], SCinfo['SCref'], SCinfo['SCpos'], SCinfo['SCmat'], SCinfo['invSCmat'], SCinfo['order']] tmp=[] for i in range(7): tmp1=[] for j in range(lenlist[i]): if i in [0,1,3,4,5]: tmp1.append(list(map(float,(fin.readline()).split()))) elif i in [2]: tmp1.append(list(map(int,(fin.readline()).split()))) else: tmp1.append(list(map(int,(fin.readline()).split()))[0]) tmp.append(tmp1) SCinfo['SC']=tmp[0] SCinfo['invSC']=tmp[1] SCinfo['SCref']=tmp[2] SCinfo['SCpos']=tmp[3] SCinfo['SCmat']=tmp[4] SCinfo['invSCmat']=tmp[5] SCinfo['order']=tmp[6] else: print(str(file)+" not found :(") return SCinfo #test if False: SCinfo={'invSCmat': [[-0.25, 0.25, 0.25], [0.25, -0.25, 0.25], [0.25, 0.25, -0.25]], 'SCmat': [[0.0, 2.0, 2.0], [2.0, 0.0, 2.0], [2.0, 2.0, 0.0]], 'SCref': [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], [1, 1, 1], [1, 1, 2], [1, 2, 1], [1, 2, 2], [2, 1, 1], [2, 1, 2], [2, 2, 1], [2, 2, 2], [2, 2, 3], [2, 3, 2], [3, 2, 2], [3, 3, 3]], 'SCpos': [[0.75, 0.25, 0.5], [0.25, 0.75, 0.5], [0.5, 0.25, 0.75], [0.5, 0.75, 0.25], [0.25, 0.5, 0.75], [0.75, 0.5, 0.25], [0.785, 0.785, 0.0], [0.215, 0.215, 0.0], [0.0, 0.215, 0.215], [0.0, 0.785, 0.785], [0.785, 0.0, 0.785], [0.215, 0.0, 0.215], [0.5239, 0.0, 0.7543], [0.7543, 0.0, 0.5239], [0.4761, 0.2304, 0.4761], [0.2457, 0.7696, 0.2457], [0.5239, 0.7543, 0.0], [0.7543, 0.5239, 0.0], [0.2457, 0.2457, 0.7696], [0.4761, 0.4761, 0.2304], [0.7696, 0.2457, 0.2457], [0.2304, 0.4761, 0.4761], [0.0, 0.5239, 0.7543], [0.0, 0.7543, 0.5239], [0.0, 0.0, 0.0], [0.4636, 0.0, 0.0], [0.0, 0.0, 0.4636], [0.5364, 0.5364, 0.5364], [0.0, 0.4636, 0.0], [0.75, 1.25, 1.5], [0.25, 1.75, 1.5], [0.5, 1.25, 1.75], [0.5, 1.75, 1.25], [0.25, 1.5, 1.75], [0.75, 1.5, 1.25], [0.785, 1.785, 1.0], [0.215, 1.215, 1.0], [0.0, 1.215, 1.215], [0.0, 1.785, 1.785], [0.785, 1.0, 1.785], [0.215, 1.0, 1.215], [0.5239, 1.0, 1.7543], [0.7543, 1.0, 1.5239], [0.4761, 1.2304, 1.4761], [0.2457, 1.7696, 1.2457], [0.5239, 1.7543, 1.0], [0.7543, 1.5239, 1.0], [0.2457, 1.2457, 1.7696], [0.4761, 1.4761, 1.2304], [0.7696, 1.2457, 1.2457], [0.2304, 1.4761, 1.4761], [0.0, 1.5239, 1.7543], [0.0, 1.7543, 1.5239], [0.0, 1.0, 1.0], [0.4636, 1.0, 1.0], [0.0, 1.0, 1.4636], [0.5364, 1.5364, 1.5364], [0.0, 1.4636, 1.0], [1.75, 0.25, 1.5], [1.25, 0.75, 1.5], [1.5, 0.25, 1.75], [1.5, 0.75, 1.25], [1.25, 0.5, 1.75], [1.75, 0.5, 1.25], [1.785, 0.785, 1.0], [1.215, 0.215, 1.0], [1.0, 0.215, 1.215], [1.0, 0.785, 1.785], [1.785, 0.0, 1.785], [1.215, 0.0, 1.215], [1.5239, 0.0, 1.7543], [1.7543, 0.0, 1.5239], [1.4761, 0.2304, 1.4761], [1.2457, 0.7696, 1.2457], [1.5239, 0.7543, 1.0], [1.7543, 0.5239, 1.0], [1.2457, 0.2457, 1.7696], [1.4761, 0.4761, 1.2304], [1.7696, 0.2457, 1.2457], [1.2304, 0.4761, 1.4761], [1.0, 0.5239, 1.7543], [1.0, 0.7543, 1.5239], [1.0, 0.0, 1.0], [1.4636, 0.0, 1.0], [1.0, 0.0, 1.4636], [1.5364, 0.5364, 1.5364], [1.0, 0.4636, 1.0], [1.75, 1.25, 0.5], [1.25, 1.75, 0.5], [1.5, 1.25, 0.75], [1.5, 1.75, 0.25], [1.25, 1.5, 0.75], [1.75, 1.5, 0.25], [1.785, 1.785, 0.0], [1.215, 1.215, 0.0], [1.0, 1.215, 0.215], [1.0, 1.785, 0.785], [1.785, 1.0, 0.785], [1.215, 1.0, 0.215], [1.5239, 1.0, 0.7543], [1.7543, 1.0, 0.5239], [1.4761, 1.2304, 0.4761], [1.2457, 1.7696, 0.2457], [1.5239, 1.7543, 0.0], [1.7543, 1.5239, 0.0], [1.2457, 1.2457, 0.7696], [1.4761, 1.4761, 0.2304], [1.7696, 1.2457, 0.2457], [1.2304, 1.4761, 0.4761], [1.0, 1.5239, 0.7543], [1.0, 1.7543, 0.5239], [1.0, 1.0, 0.0], [1.4636, 1.0, 0.0], [1.0, 1.0, 0.4636], [1.5364, 1.5364, 0.5364], [1.0, 1.4636, 0.0], [1.75, 1.25, 1.5], [1.25, 1.75, 1.5], [1.5, 1.25, 1.75], [1.5, 1.75, 1.25], [1.25, 1.5, 1.75], [1.75, 1.5, 1.25], [1.785, 1.785, 1.0], [1.215, 1.215, 1.0], [1.0, 1.215, 1.215], [1.0, 1.785, 1.785], [1.785, 1.0, 1.785], [1.215, 1.0, 1.215], [1.5239, 1.0, 1.7543], [1.7543, 1.0, 1.5239], [1.4761, 1.2304, 1.4761], [1.2457, 1.7696, 1.2457], [1.5239, 1.7543, 1.0], [1.7543, 1.5239, 1.0], [1.2457, 1.2457, 1.7696], [1.4761, 1.4761, 1.2304], [1.7696, 1.2457, 1.2457], [1.2304, 1.4761, 1.4761], [1.0, 1.5239, 1.7543], [1.0, 1.7543, 1.5239], [1.0, 1.0, 1.0], [1.4636, 1.0, 1.0], [1.0, 1.0, 1.4636], [1.5364, 1.5364, 1.5364], [1.0, 1.4636, 1.0], [1.75, 1.25, 2.5], [1.25, 1.75, 2.5], [1.5, 1.25, 2.75], [1.5, 1.75, 2.25], [1.25, 1.5, 2.75], [1.75, 1.5, 2.25], [1.785, 1.785, 2.0], [1.215, 1.215, 2.0], [1.0, 1.215, 2.215], [1.0, 1.785, 2.785], [1.785, 1.0, 2.785], [1.215, 1.0, 2.215], [1.5239, 1.0, 2.7543], [1.7543, 1.0, 2.5239], [1.4761, 1.2304, 2.4761], 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2.0], [3.0, 2.0, 2.4636], [3.5364, 2.5364, 2.5364], [3.0, 2.4636, 2.0], [3.75, 3.25, 3.5], [3.25, 3.75, 3.5], [3.5, 3.25, 3.75], [3.5, 3.75, 3.25], [3.25, 3.5, 3.75], [3.75, 3.5, 3.25], [3.785, 3.785, 3.0], [3.215, 3.215, 3.0], [3.0, 3.215, 3.215], [3.0, 3.785, 3.785], [3.785, 3.0, 3.785], [3.215, 3.0, 3.215], [3.5239, 3.0, 3.7543], [3.7543, 3.0, 3.5239], [3.4761, 3.2304, 3.4761], [3.2457, 3.7696, 3.2457], [3.5239, 3.7543, 3.0], [3.7543, 3.5239, 3.0], [3.2457, 3.2457, 3.7696], [3.4761, 3.4761, 3.2304], [3.7696, 3.2457, 3.2457], [3.2304, 3.4761, 3.4761], [3.0, 3.5239, 3.7543], [3.0, 3.7543, 3.5239], [3.0, 3.0, 3.0], [3.4636, 3.0, 3.0], [3.0, 3.0, 3.4636], [3.5364, 3.5364, 3.5364], [3.0, 3.4636, 3.0]], 'SC': [[2.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 2.0]], 'order': [81, 33, 1, 65, 49, 17, 137, 129, 97, 105, 121, 113, 274, 285, 257, 363, 219, 213, 298, 193, 333, 225, 250, 243, 385, 461, 442, 401, 451, 85, 37, 5, 69, 53, 21, 141, 133, 101, 109, 125, 117, 278, 281, 261, 367, 223, 209, 302, 197, 329, 229, 254, 247, 389, 457, 446, 405, 455, 83, 35, 3, 67, 51, 19, 139, 131, 99, 107, 123, 115, 276, 287, 259, 361, 217, 215, 300, 195, 335, 227, 252, 241, 387, 463, 444, 403, 449, 82, 34, 2, 66, 50, 18, 138, 130, 98, 106, 122, 114, 273, 286, 258, 364, 220, 214, 297, 194, 334, 226, 249, 244, 386, 462, 441, 402, 452, 43, 93, 75, 10, 29, 58, 186, 177, 145, 157, 171, 161, 371, 379, 353, 265, 314, 306, 201, 289, 233, 321, 349, 341, 393, 425, 409, 433, 417, 87, 39, 7, 71, 55, 23, 143, 135, 103, 111, 127, 119, 280, 283, 263, 365, 221, 211, 304, 199, 331, 231, 256, 245, 391, 459, 448, 407, 453, 86, 38, 6, 70, 54, 22, 142, 134, 102, 110, 126, 118, 277, 282, 262, 368, 224, 210, 301, 198, 330, 230, 253, 248, 390, 458, 445, 406, 456, 47, 89, 79, 14, 25, 62, 190, 181, 149, 153, 175, 165, 375, 383, 357, 269, 318, 310, 205, 293, 237, 325, 345, 337, 397, 429, 413, 437, 421, 84, 36, 4, 68, 52, 20, 140, 132, 100, 108, 124, 116, 275, 288, 260, 362, 218, 216, 299, 196, 336, 228, 251, 242, 388, 464, 443, 404, 450, 41, 95, 73, 12, 31, 60, 188, 179, 147, 159, 169, 163, 369, 377, 355, 267, 316, 308, 203, 291, 235, 323, 351, 343, 395, 427, 411, 435, 419, 44, 94, 76, 9, 30, 57, 185, 178, 146, 158, 172, 162, 372, 380, 354, 266, 313, 305, 202, 290, 234, 322, 350, 342, 394, 426, 410, 434, 418, 88, 40, 8, 72, 56, 24, 144, 136, 104, 112, 128, 120, 279, 284, 264, 366, 222, 212, 303, 200, 332, 232, 255, 246, 392, 460, 447, 408, 454, 45, 91, 77, 16, 27, 64, 192, 183, 151, 155, 173, 167, 373, 381, 359, 271, 320, 312, 207, 295, 239, 327, 347, 339, 399, 431, 415, 439, 423, 48, 90, 80, 13, 26, 61, 189, 182, 150, 154, 176, 166, 376, 384, 358, 270, 317, 309, 206, 294, 238, 326, 346, 338, 398, 430, 414, 438, 422, 42, 96, 74, 11, 32, 59, 187, 180, 148, 160, 170, 164, 370, 378, 356, 268, 315, 307, 204, 292, 236, 324, 352, 344, 396, 428, 412, 436, 420, 46, 92, 78, 15, 28, 63, 191, 184, 152, 156, 174, 168, 374, 382, 360, 272, 319, 311, 208, 296, 240, 328, 348, 340, 400, 432, 416, 440, 424], 'invSC': [[0.5, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 0.5]]} writeSCinfo(SCinfo, "SCinfo") haha=readSCinfo("SCinfo") print(haha['SC']) print(haha['invSC']) print(haha['SCref']) print(haha['SCpos']) print(haha['SCmat']) print(haha['invSCmat']) print(haha['order']) def readclus(file): if check(file): fin=open(file, "r") nclus=int(fin.readline()) clus=[] for i in range(nclus): item=[] fin.readline() npt=int(fin.readline()) for j in range(npt): item.append(list(map(float, fin.readline().split()))) clus.append(item) return clus else: print(str(file)+" not found :(") #writeclus(clus,"uniqueC") #print "\n".join(map(str, readclus("uniqueC"))) def readorb(file): if check(file): orbset=[] fin=open(file, "r") Norb=int(fin.readline()) for i in range(Norb): orb=[] fin.readline() nitem=int(fin.readline()) fin.readline() for j in range(nitem): item=[] npt=int(fin.readline()) clus=[] lpt=[] for k in range(npt): line=fin.readline() clus.append(list(map(float,line.split()))) item.append(clus) item.append(int(fin.readline())) item.append(int(fin.readline())) for k in range(npt): line=fin.readline() tmp=list(map(float,line.split())) tmp=list(map(int, tmp)) lpt.append([[tmp[0],tmp[1],tmp[2]],tmp[3]]) item.append(lpt) orb.append(item) orbset.append(orb) fin.close() return orbset else: print(str(file)+" not found :(") #test if False: orbset=[[[[[0.75, 0.25, 0.5]], 1, 1, [[[0.0, 0.0, 0.0], 1]]], [[[0.75, 0.5, 0.25]], 1, 2, [[[0.0, 0.0, 0.0], 6]]], [[[0.5, 0.25, -0.25]], 1, 3, [[[0.0, 0.0, -1.0], 3]]], [[[0.25, -0.25, -0.5]], 1, 4, [[[0.0, -1.0, -1.0], 2]]], [[[0.5, -0.25, 0.25]], 1, 5, [[[0.0, -1.0, 0.0], 4]]], [[[0.25, -0.5, -0.25]], 1, 6, [[[0.0, -1.0, -1.0], 5]]]],[[[[0.7696, 0.2457, 0.2457], [0.0, -0.215, -0.215]], 42, 1, [[[0.0, 0.0, 0.0], 21], [[0.0, -1.0, -1.0], 10]]], [[[0.5238999999999999, 0.0, -0.2457], [0.215, 0.0, 0.215]], 42, 3, [[[-0.0, 0.0, -1.0], 13], [[0.0, 0.0, 0.0], 12]]], [[[0.5238999999999999, -0.2457, 0.0], [0.215, 0.215, 0.0]], 42, 5, [[[-0.0, -1.0, 0.0], 17], [[0.0, 0.0, 0.0], 8]]], [[[-0.2457, 0.0, 0.5238999999999999], [0.215, 0.0, 0.215]], 42, 7, [[[-1.0, 0.0, -0.0], 14], [[0.0, 0.0, 0.0], 12]]], [[[0.2457, 0.2457, 0.7696], [-0.215, -0.215, 0.0]], 42, 9, [[[0.0, 0.0, 0.0], 19], [[-1.0, -1.0, 0.0], 7]]], [[[0.0, -0.2457, 0.5238999999999999], [0.0, 0.215, 0.215]], 42, 11, [[[0.0, -1.0, -0.0], 24], [[0.0, 0.0, 0.0], 9]]], [[[-0.7696, -0.5238999999999999, -0.5238999999999999], [0.0, -0.215, -0.215]], 42, 13, [[[-1.0, -1.0, -1.0], 22], [[0.0, -1.0, -1.0], 10]]], [[[-0.5238999999999999, -0.5238999999999999, -0.7696], [-0.215, -0.215, 0.0]], 42, 15, [[[-1.0, -1.0, -1.0], 20], [[-1.0, -1.0, 0.0], 7]]], [[[-0.5238999999999999, -0.7696, -0.5238999999999999], [-0.215, 0.0, -0.215]], 42, 17, [[[-1.0, -1.0, -1.0], 15], [[-1.0, 0.0, -1.0], 11]]], [[[-0.2457, 0.5238999999999999, 0.0], [0.215, 0.215, 0.0]], 42, 19, [[[-1.0, -0.0, 0.0], 18], [[0.0, 0.0, 0.0], 8]]], [[[0.2457, 0.7696, 0.2457], [-0.215, 0.0, -0.215]], 42, 21, [[[0.0, 0.0, 0.0], 16], [[-1.0, 0.0, -1.0], 11]]], [[[0.0, 0.5238999999999999, -0.2457], [0.0, 0.215, 0.215]], 42, 23, [[[0.0, -0.0, -1.0], 23], [[0.0, 0.0, 0.0], 9]]]]] print("\n".join(map(str,orbset))) writeorb(orbset,"test-orb") print("\n") print("\n".join(map(str,readorb("test-orb")))) #def read fit.ou def readfit(file): if check(file): counter=0 readflag=False for line in open(file): counter=counter+1 if counter==1: nstruc=list(map(int, line.split()))[1] fitlist=[0.0]*nstruc if len(line.split())>=1 and (line.split())[0]=="found": readflag=True continue if readflag: index=int((line.split())[0]) resl=float((line.split())[1]) fitlist[index-1]=resl print("Fit.our read successfully and length: "+str(len(fitlist))) return fitlist else: print(str(file)+" not found :(") #test: if False: print(readfit("fit.out-mu1"))

65.537662

12,884

0.507411

5,174

25,232

2.474295

0.128334

0.038432

0.03187

0.021872

0.645837

0.383065

0.345649

0.280191

0.259959

0.153648

0

0.372021

0.214965

25,232

384

12,885

65.708333

0.274283

0.044031

0

0.452229

0

0.029573

0

1

0.070064

false

0

0.022293

0

0.130573

0.095541

0

null

0

1

0

1

0

1

0

null

0

3

6a7d44f1e562967fd6fedbdfc2867ad65df6f217

2,163

py

Python

yekpay/migrations/0014_auto_20181120_1453.py

maryam-afzp/django-yekpay

f7b9d7914035ea4f27238eba9e0c70227cc65046

[ "MIT" ]

3

2020-05-17T18:33:22.000Z

2021-12-06T08:31:42.000Z

yekpay/migrations/0014_auto_20181120_1453.py

Glyphack/django-yekpay

8c4a44853207be4ff0b1711c0524fb0201859b19

[ "MIT" ]

null

yekpay/migrations/0014_auto_20181120_1453.py

Glyphack/django-yekpay

8c4a44853207be4ff0b1711c0524fb0201859b19

[ "MIT" ]

4

2019-11-14T14:16:49.000Z

2021-12-06T08:31:44.000Z

# Generated by Django 2.0.9 on 2018-11-20 11:23 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('yekpay', '0013_auto_20181030_1911'), ] operations = [ migrations.RenameField( model_name='transaction', old_name='authorityStart', new_name='authority_start', ), migrations.RenameField( model_name='transaction', old_name='authorityVerify', new_name='authority_verify', ), migrations.RenameField( model_name='transaction', old_name='failureReason', new_name='failure_reason', ), migrations.RenameField( model_name='transaction', old_name='firstName', new_name='first_name', ), migrations.RenameField( model_name='transaction', old_name='fromCurrencyCode', new_name='from_currency_code', ), migrations.RenameField( model_name='transaction', old_name='lastName', new_name='last_name', ), migrations.RenameField( model_name='transaction', old_name='orderNumber', new_name='order_number', ), migrations.RenameField( model_name='transaction', old_name='postalCode', new_name='postal_code', ), migrations.RenameField( model_name='transaction', old_name='toCurrencyCode', new_name='to_currency_code', ), migrations.AddField( model_name='transaction', name='simulation', field=models.BooleanField(default=False), ), migrations.AddField( model_name='transaction', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), ]

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null

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1

0

1

0

null

0

3

6a8199a221f44d9fef4df3ccc6d623b0243a377c

1,058

py

Python

tests/dummies.py

arvindmuralie77/gradsflow

d6ec5bc517dcf714cd4ecb91a7f702dce6bded3f

[ "Apache-2.0" ]

253

2021-08-17T17:42:25.000Z

2022-03-25T07:59:41.000Z

tests/dummies.py

arvindmuralie77/gradsflow

d6ec5bc517dcf714cd4ecb91a7f702dce6bded3f

[ "Apache-2.0" ]

161

2021-08-17T16:28:08.000Z

2022-03-27T02:36:45.000Z

tests/dummies.py

arvindmuralie77/gradsflow

d6ec5bc517dcf714cd4ecb91a7f702dce6bded3f

[ "Apache-2.0" ]

35

2021-08-23T16:26:15.000Z

2022-03-26T17:08:15.000Z

# Copyright (c) 2021 GradsFlow. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from gradsflow.models import Model class DummyModel(Model): def __init__(self): learner = torch.nn.Linear(1, 4) super().__init__(learner) def backward(self, loss: torch.Tensor): return None def train_step(self, batch): return {"loss": torch.as_tensor(1), "metrics": {"accuracy": 1}} def val_step(self, batch): return {"loss": torch.as_tensor(1), "metrics": {"accuracy": 1}}

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null

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0

3

6a8c272cd22c6193695ebfa5fa34ff4d88d4565d

579

py

Python

src/solutions/part2/q104_max_bi_tree_depth.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

src/solutions/part2/q104_max_bi_tree_depth.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

src/solutions/part2/q104_max_bi_tree_depth.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

from src.base.solution import Solution from src.tests.part2.q104_test_max_bi_tree_depth import MaxBiTreeDepthTestCases class MaxBiTreeDepth(Solution): def gen_test_cases(self): return MaxBiTreeDepthTestCases() def run_test(self, input): return self.maxDepth(input) def maxDepth(self, root): """ :type root: TreeNode :rtype: int """ if not root: return 0 return max(self.maxDepth(root.left), self.maxDepth(root.right)) + 1 if __name__ == '__main__': sol = MaxBiTreeDepth() sol.run_tests()

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3

6a921ec9df90e9d0bc4821cbf3d19c03f4f29792

1,882

py

Python

scripts/common/frozendict.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/common/frozendict.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/common/frozendict.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

1

2020-07-23T11:05:06.000Z

# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Implements a frozen dictionary-like object""" import collections import copy import common.memo as memo class frozendict(collections.Mapping): """A frozen dictionary class""" def __init__(self, *args, **kwargs): self._data = dict(*args, **kwargs) def __iter__(self): return iter(self._data) def __len__(self): return len(self._data) def __getitem__(self, key): return self._data[key] @memo.memo_i() def __hash__(self): return hash(self.itemtuple()) def __str__(self): return str(self._data) def __repr__(self): return '%s(%s)' % (type(self).__name__, str(self)) def __eq__(self, other): return self._data == other def __ne__(self, other): return not self == other def __deepcopy__(self, _memo): return copy.deepcopy(self._data) @memo.memo_i() def itemtuple(self): return tuple(sorted(self.iteritems())) def mutableDict(self): """ Returns a mutable dictionary copy, replacing 'frozendict' with 'dict's. This function uses the 'copy.deepcopy' method to create a mutable deep copy of the dictionary. Note that due to the one-size-fits-all behavior of 'deepcopy', the result can be anything from heavyhanded to incorrect depending on the contents of the dictionary. The caller should make sure they understand the operation and its behavior on all of the dictionary's subtypes before using it. Returns: (dict) A mutable clone of the dictionary and its members. """ return copy.deepcopy(self) def extend(self, **kwargs): """Returns a copy of this object with the 'kwargs' fields updated.""" ndata = self.mutableDict() ndata.update(kwargs) return type(self)(**ndata)

26.507042

79

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1,882

4.736842

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0.044444

0.047619

0.019048

0

0.002653

0.198725

1,882

70

80

26.885714

0.832891

0.435175

0

0.058824

0

0.005988

0

1

0.382353

false

0

0.088235

0.294118

0.852941

0

null

0

null

0

1

0

1

0

3

6aab26683b9b2a063b1ca8928d6b0655775e0f6b

86,132

py

Python

model/entity_quotes.py

tkuculo/QuoteKG

a7b7d323679624a9cd3805e866028fad0a5a5408

[ "MIT" ]

null

model/entity_quotes.py

tkuculo/QuoteKG

a7b7d323679624a9cd3805e866028fad0a5a5408

[ "MIT" ]

null

model/entity_quotes.py

tkuculo/QuoteKG

a7b7d323679624a9cd3805e866028fad0a5a5408

[ "MIT" ]

null

#main_section > lines > line > text #main_section > lines > line > sub_line > text #main_section > sub_sections #main_section > templates > type #main_section > templates > empty_values #main_section > templates > values #main_section > templates > sub_templates #main_section > title > line > text from transformers.models.auto import configuration_auto from model.quote import * import collections languages_with_templates=["fr","da","nl","be","is","ca","bg","da","ka"] hybrid_languages = ["uk","ru","sv","et"] + ["ko","fa","cs","fi", "hy"] misattributed = { 'ar': ['ضعيف', 'متنازع عليه', 'بشكل غير صحيح', 'قائلا نعزى خطأ', 'يعزى خطأ إلى', 'ونقلت تم تعيينها', 'إساءة', 'نعزى بشكل غير صحيح', 'متصل بشكل غير صحيح', 'يعزى بشكل غير صحيح إلى', 'مثيرة للجدل', 'تم تعيينه بشكل غير صحيح', 'تم تعيينه بشكل غير صحيح', 'الفضل بشكل غير صحيح', 'مشكوك فيه', 'سوء المعاملة', 'سيئة', 'خاطئ', 'الفضل بشكل خاطئ', 'لم يتم التحقق منه', 'مرفقة بشكل غير صحيح', 'الفضل بشكل غير صحيح', 'غير صحيح', 'يعزى إلى الخطأ', 'مشبوه أو مشكوك فيه'],\ 'az': ['zəif', 'mübahisəli', 'yanlış', 'yanlış şəkildə aid olduğunu söyləmək', 'səhv yanına aiddir', 'Təyin olunmuş sitatlar', 'yanılsaq', 'səhv aiddir', 'səhv bağlıdır', 'səhv aiddir', 'mübahisəli', 'səhv təyin olunur', 'səhv təyin olunmuşdur', 'səhv hesablanır', 'şübhəli', 'zəif', 'səhv', 'səhv hesablanır', 'təsdiqlənməmiş', 'səhv əlavə olunur', 'səhv hesablanır', 'yanlış', 'səhvən aiddir', 'şübhəli'],\ 'be': ['слабы', 'спрэчны', 'няправільна', 'кажучы няправільна прыпісаны', 'памылкова звязаны з', 'Цытаты прызначаныя', 'misatributed', 'няправільна прыпісваецца', 'няправільна падлучаны', 'няправільна прыпісваецца', 'супярэчлівы', 'няправільна прызначаны', 'няправільна прызначаны', 'залічваецца няправільна', 'няпэўны', 'адварочваў', 'кепска', 'памылковы', 'памылкова залічана', 'неўверыў', 'няправільна прыкладаецца', 'няправільна залічаны', 'няправільны', 'прыпісваецца памылкова', 'падазроны'],\ 'bg': ['слаб', 'оспорван', 'неправилно', 'казвайки погрешно приписване', 'погрешно се приписва', 'Misattributed.', 'неправилно приписано', 'неправилно свързани', 'неправилно', 'противоречиви', 'е неправилно назначен', 'неправилно зададен', 'кредитирани неправилно', 'съмнително', 'Млъкни', 'лошо', 'погрешно', 'неправилно кредитирани', 'Несъвършен', 'неправилно прикрепени', 'неправилно кредитирани', 'неправилен', 'се приписва на погрешно', 'подозрителен'],\ 'bs': ['slab', 'sporan', 'pogrešno', 'govoreći pogrešno pripisano', 'pogrešno se pripisuje', 'Citati dodijeljene', 'misao', 'Netačno pripisan', 'Nepravilno povezani', 'pogrešno pripisan', 'kontroverzan', 'pogrešno je dodeljen', 'pogrešno dodijeljeno', 'pripisuju pogrešno', 'sumnjiv', 'maltretiran', 'slabo', 'pogrešno', 'pogrešno pripisan', 'neprovjeren', 'pogrešno priložen', 'pogrešno pripisan', 'netačan', 'pripisuje se pogrešno', 'sumnjiv'], \ 'ca': ['feble', 'en disputa', 'incorrectament', 'dient incorrectament atribuït', "s'atribueix incorrectament a", 'Cotitzacions assignades', 'Misattributed', 'atribuïts incorrectament', 'connectat incorrectament', 'atribuït incorrectament a', 'controvertit', 'està assignat incorrectament', 'assignat incorrectament', 'acreditat incorrectament', 'dubtós', 'maltractat', 'pobrament', 'mal', 'acreditat incorrectament', 'no verificat', 'incorrectament adjunt', 'acreditat incorrectament', 'incorrecte', "s'atribueix a erròniament", 'sospitós'], \ 'co': ['debuli', 'disputa', 'sbagliatu', 'dicendu attribuitu sbagliatu', 'sbagliatu hè attribuita à', 'Quotes assignati', 'misattribuitu', 'attribuitu sbagliatu', 'cunnessu sbagliatu', 'attribuitu sbagliatu à', 'cuntruversuale', 'hè incorrectamente assignatu', 'assignatu sbagliatu', 'creditu sbagliatu', 'dubbitu', 'MISTORATU', 'Poviru', 'sbagliatu', 'sbagliatu creditu', 'Unvererazionatu', 'sbagliatu attaccatu', 'incorrectamente creditu', 'sbagliatu', 'hè attribuita à sbaglià', 'suspicosu'],\ "cs": ['pochybný', 'nesprávně je připisován', 'je přičítán omylem', 'neosgejavané.', 'říká se nesprávně přiřazené', 'sporný', 'je nesprávně přiřazen', 'špatně', 'nesprávně připojeno', 'nesprávně', 'nezbytný', 'nesprávně přiřazeno', 'nesprávně přisuzováno', 'špatně zacházený', 'slabý', 'nesprávný', 'nesprávně připsány', 'nesprávně připsaný', 'přidělené nabídky', 'podezřelý', 'neověřené'],\ 'da': ['svag', 'bestridt', 'forkert', 'siger fejlagtigt tilskrevet', 'fejlagtigt tilskrives', 'citater tildelt', 'misattributed.', 'forkert tilskrevet', 'forkert forbundet', 'forkert tilskrives', 'kontroversielt', 'er forkert tildelt', 'forkert tildelt', 'krediteret forkert', 'tvivlsom', 'mishandlet', 'Dårlig', 'forkert', 'fejlagtigt krediteret', 'unverified.', 'forkert vedhæftet', 'forkert krediteret', 'ukorrekt', 'er tilskrevet fejlagtigt', 'mistænksom'], \ "de": ['falsch verbunden', 'falsch angebracht', 'falsch zugewiesen', 'wird fehlerhaft zurückgeführt', 'schwach', 'fälschlich zugeschrieben', 'falsch zugerechnet', 'falsch wird zugeschrieben', 'falsch', 'falsch angeschlossen', 'misshandelt', 'unrecht zugeschrieben werden', 'misstrauisch', 'falsch gutgeschrieben', 'zweifelhaft', 'ist falsch zugewiesen', 'notwendig', 'zitate zugewiesen', 'nicht verifiziert'],\ 'el': ['αδύναμος', 'αμφισβητούμενος', 'εσφαλμένα', 'λέγοντας εσφαλμένα αποδόσεις', 'λανθασμένα αποδίδεται σε', 'αποσπάσματα', 'απροσδόκητος', 'που αποδίδονται εσφαλμένα', 'εσφαλμένα συνδεδεμένο', 'που αποδοθεί εσφαλμένα', 'αμφιλεγόμενος', 'έχει ανατεθεί εσφαλμένα', 'εσφαλμένα αποδίδεται', 'πιστώθηκε λανθασμένα', 'αμφίβολος', 'κακομεταχειρίζομαι', 'πτωχώς', 'λανθασμένος', 'λάθος πιστώθηκε', 'ανεπιβεβαίωτος', 'Επισυνάπτεται εσφαλμένα', 'εσφαλμένα πιστώνεται', 'ανακριβής', 'αποδίδεται λανθασμένα', 'ύποπτος'],\ "en": ['weak', 'disputed', 'incorrectly', 'saying wrongly attributed', 'wrongly is attributed to', 'quotes assigned', 'misattributed', 'incorrectly attributed', 'incorrectly connected', 'incorrectly attributed to', 'controversial', 'is incorrectly assigned', 'incorrectly assigned', 'credited incorrectly', 'doubtful', 'mistreated', 'poorly', 'wrong', 'wrongly credited', 'unverified', 'incorrectly attached', 'incorrectly credited', 'incorrect', 'is attributed to mistakenly', 'suspicious'],\ "es": ['débil', 'disputado', 'incorrectamente', 'decir atribuido incorrectamente', 'atribuido incorrectamente a', 'citas asignadas', 'atribuido incorrectamente', 'atribuido incorrectamente', 'conectado incorrectamente', ' atribuido incorrectamente a ',' controvertido ',' asignado incorrectamente ',' asignado incorrectamente ',' acreditado incorrectamente ',' dudoso ',' maltratado ',' mal ',' incorrecto ',' acreditado incorrectamente ',' no verificado ', 'adjunto incorrectamente', 'acreditado incorrectamente', 'incorrecto', 'atribuido erróneamente', 'sospechoso'],\ 'et': ['nõrk', 'vaidlustatud', 'valesti', 'öeldes valesti omistatud', 'valesti omistatakse', 'määratud hinnapakkumisi', 'eksima', 'valesti omistatud', 'valesti ühendatud', 'valesti omistatud', 'vastuoluline', 'on valesti määratud', 'valesti määratud', 'krediteeritud valesti', 'kahtlane', 'väärkohtlemine', 'halvasti', 'vale', 'valesti krediteeritud', 'vastamata jätmine', 'valesti kinnitatud', 'valesti krediteeritud', 'vale', 'omistatakse ekslikult', 'kahtlane'],\ 'eu': ['ahul', 'jokatu', 'gaizki', 'gaizki egozten esanda', 'gaizki egozten zaio', 'esleitutako aipuak', 'Misattributatua', 'oker egotzi', 'Gaizki konektatuta', 'oker egotzita', 'Polemika', 'gaizki esleitzen da', 'gaizki esleituta', 'oker kreditua', 'zalantzazko', 'tratu txarrak', 'txarto', 'okerreko', 'gaizki kreditatu', 'irentetu gabe', 'oker erantsita', 'Gaizki kreditatu', 'ez zuzen', 'oker egozten zaio', 'goganbehartsu'],\ 'fa': ['ضعیف', 'متضاد', 'نادرست', 'گفتن اشتباه است', 'اشتباه به آن نسبت داده می شود', 'نقل قول اختصاص داده شده', 'سوء تفاهم', 'نادرست نسبت داده شده است', 'نادرست متصل است', 'نادرست به', 'بحث برانگیز', 'نادرست اختصاص داده شده است', 'اشتباه اختصاص داده شده است', 'اعتبار نادرست', 'مشکوک', 'بدرفتاری', 'ضعیف', 'اشتباه', 'اشتباه اعتبار', 'غیر قابل تایید', 'اشتباه متصل شده', 'اشتباه اعتبار', 'غلط', 'به اشتباه نسبت داده شده است', 'مشکوک'],\ 'fi': ['heikko', 'kiistanalainen', 'väärin', 'sanomalla väärin', 'virheellisesti johtuu', 'Lainaukset', 'huonosti', 'virheellisesti', 'Väärin kytketty', 'virheellisesti', 'kiistanalainen', 'on asetettu virheellisesti', 'Virheellisesti määritetty', 'hyvitetään väärin', 'epäilyttävä', 'kohteliaisuus', 'huonosti', 'väärä', 'Väärin hyvitetty', 'vahvistettu', 'Virheellisesti kiinnitetty', 'Virheellisesti hyvitetty', 'väärä', 'johtuu virheellisesti', 'epäilyttävä'],\ 'fr': ['faible', 'contesté', 'incorrectement', 'dire attribué à tort', 'est attribué à tort à', 'citations attribuées', 'mal attribué', 'mal attribué', 'incorrectement connecté', ' attribué à tort à', 'controversé', 'est attribué de manière incorrecte', 'attribué de manière incorrecte', 'crédité de manière incorrecte', 'douteux', 'maltraité', 'mal', 'mauvais', 'crédité à tort', 'non vérifié', 'incorrectement joint', 'mal crédité', 'incorrect', 'est attribué à tort', 'suspect'],\ 'he': ['חלש', 'משווקת', 'לא נכון', 'אומר מיוחסת בטעות', 'בטעות מיוחסת', 'ציטוטים שהוקצו', 'misattributed', 'המיוחס בצורה שגויה', 'קשור באופן שגוי', 'המיוחס לא נכון', 'שנוי במחלוקת', 'מוקצה באופן שגוי', 'שהוקצו באופן שגוי', 'זוכה באופן שגוי', 'מוטל בספק', 'התעללות', 'גרוע', 'שגוי', 'שזוכו בטעות', 'unverified', 'המצורפת באופן שגוי', 'זוכה לא נכון', 'לֹא נָכוֹן', 'מיוחסת לטעות בטעות', 'חָשׁוּד'], 'hi': ['कमज़ोर', 'विवादित', 'गलत तरीके से', 'गलत तरीके से कहना', 'गलत तरीके से जिम्मेदार है', 'उद्धरण सौंपा', 'गलत', 'गलत तरीके से जिम्मेदार', 'गलत तरीके से जुड़ा हुआ', 'गलत तरीके से जिम्मेदार ठहराया', 'विवादास्पद', 'गलत तरीके से सौंपा गया है', 'गलत तरीके से असाइन किया गया', 'गलत तरीके से श्रेय दिया गया', 'संदिग्ध', 'दुराचारित', 'बीमार', 'गलत', 'गलत तरीके से श्रेय दिया गया', 'असत्यापित', 'गलत तरीके से संलग्न', 'गलत तरीके से श्रेय दिया गया', 'ग़लत', 'गलती से जिम्मेदार है', 'संदेहजनक'],\ 'hr': ['slab', 'osporen', 'nepravilno', 'govoreći pogrešno pripisuje se', 'pogrešno se pripisuje', 'dodijeljeni citati', 'pogrešan', 'Neispravno se pripisuje', 'pogrešno povezan', 'pogrešno pripisuje', 'kontroverzno', 'je pogrešno dodijeljen', 'pogrešno dodijeljen', 'pogrešno pripisano', 'sumnjiv', 'maltretiran', 'slabo', 'pogrešno', 'pogrešno pripisano', 'neveritičan', 'pogrešno pričvršćen', 'pogrešno pripisano', 'netočno', 'se pripisuje pogrešno', 'sumnjičav'],\ 'hu': ['gyenge', 'vitatott', 'tévesen', 'rosszul mondván', 'helytelenül tulajdonítható', 'Idézetek hozzárendeltek', 'félreérthetetlen', 'helytelenül tulajdonítható', 'Helytelenül csatlakoztatva van', 'helytelenül tulajdonítható', 'vitatott', 'helytelenül hozzárendelt', 'Helytelenül hozzárendelt', 'helytelenül jóváírják', 'kétséges', 'rosszul kezelt', 'rosszul', 'rossz', 'tévesen jóváírta', 'ellenőrizetlen', 'Helytelenül csatolt', 'helytelenül jóváírta', 'helytelen', 'tévesen tulajdonítható', 'gyanús'],\ 'hy': ['թույլ', 'վիճված', 'սխալ', 'սխալ ասելով, վերագրվում է', 'սխալ է վերագրվում', 'Նշված մեջբերումները', 'Մատսել է', 'Սխալ կերպով վերագրվում է', 'Սխալ միացված', 'սխալ է վերագրվել', 'վիճաբանական', 'սխալ է նշանակվել', 'Սխալ նշանակված', 'սխալվել է սխալ', 'կասկածելի', 'չարամտել', 'վատ', 'սխալ', 'սխալվել է', 'անավարտ', 'Սխալորեն կցված', 'սխալ է գնահատվել', 'սխալ', 'վերագրվում է սխալմամբ', 'կասկածելի'],\ 'id': ['lemah', 'diperdebatkan', 'salah', 'mengatakan salah dikaitkan.', 'salah dikaitkan dengan', 'Kutipan ditugaskan', 'salah penyibaran', 'salah dikaitkan', 'salah terhubung', 'salah dikaitkan dengannya', 'kontroversial', 'salah ditugaskan', 'salah ditugaskan', 'dikreditkan secara salah', 'diragukan lagi', 'Dianiaya', 'buruk', 'salah', 'salah dikreditkan', 'tidak diverifikasi', 'salah melekat', 'salah dikreditkan', 'salah', 'dikaitkan dengan keliru', 'mencurigakan'],\ 'is': ['veik', 'umdeildur', 'rangt', 'segja að ranglega rekja til', 'rangt stafar af', 'Tilvitnanir úthlutað', 'misertributed.', 'rangt rekja má', 'rangt tengt', 'rangt rekja til', 'umdeild', 'er rangt úthlutað', 'rangt úthlutað', 'lögð rangt', 'efast', 'mistreated.', 'illa', 'rangt', 'ranglega lögð inn', 'unverfied.', 'rangt fylgir', 'Rangt viðurkennt', 'rangt', 'er rekja til ranglega', 'grunsamlegt'],\ 'it': ['debole', 'disputato', 'erroneamente', 'detto erroneamente attribuito', 'erroneamente attribuito a', 'virgolette assegnate', 'erroneamente attribuito', 'erroneamente attribuito', 'erroneamente connesso', ' erroneamente attribuito a', 'controverso', 'è assegnato in modo errato', 'assegnato in modo errato', 'accreditato in modo errato', 'dubbio', 'maltrattato', 'male', 'sbagliato', 'accreditato erroneamente', 'non verificato', 'erroneamente allegato', 'erroneamente accreditato', 'errato', 'è attribuito a erroneamente', 'sospetto'],\ 'ja': ['弱い', '議論した', '誤って', '間違って帰ったことを言っています', '間違って帰属しています', '割り当てられた引用符', '誤動作しました', '間違って帰属しました', '誤って接続されています', '誤って帰属しました', '物議を醸す', '間違って割り当てられています', '間違って割り当てられています', '誤って入金されました', '疑わしい', '虐待された', '不完全に', '間違い', '間違ってクレジットされました', '未検証', '誤って添付されています', '誤ってクレジットされました', '正しくない', '誤って帰属されています', '疑わしい'],\ 'ka': ['სუსტი', 'სადავო', 'არასწორად', 'არასწორად მიეკუთვნება', 'არასწორად მიეკუთვნება', 'შეთავაზებები', 'misattributed', 'არასწორად მიეკუთვნება', 'არასწორად უკავშირდება', 'არასწორად მიეკუთვნება', 'დროებითი', 'არასწორად არის მინიჭებული', 'არასწორად მინიჭებული', 'არასწორად დაკრედიტდება', 'საეჭვო', 'mistreated', 'ღარიბად', 'მცდარი', 'არასწორად დაკრედიტდება', 'გადაუსებული', 'არასწორად ერთვის', 'არასწორად დაკრედიტდება', 'არასწორი', 'შეცდომით მიეკუთვნება', 'საეჭვო'],\ 'ko': ['약한', '분쟁', '틀리게', '잘못된 것으로 말하고있다', '잘못된 것은', '할당 된 따옴표', '미해시', '잘못된 것으로 잘못된 것입니다', '잘못 연결되었습니다', '잘못된 것으로 잘못된 것입니다', '논란이 많은', '잘못 지정됩니다', '잘못 지정되었습니다', '잘못 적립되었습니다', '불안한', '학대하다', '신통치 않게', '잘못된', '잘못된 적립 된 것', '확인되지 않았습니다', '잘못 첨부되었습니다', '잘못 적립되었습니다', '잘못된', '실수로 기인합니다', '의심스러운'],\ 'lt': ['Silpnas', 'ginčijama', 'Neteisingai', 'sakydamas neteisingai priskirtas', 'neteisingai priskiriama', 'Citatos', 'nesuderinta', 'neteisingai priskiriama', 'neteisingai prijungta', 'neteisingai priskirta', 'prieštaringas', 'yra neteisingai priskirtas', 'neteisingai priskirtas', 'neteisingai įskaityta', 'abejotina', 'netinkamai elgiamasi', 'blogai', 'neteisingas', 'neteisingai įskaityta', 'nepatvirtinta', 'neteisingai prijungtas', 'neteisingai įskaityta', 'Neteisinga', 'priskiriama klaidingai', 'įtartinas'],\ 'nl': ['zwak', 'twijfelachtig', 'onjuist', 'Samenstellen ten onrechte toegeschreven', 'ten onrechte wordt toegeschreven aan', 'Citaten toegewezen', 'verkeerd ingesteld', 'Onjuist toegeschreven', 'Onjuist aangesloten', 'onjuist toegeschreven aan', 'controverseel', 'is verkeerd toegewezen', 'Onjuist toegewezen', 'verkeerd gecrediteerd', 'twijfelachtig', 'mishandeld', 'slecht', 'mis', 'ten onrechte gecrediteerd', 'ongehroken', 'verkeerd bevestigd', 'onjuist gecrediteerd', 'niet correct', 'wordt toegeschreven aan ten onrechte', 'verdacht'],\ 'no': ['svak', 'omstridt', 'feil', 'sier feilaktig tilskrives det', 'feil er tilskrevet', 'Sitater tildelt', 'misattributed.', 'feilaktig tilskrives det', 'feil tilkoblet', 'feilaktig tilskrives', 'kontroversiell', 'er feil tildelt', 'feilaktig tildelt', 'krediteres feil', 'tvilsom', 'feilbehandlet', 'dårlig', 'feil', 'feil kreditert', 'unverified.', 'feil festet', 'feil kreditert', 'stemmer ikke', 'er tilskrevet feilaktig', 'mistenkelig'],\ 'ro': ['slab', 'contestată', 'incorect', 'spunând atribuit greșit', 'este atribuit în mod greșit', 'Citate atribuite', 'misattribuit', 'incorect atribuită', 'incorect conectat', 'incorect atribuită', 'controversat', 'este atribuită incorect', 'incorect atribuite', 'creditat incorect', 'îndoielnic', 'maltratat', 'slab', 'gresit', 'creditat greșit', 'neveriectificat', 'În mod incorect atașat', 'incorect creditate', 'incorect', 'este atribuită în mod eronat', 'suspicios'],\ 'ru': ['слабый', 'оспариваемый', 'неправильно', 'говорить неправильно приписанным', 'неправильно объясняется', 'цитаты назначены', 'несущественно', 'неправильно приписан', 'неправильно подключен', 'неправильно приписан', 'спорный', 'неверно назначен', 'неверно назначен', 'зачислен неправильно', 'сомнительный', 'плохо обращаться', 'плохо', 'неправильный', 'неправильно приписывать', 'неверно', 'неправильно прилагается', 'неправильно зачислено', 'неверный', 'приписывается по ошибке', 'подозрительный'],\ 'sk': ['slabý', 'sporný', 'nesprávne', 'hovorí nesprávne pripisované', 'nesprávne sa pripisuje', 'Pridelené citácie', 'nesprávny', 'Nesprávne pripísané', 'Nesprávne pripojené', 'nesprávne pripísané', 'kontroverzný', 'je nesprávne priradený', 'Nesprávne priradené', 'nesprávne pripísané', 'pochybný', 'nespokojný', 'úboho', 'vhodný', 'nesprávne pripísané', 'neoverený', 'Nesprávne pripojené', 'Nesprávne pripísané', 'nesprávny', 'sa pripisuje mylne', 'podozrivý'],\ "sl": ["neozdrojované"'napačno prijavljeno', 'rekel napačno pripisano', 'napačno nakazana', 'napačno povezan', 'slabo', 'sumljivega', 'nepravilno dodeljena', 'neosgejavan.', 'dodeljeni citati', 'sporno', 'nepravilno pritrjena', 'nepreverjeno', 'napačno', 'je nepravilno dodeljen', 'nepravilno', 'napačno pripisano', 'se pripisuje pomotoma', 'in pavipe.', 'napačno pripisuje', 'dvomljiv', 'šibko', 'narobe', 'nepravilno pripisana'],\ "sq": ['i diskutueshëm', 'atribuohet gabimisht', 'i keqtrajtuar', 'i atribuohet gabimisht', 'i pasaktë', 'kredituar gabimisht', 'caktohet gabimisht', 'i lidhur gabimisht', 'i dyshimtë', 'i pavepi', 'i gabuar', 'thënie të atribuara gabimisht', 'bashkangjitur gabimisht', 'dobet'],\ "pl": ['zło', 'błędny', 'misattriruted.', 'źle traktować', 'słabo', 'wątpliwy', 'nieprawidłowo przymocowany', 'nieprawidłowo przypisany do', 'niepoprawnie przypisany', 'niepoprawnie połączony', 'mówiąc błędnie przypisany', 'kwestionować', 'cytaty przypisywane', 'niesprawdzony', 'błędnie przypisany', 'nieprawidłowo przypisany'], \ 'pt': ['fraca', 'contestada', 'incorretamente', 'dizendo atribuída incorretamente', 'atribuída incorretamente a', 'citações atribuídas', 'atribuída incorretamente', 'atribuída incorretamente', 'conectada incorretamente', ' atribuído incorretamente a ',' controverso ',' atribuído incorretamente ',' atribuído incorretamente ',' creditado incorretamente ',' duvidoso ',' maltratado ',' mal ',' errado ',' creditado incorretamente ',' não verificado ', 'incorretamente anexado', 'incorretamente creditado', 'incorreto', 'atribuído a incorretamente', 'suspeito'], \ 'ta': ['பலவீனமான', 'விவாதத்திற்குரியது', 'தவறாக', 'தவறாக சொல்லப்பட்டது', 'தவறாக காரணம்', 'மேற்கோள் ஒதுக்கப்படும்', 'misattributed.', 'தவறாக காரணம்', 'தவறாக இணைக்கப்பட்டுள்ளது', 'தவறாக காரணம்', 'சர்ச்சைக்குரிய', 'தவறாக ஒதுக்கப்பட்டுள்ளது', 'தவறாக ஒதுக்கப்படும்', 'தவறாக வழங்கப்பட்டது', 'சந்தேகம்', 'தவறாக நடத்தப்பட்டது', 'மோசமாக', 'தவறு', 'தவறாக வரவு', 'சரிபார்க்கப்படவில்லை', 'தவறாக இணைக்கப்பட்டுள்ளது', 'தவறாக நம்பப்படுகிறது', 'தவறானது', 'தவறுதலாக காரணம்', 'சந்தேகத்திற்கிடமான'],\ 'te': ['బలహీనమైన', 'వివాదాస్పదంగా', 'తప్పుగా', 'తప్పుగా ఆపాదించబడినది', 'తప్పుగా ఆపాదించబడినది', 'కేటాయించిన కోట్స్', 'myatattributed', 'తప్పుగా ఆపాదించబడినది', 'తప్పుగా కనెక్ట్ చేయబడింది', 'తప్పుగా ఆపాదించబడినది', 'వివాదాస్పద', 'తప్పుగా కేటాయించబడుతుంది', 'తప్పుగా కేటాయించబడింది', 'తప్పుగా జమ చేయబడుతుంది', 'అనుమానాస్పద', 'బాధితుడు', 'పేలవంగా', 'తప్పు', 'తప్పుగా ఘనత పొందింది', 'ధృవీకరించనిది', 'తప్పుగా జతచేయబడింది', 'తప్పుగా ఘనత పొందింది', 'తప్పు', 'తప్పుగా ఆపాదించబడింది', 'అనుమానాస్పద'],\ 'uk': ['слабкий', 'спірний', 'неправильно', 'кажучи неправильно віднесено', 'неправильно пояснюється', 'Призначені цитати', 'мізерний', 'неправильно віднесено', 'неправильно підключено', 'неправильно віднесено', 'суперечливий', 'неправильно призначено', 'неправильно призначено', 'неправильно приписується', 'сумнівний', 'погано', 'погано', 'неправильний', 'неправильно зарахований', 'неперевірений', 'неправильно прикріплені', 'неправильно зараховано', 'неправильний', 'пояснюється помилково', 'підозрілий'],\ 'ur': ['کمزور', 'متنازعہ', 'غلط طور پر', 'غلط طور پر منسوب کیا گیا ہے', 'غلط طور پر منسوب کیا جاتا ہے', 'حوالہ جات', 'غلط استعمال کی اطلاع دیتے ہوئے ایرر آ گیا ہے', 'غلط طور پر منسوب', 'غلط طور پر منسلک', 'غلط طور پر منسوب', 'متضاد', 'غلط طور پر تفویض کیا جاتا ہے', 'غلط طور پر تفویض', 'غلط طریقے سے کریڈٹ', 'شکست', 'غلطی', 'غریب', 'غلط', 'غلط طور پر کریڈٹ', 'غیر تصدیق شدہ', 'غلط طریقے سے منسلک', 'غلط طریقے سے کریڈٹ', 'غلط', 'غلطی سے منسوب کیا جاتا ہے', 'مشکوک'],\ 'vi': ['Yếu', 'tranh chấp', 'không chính xác', 'nói sai quy kết', 'sai được quy cho', 'Báo giá được giao', 'sai lệch', 'quy cho không chính xác', 'kết nối không chính xác', 'quy cho không chính xác cho.', 'gây tranh cãi', 'được giao không chính xác', 'chỉ định không chính xác', 'ghi có không chính xác', 'nghi ngờ', 'ngược đãi', 'kém', 'Sai lầm', 'Tín dụng sai', 'chưa được xác minh', 'đính kèm không chính xác', 'Credited không chính xác', 'không đúng', 'được quy cho nhầm', 'khả nghi'],\ 'zh': ['弱', '有争议', '不正确', '错误归因', '错误归因于', '引用分配', '错误归因', '错误归因', '错误连接', ' 错误地归因于', '有争议的', '被错误地分配', '错误地分配','记入错误','可疑','虐待','差','错误','错误记入','未验证', '错误附加','错误记入','错误','归因于错误','可疑'] } #attributed? Neověřené disputed # to be checked: Djela, Obras, Povedali o forbidden_by_language = { "ar" : ["قالوا عنه","قالوا عنه","أشهر مؤلفاتها","الوصلات الخارجية"],\ "az" : ["İstinadlar","Mənbə","Xarici keçidlər","Haqqında deyilənlər","istinadlar"],\ "be":["Выказванні пра", "зноскі","спасылкі"],\ "bg":["За нея","за него","Източници","Бележки","Външни препратки","литература"],\ "bs":["Drugi o njemu","Djela","Također pogledajte","Vanjski linkovi","Izdanja"],\ "ca":["citacions sobre","Referències","Bibliografia","Enllaços externs","referències"],\ "co":["daveoù"],\ "cs":["ve výrocích","Reference","Externí odkazy","Související"],\ "da":["Eksterne henvisninger","Kilder"],\ "de":["zitate mit bezug auf", ],\ "el":["εξωτερικοί σύνδεσμοι"],\ "es":["sobre", "Obras", "Véase también", "Bibliografía","referencias"],\ "et":["välislingid"],\ "en":["quotes about", "filmography", "footnote", "sources", "resources", "other projects","external links","links",\ "notes", "note", "weblinks", "bibliogprahy", "related items","works", "references","literature","see","see also",\ "footnote","other projects"],\ "eu":["Kanpo loturak","Erreferentziak"],\ "fa":["دربارهٔ او","پیوند به بیرون","جستارهای وابسته","منبع‌دار", "منابع","پیوند به‌بیرون"],\ "fi":["sanottua","lähteet"],\ "fr":["sur "],\ "he":["על עצמה", "נאמר עליה","מקורות","קישורים חיצוניים","נאמר עליו","ראו גם"],\ "hi":["बाहरी कडियाँ"],\ "hr":["vanjske poveznice"],\ "hu":["róla mondták","külső hivatkozások","Művei"],\ "hy":["Աղբյուրներ","Ծանոթագրություններ","ծանոթագրություններ"],\ "is":["tenglar"],\ "id":["pranala luar"],\ "it":["citazioni su","Doppiaggio","film","filmografia","altri progetti","voci correlate"], \ "ja":["外部リンク"],\ "ka":["რესურსები ინტერნეტში"],\ "ko":["각주","관련 어록"],\ "lt":["nuorodos"],\ "nl":["over "], \ "no":["eksterne lenker","referanser"],\ "pl":["zobacz też","o "],\ "pt":["obras", "sobre","Ligações externas"],\ "ro":["legături externe","despre"],\ "ru":["Об","Фильмография","примечания","ссылки", "см. также"],\ "sk":["Povedali o","iné projekty","referencie"],\ "sl":["viri","sklici"],\ "sq":["Thënie për të","Referimet","Shiko edhe","lidhje të jashtme","referime"],\ "ta":["வெளி இணைப்புகள்","சான்றுகள்"],\ "te":["మూలాలు"],\ "tr":["Hakkında","kaynakça"],\ "uk":["Про","Джерела","примітки","література"],\ "ur":["حوالہ جات"],\ "vi":["Liên kết ngoài","notennoù"],\ "zh":["外部链接","参见","参考文献"] } forbidden_by_language["ar"] = ["قالوا عنه", "قالوا عنه", "أشهر مؤلفاتها", "الوصلات الخارجية", "انظر أيضا إلى", "فهرس", "ويعمل", "ملحوظة", "المرجعي", "آخر حول هذا الموضوع", "في الذكرى", "قالوا عن ذلك", "فيلموجرافيا.", "قوله", "روابط", "قالوا عنه", "يقال عن", "يقتبس", "رابط ل", "الإحالات", "الأكثر شهرة الكتب", "الفمود الخارجي", "وصلات خارجية", "مصادر:", "عنه", "استفسارات الاعتماد", "المرجعي", "دبلجة", "فيلموجرافيا.", "له", "في", "روابط خارجية", "يلعب", "هامش", "قيل عنه", "ويعمل", "يلعب", "على", "مشاريع أخرى", "عن", "عنها", "موارد", "رابط خارجي", "المراجع", "مصادر", "قيل عنها", "الحواشي", "المراجع الخارجية", "الأصناف ذات الصلة", "مصدر", "ملحوظات:", "روابط", "لها", "إطلاق", "الشهادات - التوصيات", "ملحوظات", "قل", "الموارد في الإنترنت", "أنظر أيضا", "daveoù.", "رابط إلى الخارج", "عنه", "أنظر أيضا", "فيلم", "تشغيل", "مراجع", "قالوا O.", "متعلق ب", "رابط خارجي", "بيانات حول", "حول", "الاستشهادات المذكورة أعلاه", "مصادر", "سفير", "يقال له", "المؤلفات", "حول نفسها", "روابط خارجية", "التطبيقات ذات الصلة", "ونقلت فيما يتعلق", "ارى", "على", "الروابط الزائدة", "ونقلت حول", "فيلموجرافيا.", "هامش", "مصادر", "مصادر", "مشاريع أخرى", "روابط خارجية", "روابط", "ملحوظات", "ملاحظة", "روابط انترنت", "فهرس", "الأصناف ذات الصلة", "ويعمل", "المراجع", "المؤلفات", "ارى", "أنظر أيضا", "هامش", "مشاريع أخرى"] forbidden_by_language["az"] = ["İstinadlar", "Mənbə", "Xarici keçidlər", "Haqqında deyilənlər", "istinadlar", "Də baxmaq", "Biblioqrafiya", "əsər", "Əlamətdar", "istinad", "Bu barədə başqa bir şey", "Yubileydə", "Bu barədə dedilər", "Filmoqrafiya", "Deyən", "linklər", "Onun haqqında dedilər", "Haqqında deyilir", "sitat gətirən", "Birləşdirmək", "refertral", "Ən məşhur kitablar", "Xarici TADS", "Xarici əlaqələr", "Mənbələr:", "onun haqqında", "sualları asılı idi", "İstinad", "Dublying", "filmoqrafiya", "onun üçün", "O", "xarici linklər", "pyes", "izdihamlı", "Onun haqqında deyildi", "Əsər", "Pyes", "üstünə", "Digər layihələr", "Haqqında", "onun haqqında", "Resurslar", "Xarici əlaqə", "arayışlar", "Mənbələr", "Onun haqqında deyildi", "izahat", "Xarici İstinadlar", "Oxşar əşyalar", "Mənbəyi", "Qeydlər:", "Linklər", "Onun üçün", "Buraxılış", "Şöhrətli", "Qeydlər", "ərz etmək", "İnternetdəki mənbələr", "Həmçinin bax", "daveoùù", "Xarici ilə əlaqə", "Onun haqqında", "həmçinin bax", "filmə", "yan", "Arayışlar", "Dedilər.", "Bahalı", "xarici əlaqə", "Haqqında ifadələr", "haqqında", "Yuxarıdakı sitatlar", "mənbələr", "Səfir", "Ona deyilir", "ədəbiyyat", "haqqında özü haqqında", "xarici linklər", "Əlaqədar tətbiqlər", "Hörmətlə sitatlar", "Görmək", "artıq", "Həddindən artıq bağlantılar", "haqqında sitatlar", "filmoqrafiya", "izdihamlı", "mənbələr", "resurslar", "Digər layihələr", "xarici linklər", "linklər", "qeydlər", "Qeyd", "vaklar", "biblioqrafiya", "Oxşar əşyalar", "əsər", "arayışlar", "ədəbiyyat", "görmək", "həmçinin bax", "izdihamlı", "Digər layihələr"] forbidden_by_language["be"] = ["Выказванні пра", "зноскі", "спасылкі", "Таксама глядзець на", "Бібліяграфія", "работы", "нотенно", "спасылка", "Іншы пра гэта", "У гадавіне", "Яны сказалі пра гэта", "Фільмаграфія", "Кажучы", "спасылкі", "Яны сказалі пра яго", "Кажуць пра", "каціроўкі", "Спасылка на", "рэфералы", "Самыя вядомыя кнігі", "Знешнія казалі", "Знешнія злучэння", "Крыніцы:", "пра яго", "Залежныя запыты", "Спасылка", "Нядбайнны", "фільмаграфія", "для яго", "Аб", "Знешнія спасылкі", "п'есы", "знос", "было сказана пра яго", "Работы", "П'есы", "на", "Іншыя праекты", "Пра", "пра яе", "Рэсурсы", "Знешняя спасылка", "рэкамендацыі", "Крыніцы", "Было сказана пра яе", "знос", "Знешнія спасылкі", "Звязаныя элементы", "Крыніца", "Нататкі:", "Спасылкі", "Для яе", "Верхі", "Водгукі", "Нататкі", "гаварыць", "Рэсурсы ў Інтэрнэце", "См таксама", "Спасылка на вонкавым боку", "Пра яго", "см таксама", "плёнка", "на", "Рэкамендацыі", "Яны сказалі, што О.", "Звязаны", "знешняя спасылка", "Заявы аб", "пра", "Цытаты вышэй", "крыніцы", "літаратура", "знешнія спасылкі", "Звязаныя з прыкладаннямі", "Каціроўкі ў адносінах да", "Бачыць", "больш", "Лішак спасылкі", "цытаты аб", "фільмаграфія", "крыніцы", "рэсурсы", "іншыя праекты", "знешнія спасылкі", "спасылкі", "нататкі", "нататка", "weblinks", "бібліяграфія", "Звязаныя элементы", "работы", "рэкамендацыі", "літаратура", "бачыць", "см таксама", "іншыя праекты"] forbidden_by_language["bg"] = ["За нея", "за него", "Източници", "Бележки", "Външни препратки", "литература", "Също погледнете", "Библиография", "върши работа", "Бележит", "справка", "Друг за него", "в годишнината", "Те казаха за това", "Филмография", "Да се каже", "Връзки", "Те казаха за него", "Се казва", "Връзка към", "Реферали", "Най-известните книги", "Външни тади", "Външни връзки", "Източници:", "за него", "Запитвания", "Справка", "Дублиране", "Филмография", "за него", "О", "външни връзки", "Играе", "Бележка под линия", "Беше казано за него", "Върши работа", "Играе", "в", "Други проекти", "относно", "за нея", "Ресурси", "Външен линк", "препратки", "Източници", "Беше казано за нея", "Бележки под линия", "Външни препратки", "Подобни продукти", "Източник", "Забележки:", "Връзки", "За нея", "Освобождаване", "Отзиви", "Бележки", "казвам", "Ресурси в Интернет", "Вижте също", "Дъстина", "Връзка с външната страна", "За него", "Вижте също", "филм", "На", "Препратки", "Те казаха О.", "Свързани", "външен линк", "Изявления за", "относно", "Цитати над", "Източници", "Посланик", "Му се казва", "Литература", "за себе си", "външни връзки", "Свързани приложения", "Цитати по отношение на", "Вж", "над", "Излишните връзки", "цитати за", "Филмография", "Бележка под линия", "Източници", "Ресурси", "Други проекти", "външни връзки", "Връзки", "Бележки", "Забележка", "WeBlinks.", "Библиография", "подобни продукти", "върши работа", "препратки", "Литература", "вж", "Вижте също", "Бележка под линия", "Други проекти"] forbidden_by_language["bs"] = ["Drugi o njemu", "Djela", "Također pogledajte", "Vanjski linkovi", "Izdanja", "Takođe pogledajte", "Bibliografija", "radovi", "Primijetan", "referenca", "Još jedan o tome", "u godišnjici", "Rekli su o tome", "Filmografija", "Govoreći", "linkove", "Rekli su o njemu", "Su rekli o", "citati", "Link na", "preporuke", "Najpoznatije knjige", "Vanjski tads", "Vanjske veze", "Izvori:", "o njemu", "Zavito upiti", "Referenca", "Presnimav", "Filmografija", "za njega", "O", "Vanjske veze", "igra", "fusnota", "Rečeno je o njemu", "Radovi", "Igra", "na", "Ostali projekti", "O", "o njoj", "Resursi", "Vanjska veza", "reference", "Izvori", "Rečeno je o njoj", "fusnote", "Vanjske reference", "Srodni predmeti", "Izvor", "Napomene:", "Linkove", "Za nju", "Izdanja", "Testimonials", "Bilješke", "izgovoriti", "Resursi na Internetu", "Vidjeti i", "Daveoù", "Veza sa spolja", "O njemu", "vidjeti i", "film", "na", "Reference", "Rekli su O.", "Povezani", "Vanjska veza", "Izjave o", "o", "Citati gore", "izvori", "Ambasador", "Kaže mu", "literatura", "o sebi", "Vanjske veze", "Srodne aplikacije", "Citati u odnosu na", "Vidjeti", "preko", "Višak veze", "citati o", "Filmografija", "fusnota", "izvori", "resursi", "Ostali projekti", "Vanjske veze", "linkove", "bilješke", "Bilješka", "Webliks", "bibliografija", "Srodni predmeti", "radovi", "reference", "literatura", "vidjeti", "vidjeti i", "fusnota", "Ostali projekti"] forbidden_by_language["ca"] = ["citacions sobre", "Referències", "Bibliografia", "Enllaços externs", "referències", "També mireu", "Bibliografia", "treballa", "Notable", "referència", "Un altre sobre això", "En l'aniversari", "Van dir sobre això", "Filtrografia", "Dient", "enllaç", "Van dir sobre ell", "Es diu sobre", "cites", "Enllaç a", "referències", "Els llibres més famosos", "Tads exteriors", "Connexions externes", "Fonts:", "sobre ell", "Consultes dependents", "Referència", "% De comportament", "filtrografia", "per ell", "O a", "Enllaços externs", "obert", "Nota al peu", "Es va dir sobre ell", "Treballa", "Obert", "a sobre de", "Altres projectes", "Sobre", "sobre ella", "Recursos", "Enllaç extern", "referències", "Fonts", "Es va dir sobre ella", "Notes al peu de pàgina", "Referències externes", "Articles relacionats", "Font", "NOTES:", "Enllaç", "Per ella", "Llançaments", "Testimonis", "Notes", "dir", "Recursos a Internet", "Vegeu també", "daveoù", "Enllaç a l'exterior", "Sobre ell", "Vegeu també", "pel·lícula", "conectada", "Referències", "Van dir O.", "Relacionada", "Enllaç extern", "Declaracions sobre", "Sobre", "Cites anteriors", "fonts", "Ambaixador", "Se li diu", "literatura", "sobre ella mateixa", "Enllaços externs", "Aplicacions relacionades", "Cites respecte a", "Veure", "sobrar", "Enllaços d'excés", "cites sobre", "filtrografia", "Nota al peu", "fonts", "recursos", "Altres projectes", "Enllaços externs", "enllaç", "notes", "nota", "Weblinks", "bibliografia", "Articles relacionats", "treballa", "referències", "literatura", "veure", "Vegeu també", "Nota al peu", "Altres projectes"] forbidden_by_language["co"] = ["daveoù", "Fighjà ancu", "Bibliografia", "FUNZIONI", "Notabile", "Riferimentu", "Un altru nantu à questu", "In l'anniversariu", "Anu dettu di questu", "Filmografia", "Dicendu à", "Ligami", "Anu dettu di ellu", "Sò dettu di circa", "Ligame cù", "I referenze", "I libri più famosi", "Tadri esterni", "Cunnessioni esterni", "FONTI:", "circa ellu", "Quistioni dipendenti", "Riferimentu", "Dubaghju", "Filmografia", "per ellu", "O", "Ligami esterni", "Ghjucà", "nota di nota", "si dicia di ellu", "FUNZIONI", "Ghjucà", "à", "Altri prughjetti", "Circa à", "circa ella", "Risorse", "Link esternu", "Riferimenti", "Fonti", "Si dicia di ella", "Testrootes", "Riferimenti esterni", "Oggetti Relativi", "Fonte", "NOTI:", "Ligami", "Per ella", "Release", "Testimonianza", "Note", "dì", "Risorse in Internet", "Vede ancu", "daveoù", "Ligame à l'esterno", "Circa ellu", "vede ancu", "film", "avanti", "Riferimenti", "Anu dettu O.", "Ligatu", "Link esternu", "Dichjarazioni circa", "circa à", "Citazioni sopra", "fonti", "Ambasciatore", "Si dice à ellu", "Letteratura", "circa ella stessu", "ligami esterni", "Applicazioni ligate", "Quotes cun rispettu à", "Vede", "finitu", "Ligami d'uccasioni", "citazioni circa", "Filmografia", "nota di nota", "fonti", "Risorse", "altri prughjetti", "ligami esterni", "Ligami", "Note", "Nota", "weblinks", "bibliografia", "Oggetti Relativi", "FUNZIONI", "Riferimenti", "Letteratura", "vede", "vede ancu", "nota di nota", "altri prughjetti"] forbidden_by_language["cs"] = ["ve výrocích", "Reference", "Externí odkazy", "Související", "Také se podívejte na", "Bibliografie", "práce", "Pozoruhodný", "odkaz", "Další o tom", "v výročí", "Řekli o tom", "Filmografie", "Říkat", "Odkazy", "Řekli o něm", "Říkají se asi", "citáty", "Odkaz na", "odkazy", "Nejznámější knihy", "Vnější Tads.", "Externí připojení", "Prameny:", "o něm", "Závislé dotazy", "Odkaz", "Dabing", "filmografie", "pro něj", "Ó", "externí odkazy", "hra", "poznámka pod čarou", "Řekl to o něm", "Práce", "Hra", "na", "Další projekty", "O", "o ní", "Zdroje", "Externí odkaz", "Reference", "Prameny", "Řekl to o ní", "poznámky pod čarou", "Externí odkazy", "Související zboží", "Zdroj", "Poznámky:", "Odkazy", "Pro ni", "Releases", "Svědectví", "Poznámky", "říci", "Zdroje v Internetu", "Viz také", "daveoù.", "Odkaz na vnější stranu", "O něm", "viz také", "film", "na", "Reference", "Řekli O.", "Příbuzný", "Externí odkaz", "Výkazy", "o", "Citace výše", "prameny", "Velvyslanec", "Říká se mu", "literatura", "o sobě", "externí odkazy", "Související aplikace", "S ohledem na", "Vidět", "přes", "Přebytečné odkazy", "cituje", "filmografie", "poznámka pod čarou", "prameny", "zdroje", "Další projekty", "externí odkazy", "Odkazy", "poznámky", "Poznámka", "webové odkazy", "bibliografie", "Související zboží", "práce", "Reference", "literatura", "vidět", "viz také", "poznámka pod čarou", "Další projekty"] forbidden_by_language["da"] = ["Eksterne henvisninger", "Kilder", "Se også på", "Bibliografi.", "arbejder", "Bemærkelsesværdig", "reference", "En anden om det", "i jubilæet.", "de sagde om det", "Filmografi.", "Siger til", "links.", "De sagde om ham", "Er sagt omkring", "citater", "Link til", "henvisninger.", "De mest berømte bøger", "Ydre tads.", "Eksterne forbindelser", "Kilder:", "om ham", "Afhængige forespørgsler", "Reference", "Dubbing.", "Filmografi.", "For ham", "O.", "eksterne links", "spiller.", "fodnote.", "Det blev sagt om ham", "Arbejder", "Spiller.", "på", "Andre projekter", "Om", "om hende", "Ressourcer.", "Eksternt link", "Referencer.", "Kilder.", "Det blev sagt om hende", "fodnoter.", "Eksterne referencer.", "Relaterede elementer.", "Kilde", "Noter:", "Links.", "For hende", "Udgivelser.", "Testimonials.", "Noter.", "sige", "Ressourcer på internettet", "Se også", "daveoù.", "Link til ydersiden", "Om ham", "se også", "film", "på", "Referencer.", "De sagde O.", "Relaterede", "Eksternt link", "Udsagn om", "om", "Citater ovenfor", "Kilder.", "Ambassadør", "Det siges til ham", "litteratur", "om sig selv.", "eksterne links", "Relaterede applikationer", "Citater med hensyn til", "Se", "over", "Overskydende links.", "citater om", "Filmografi.", "fodnote.", "Kilder.", "ressourcer.", "andre projekter", "eksterne links", "links.", "noter.", "Bemærk", "Weblinks.", "bibliografi", "relaterede elementer.", "arbejder", "Referencer.", "litteratur", "se", "se også", "fodnote.", "andre projekter"] forbidden_by_language["de"] = ["Zitate über", "Filmografie", "Fußnote", "Quellen", "Ressourcen", "andere Projekte", "externe Links", "Links", "Notizen", "Hinweis", "Weblinks", "Literaturverzeichnis", "verwandte Artikel", "Werke", "Referenzen", "Literatur", "sehen", "siehe auch", "Fußnote", "andere Projekte", "Auch anschauen", "Bibliographie", "Werke", "Bemerkenswert", "Referenz", "Noch einer darüber", "im Jubiläum", "Sie sagten darüber", "Filmografie", "Sagen zu", "Links", "Sie sagten über ihn", "Sind sagte über", "Zitate", "Link zu", "Empfehlungen", "Die berühmtesten Bücher", "Outer tads", "Externe Verbindungen", "Quellen:", "über ihn", "Abhängige Anfragen", " Referenz", "Synchronisation", "Filmografie", "für ihn", "O", "Externe Links", "Spiele", "Fußnote", "es wurde über ihn gesagt", "Werke", "Spiele", " auf", "Andere Projekte", "Über", "Über sie", "Ressourcen", "Externer Link", "Referenzen", "Quellen", "Es wurde über sie gesagt", "Fußnoten", "Externe Verweise", "Verwandte Artikel", "Quelle", "Notizen:", "Links", "Für sie", "Veröffentlichungen", "Testimonials", "Nicht es", "sagen", "Ressourcen im Internet", "Siehe auch", "daveoù", "Link nach außen", "Über ihn", "Siehe auch", "Film", "on", "Referenzen", "Sie sagten O.", "Verwandte", "externer Link", "Aussagen über", "über", "Zitate oben", "Quellen", "Botschafter", "Es wird ihm gesagt", "Literatur", "über sich selbst", "externe Links", "Verwandte Anwendungen", "Zitate in Bezug auf", "Siehe", "über", "Überzählige Links", "Zitate über", "Filmografie", "Fußnote", " Quellen", "Ressourcen", "andere Projekte", "externe Links", "Links", "Notizen", "Hinweis", "Weblinks", "Bibliographie", "Verwandte Artikel", "Werke", "Referenzen", "Literatur", "sehen", "siehe auch", "Fußnote", "andere Projekte"] forbidden_by_language["el"] = ["εξωτερικοί σύνδεσμοι", "Επίσης κοιτάξτε", "Βιβλιογραφία", "έργα", "Αξιοσημείωτος", "αναφορά", "Ένα άλλο για αυτό", "Στην επέτειο", "είπαν γι 'αυτό", "Φωτοτυπογραφία", "Λέγοντας", "συνδέσεις", "Είπαν γι 'αυτόν", "Λέγονται", "αποσπάσματα", "Συνδέω με", "παραπομπές", "Τα πιο διάσημα βιβλία", "Εξωτερικά μαύρα", "Εξωτερικές συνδέσεις", "Πηγές:", "για αυτόν", "εξαρτώμενα ερωτήματα", "Αναφορά", "Μεταγλώ", "φωτοτυπογραφία", "για εκείνον", "O", "εξωτερικοί σύνδεσμοι", "παίζει", "υποσημείωση", "Είχε ειπωθεί γι 'αυτόν", "Εργα", "Παίζει", "επάνω σε", "Άλλα έργα", "Σχετικά με", "σχετικά με αυτήν", "Πόροι", "Εξωτερικός σύνδεσμος", "βιβλιογραφικές αναφορές", "Πηγές", "Είχε ειπωθεί γι 'αυτήν", "υποσημειώσεις", "Εξωτερικές αναφορές", "Σχετικά Αντικείμενα", "Πηγή", "Σημειώσεις:", "Συνδέσεις", "Για εκείνη", "Απελευθερώνει", "Μαρτυρίες", "Σημειώνει", "λένε", "Πόροι στο Διαδίκτυο", "Δείτε επίσης", "daveoù", "Σύνδεσμος προς το εξωτερικό", "Για αυτόν", "δείτε επίσης", "ταινία", "επί", "βιβλιογραφικές αναφορές", "Είπαν Ο.", "Σχετίζεται με", "εξωτερικός σύνδεσμος", "Δηλώσεις σχετικά με", "σχετικά με", "Παραπάνω αναφορές", "πηγές", "Πρεσβευτής", "Του λέγεται", "λογοτεχνία", "Σχετικά με τον εαυτό της", "εξωτερικοί σύνδεσμοι", "Σχετικές εφαρμογές", "Αποσπάσματα σε σχέση με", "Βλέπω", "πάνω από", "Υπερβολικοί σύνδεσμοι", "αποσπάσματα περίπου", "φωτοτυπογραφία", "υποσημείωση", "πηγές", "πόροι", "Άλλα έργα", "εξωτερικοί σύνδεσμοι", "συνδέσεις", "σημειώνει", "Σημείωση", "διαδικτυακοί σύνδεσμοι", "βιβλιογραφία", "Σχετικά Αντικείμενα", "έργα", "βιβλιογραφικές αναφορές", "λογοτεχνία", "βλέπω", "δείτε επίσης", "υποσημείωση", "Άλλα έργα"] forbidden_by_language["et"] = ["välislingid", "Vaata ka", "Bibliograafia", "töötama", "Märkimisväärne", "viide", "Teine sellest", "aastapäeval", "Nad ütlesid sellest", "Filmograafia", "Öeldes", "lingid", "Nad ütlesid temast", "Öeldakse", "tsitaat", "Link", "viited", "Kõige kuulsamad raamatud", "Outer Tads", "Välised ühendused", "Allikad:", "temast", "sõltus päringutest", "Viide", "Dubleerimine", "filmograafia", "tema jaoks", "O", "Välised lingid", "mängima", "joonealune märkus", "Ta ütles temast", "Töötama", "Mängima", "peale", "Muud projektid", "Umbes", "temast", "Vahendid", "Väline link", "viited", "Allikad", "Tema kohta öeldi", "joonealused märkused", "Välised viited", "Seotud üksused", "Allikas", "Märkused:", "Lingid", "Temale", "Väljaanded", "Iseloomustused", "Märgib", "ütlema", "Ressursid Internetis", "Vaata ka", "daveoù", "Link väljastpoolt", "Temast", "Vaata ka", "film", "peal", "Viited", "Nad ütlesid O.", "Seotud", "Väline link", "Avaldused", "umbes", "Valitud tsitaadid", "allikad", "Suursaadik", "See on talle öeldud", "kirjandus", "ennast", "Välised lingid", "Seotud rakendused", "Hinnapakkumisi", "Nägema", "üle", "Liigne lingid", "hinnapakkumisi", "filmograafia", "joonealune märkus", "allikad", "vahendid", "Muud projektid", "Välised lingid", "lingid", "märgib", "Märge", "weblinks", "bibliograafia", "Seotud üksused", "töötama", "viited", "kirjandus", "nägema", "Vaata ka", "joonealune märkus", "Muud projektid"] forbidden_by_language["en"] = ["quotes about", "filmography", "footnote", "sources", "resources", "other projects", "external links", "links", "notes", "note", "weblinks", "bibliography", "related items", "works", "references", "literature", "see", "see also", "footnote", "other projects", "Also look at", "Bibliography", "works", "Notable", "reference", "Another about it", "in the anniversary", "they said about it", "Filmography", "Saying to", "links", "They said about him", "Are said about", "Link to", "referrals", "The most famous books", "Outer tads", "External connections", "Sources:", "about him", "depended queries", "Reference", "Dubbing", "filmography", "for him", "O", "External links", "plays", "footnote", "it was said about him", "Works", "Plays", "upon", "Other projects", "About", "about her", "Resources", "External link", "references", "Sources", "It was said about her", "footnotes", "External references", "Related items", "Source", "Notes:", "Links", "For her", "Releases", "Testimonials", "Notes", "say", "resources in Internet", "See also", "daveoù", "Link to the outside", "About him", "see also", "film", "on", "References", "They said O.", "Related", "external link", "Statements about", "about", "Citations above", "sources", "Ambassador", "It is said to him", "literature", "about herself", "external links", "Related Applications", "Quotes with respect to", "See", "over", "Excess links", "quotes about", "filmography", "footnote", "sources", "resources", "other projects", "external links", "links", "notes", "note", "weblinks", "bibliography", "related items", "works", "references", "literature", "see", "see also", "footnote", "other projects"] forbidden_by_language["eu"] = ["Kanpo loturak", "Erreferentziak", "Begira ere", "Bibliografia", "zeregin", "Nabarmen", "kontsulta", "Horri buruz", "Urteurrenean", "Esan zuten", "Filmografia", "Esanda", "estekak", "Berari buruz esan zuten", "Esaten da", "aipamen", "Esteka", "ikuskapen", "Liburu ospetsuenak", "Kanpoko Tads", "Kanpoko konexioak", "Iturriak:", "Berari buruz", "Dependatutako kontsultak", "Kontsulta", "Bosbing", "Filmografia", "harentzat", "O", "Kanpoko estekak", "Plays", "oharra", "Berari buruz esan zen", "Zeregin", "Plays", "-en gainean", "Beste proiektu batzuk", "Ei buruz", "haren inguruan", "Baliabide", "Kanpoko esteka", "erreferentziak", "Iturriak", "Berari buruz esan zen", "Oharrak", "Kanpoko erreferentziak", "Lotutako elementuak", "Iturri", "Oharrak:", "Estekak", "Berarentzat", "Oheratu", "Testigantzak", "Ohar", "esan", "Baliabideak Interneten", "Ikusi ere", "Daveoù", "Kanpotik estekatu", "Berari buruz", "ikusi ere", "mintz", "-en gainean", "Erreferentziak", "Esan zuten O.", "Lotinduta", "Kanpoko esteka", "Adierazpenak", "ei buruz", "Goiko aipuak", "iturriak", "Enbaxadore", "Esan dio", "literatura", "bere buruari buruz", "Kanpoko estekak", "Lotutako aplikazioak", "Aipamenak", "Ikusi", "-en gainetik", "Gehiegizko estekak", "aipamenak buruz", "Filmografia", "oharra", "iturriak", "baliabide", "Beste proiektu batzuk", "Kanpoko estekak", "estekak", "ohar", "ohar", "Weblinkak", "Bibliografia", "Lotutako elementuak", "zeregin", "erreferentziak", "literatura", "ikusi", "ikusi ere", "oharra", "Beste proiektu batzuk"] forbidden_by_language["fa"] = ["دربارهٔ او", "پیوند به بیرون", "جستارهای وابسته", "منبع\u200cدار", "منابع", "پیوند به\u200cبیرون", "همچنین نگاه کن", "کتابشناسی - فهرست کتب", "آثار", "قابل توجه", "مرجع", "یکی دیگر در مورد آن", "در سالگرد", "آنها درباره آن گفتند", "فیلمنامه نویسی", "گفتن به", "پیوندها", "آنها درباره او گفتند", "در مورد آنها گفته شده است", "نقل قول", "پیوند به", "ارجاع", "مشهورترین کتاب ها", "بیرونی", "اتصالات خارجی", "منابع:", "درباره ی او", "پرسش های وابسته", "ارجاع", "دوبله", "فیلمنامه نویسی", "برای او", "o", "لینک های خارجی", "نمایشنامه", "پاورقی", "در مورد او گفته شد", "آثار", "نمایشنامه", "بر", "پروژه های دیگر", "در باره", "در مورد او", "منابع", "لینک خارجی", "منابع", "منابع", "در مورد او گفته شد", "پانویسها و منابع", "منابع خارجی", "آیتم های مرتبط", "منبع", "یادداشت:", "پیوندها", "برای او", "منتشر شده", "توصیفات", "یادداشت", "گفتن", "منابع در اینترنت", "همچنین ببینید", "daveoù", "پیوند به خارج", "درباره ی او", "همچنین ببینید", "فیلم", "بر", "منابع", "آنها گفتند O.", "مربوط", "لینک خارجی", "اظهارات در مورد", "در باره", "نقل قول بالا", "منابع", "سفیر", "به او گفته شده است", "ادبیات", "درباره خودش", "لینک های خارجی", "برنامه های مرتبط", "نقل قول با توجه به", "دیدن", "بر فراز", "لینک های اضافی", "نقل قول در مورد", "فیلمنامه نویسی", "پاورقی", "منابع", "منابع", "پروژه های دیگر", "لینک های خارجی", "پیوندها", "یادداشت", "توجه داشته باشید", "weblinks", "کتابشناسی - فهرست کتب", "آیتم های مرتبط", "آثار", "منابع", "ادبیات", "دیدن", "همچنین ببینید", "پاورقی", "پروژه های دیگر"] forbidden_by_language["es"] = ["citas sobre", "filmografía", "nota al pie", "fuentes", "recursos", "otros proyectos", "enlaces externos", "enlaces", "notas", "nota", "enlaces web", "bibliografía"," artículos relacionados"," obras"," referencias"," literatura"," ver"," ver también"," nota al pie"," otros proyectos"," Mirar también"," Bibliografía"," obras", "Notable", "referencia", "Otro sobre eso", "en el aniversario", "Ellos dijeron al respecto", "Filmografía", "Diciendo a", "Enlaces", "Ellos dijeron sobre él", "Son dijo sobre"," citas"," Enlace a"," referencias"," Los libros más famosos"," Tads externos"," Conexiones externas"," Fuentes:"," sobre él"," consultas dependientes"," Referencia"," Doblaje"," filmografía"," para él"," O"," Enlaces externos"," obras de teatro"," nota al pie"," se dijo sobre él"," Obras"," Obras de teatro"," sobre"," Otros proyectos"," Acerca de"," Acerca de ella"," Recursos"," Enlace externo"," Referencias"," Fuentes"," Se dijo sobre ella"," Notas al pie"," Referencias externas", "Artículos relacionados", "Fuente", "Notas:", "Enlaces", "Para ella", "Lanzamientos", "Testimonios", "No es"," decir"," recursos en Internet"," Ver también"," daveoù"," Enlace con el exterior"," Acerca de él"," ver también"," película"," sobre"," Referencias", "Dijeron O.", "Relacionado", "Enlace externo", "Declaraciones sobre", "Sobre", "Citas arriba", "Fuentes", "Embajador", "Se le dice a él", "Literatura", "sobre ella", "enlaces externos", "Aplicaciones relacionadas", "Citas con respecto a", "Ver", "sobre", "Enlaces en exceso", "Citas sobre", "filmografía", "nota al pie", " fuentes"," recursos"," otros proyectos"," enlaces externos"," enlaces"," notas"," nota"," enlaces web"," bibliografía"," artículos relacionados"," obras"," referencias", "literatura", "ver", "ver también", "nota al pie", "otros proyectos"] forbidden_by_language["fi"] = ["lainaukset aiheesta","Aiheesta muualla" , "filmografia", "alaviite", "lähteet", "resurssit", "muut projektit", "ulkoiset linkit", "linkit", "muistiinpanot", "huomautus", "weblinks", "bibliografia", "liittyvät kohteet", "teokset", "viitteet", "kirjallisuus", "katso", "katso myös", "alaviite", "muut projektit", "katso myös", "Bibliografia", "teokset", "Huomattava", "viite", "Toinen siitä", "juhlapäivänä", "he sanoivat siitä", "Filmografia", "Sanominen", "linkit", "He sanoivat hänestä", "Ovatko sanoi aiheesta", "lainaukset", "Linkki", "viittaukset", "kuuluisimmat kirjat", "Ulkoiset", "Ulkoiset yhteydet", "Lähteet:", "Hänestä", "riippuvaiset kyselyt", " Viite", "Kopiointi", "filmografia", "hänelle", "O", "ulkoiset linkit", "näytelmät", "alaviite", "hänestä sanottiin", "teokset", "näytelmät", " upon", "Muut projektit", "Tietoja", "Hänestä", "Resurssit", "Ulkoinen linkki", "viitteet", "Lähteet", "Hänestä sanottiin", "alaviitteet", "Ulkoiset viitteet", "Aiheeseen liittyvät kohteet", "Lähde", "Huomautukset:", "Linkit", "Hänelle", "Julkaisut", "Lausunnot", "Ei es", "sano", "resurssit Internetissä", "Katso myös", "daveoù", "Linkki ulkopuolelta", "Tietoa hänestä", "katso myös", "elokuva", "päällä", "viitteet", "He sanoivat O.", "Aiheeseen liittyvä", "ulkoinen linkki", "Lausunnot aiheesta", "Tietoja", "Yllä olevat lainaukset", "lähteet", "suurlähettiläs", "Hänelle sanotaan", "kirjallisuus", "itsestään", "ulkoiset linkit", "Aiheeseen liittyvät sovellukset", "Lainaukset suhteessa", "Katso", "yli", "Ylimääräiset linkit", "lainauksia", "filmografia", "alaviite", " lähteet", "resurssit", "muut projektit", "ulkoiset linkit", "linkit", "muistiinpanot", "huomautus", "verkkolinkit", "bibliografia", "liittyvät kohteet", "teokset", "viitteet", "kirjallisuus", "katso", "katso myös", "alaviite", "muut hankkeet"] forbidden_by_language["fr"] = ["citations sur", "filmographie", "note de bas de page", "sources", "ressources", "autres projets", "liens externes", "liens", "notes", "note", "liens web", "bibliogprahie", "éléments liés", "œuvres", "références", "littérature", "voir", "voir aussi", "note de bas de page", "autres projets", "Regarder aussi", "Bibliographie", "œuvres", "Remarquable", "référence", "Un autre à ce sujet", "à l'anniversaire", "ils en ont dit", "Filmographie", "En disant à", "liens", "Ils ont dit à propos de lui", "Sont dit à propos de", "citations", "Lien vers", "références", "Les livres les plus célèbres", "Tads externes", "Connexions externes", "Sources :", "à propos de lui", "requêtes dépendantes", " Référence", "Doublage", "filmographie", "pour lui", "O", "Liens externes", "pièces", "note de bas de page", "on a dit de lui", "Travaux", "Joues", " sur", "Autres projets", "À propos", "à propos d'elle", "Ressources", "Lien externe", "Références", "Sources", "On a dit d'elle", "Notes de bas de page", "Références externes", "Articles associés", "Source", "Notes :", "Liens", "Pour elle", "Releases", "Témoignages", "Non es", "dire", "ressources sur Internet", "Voir aussi", "daveoù", "Lien vers l'extérieur", "A propos de lui", "voir aussi", "film", "sur", "Références", "Ils ont dit O.", "Connexe", "lien externe", "Déclarations sur", "à propos", "Citations ci-dessus", "sources", "Ambassadeur", "On lui dit", "littérature", "à propos d'elle-même", "liens externes", "Applications associées", "Citations concernant", "Voir", "over", "Liens excédentaires", "Citations sur", "filmographie", "note de bas de page", " sources", "ressources", "autres projets", "liens externes", "liens", "notes", "note", "liens web", "bibliographie", "éléments associés", "ouvrages", "références", "littérature", "voir", "voir aussi", "note de bas de page", "autres projets"] forbidden_by_language["he"] = ["ציטוטים על", "פילמוגרפיה", "הערת שוליים", "מקורות", "משאבים", "פרויקטים אחרים", "קישורים חיצוניים", "קישורים", "הערות", "הערה", "קישורי אינטרנט", "ביבליוגפרה'", "פריטים קשורים", "עבודות", "הפניות", "ספרות", "ראה", "ראה גם", "הערת שוליים", "פרויקטים אחרים", "הסתכל גם על", "ביבליוגרפיה", "עבודות", "ראוי לציון", "התייחסות", "עוד על זה", "ביום השנה", "אמרו על זה", "פילמוגרפיה", "אומרים ל", "קישורים", "אמרו עליו", "האם אמר על", "ציטוטים", "קישור אל", "הפניות", "'הספרים המפורסמים ביותר", "תקשורים חיצוניים", "חיבורים חיצוניים", "מקורות:", "עליו", "שאילתות תלויות", " הפניה", "דיבוב", "פילמוגרפיה", "בשבילו", "O", "קישורים חיצוניים", "הצגות", "הערת שוליים", "אמרו עליו", "עבודות", "מחזות", " על", "פרויקטים אחרים", "אודות", "עליה", "משאבים", "קישור חיצוני", "הפניות", "מקורות", "נאמר עליה", "הערות שוליים", "הפניות חיצוניות", "פריטים קשורים", "מקור", "הערות:", "קישורים", "בשבילה", "פרסומים", "המלצות", "לא es", "אמר", "משאבים באינטרנט", "ראה גם", "daveoù", "קישור אל החוץ", "אודותיו", "ראה גם", "סרט", "על", "הפניות", "הם אמרו O", "קשורים", "קישור חיצוני", "הצהרות על", "על", "ציטוטים למעלה", "מקורות", "שגריר", "נאמר לו", "ספרות", "על עצמה", "קישורים חיצוניים", "יישומים קשורים", "ציטוטים ביחס ל", "ראה", "מעל", "קישורים עודפים", "ציטוטים על", "פילמוגרפיה", "הערת שוליים", " מקורות", "משאבים", "פרויקטים אחרים", "קישורים חיצוניים", "קישורים", "הערות", "הערה", "קישורי אינטרנט", "ביבליוגרפיה", "פריטים קשורים", "עבודות", "הפניות", "ספרות", "ראה", "ראה גם", "הערת שוליים", "פרויקטים אחרים"] forbidden_by_language["hi"] = ["के बारे में उद्धरण", "फिल्मोग्राफी", "फुटनोट", "स्रोत", "संसाधन", "अन्य परियोजनाएं", "बाहरी लिंक", "लिंक", "नोट्स", "नोट", "वेबलिंक", "ग्रंथ सूची", "संबंधित आइटम", "कार्य", "संदर्भ", "साहित्य", "देखें", "यह भी देखें", "फुटनोट", "अन्य परियोजनाएं", "भी देखें", "ग्रंथ सूची", "काम करता है", "उल्लेखनीय", "संदर्भ", "इसके बारे में एक और", "वर्षगांठ में", "उन्होंने इसके बारे में कहा", "फिल्मोग्राफी", "सेइंग टू", "लिंक्स", "उन्होंने उसके बारे में कहा", "हैं के बारे में कहा", "उद्धरण", "लिंक टू", "रेफ़रल", "सबसे प्रसिद्ध किताबें", "बाहरी बच्चे", "बाहरी कनेक्शन", "स्रोत:", "उसके बारे में", "आश्रित प्रश्न", " संदर्भ", "डबिंग", "फिल्मोग्राफी", "उसके लिए", "ओ", "बाहरी लिंक", "नाटक", "फुटनोट", "उसके बारे में कहा गया", "काम करता है", "नाटक", "अन्य प्रोजेक्ट", "अबाउट", "उसके बारे में", "संसाधन", "बाहरी लिंक", "संदर्भ", "स्रोत", "उसके बारे में कहा गया", "फुटनोट", "बाहरी संदर्भ", "संबंधित आइटम", "स्रोत", "नोट्स:", "लिंक", "उसके लिए", "रिलीज़", "प्रशंसापत्र", "नहीं es", "कहते हैं", "इंटरनेट में संसाधन", "यह भी देखें", "डेवो", "बाहर से लिंक करें", "उसके बारे में", "यह भी देखें", "फिल्म", "पर", "संदर्भ", "उन्होंने कहा ओ", "संबंधित", "बाहरी लिंक", "बयानों के बारे में", "के बारे में", "उपरोक्त उद्धरण", "स्रोत", "राजदूत", "यह उसे कहा जाता है", "साहित्य", "अपने बारे में", "बाहरी लिंक", "संबंधित अनुप्रयोग", "के संबंध में उद्धरण", "देखें", "ओवर", "अतिरिक्त लिंक", "उद्धरण के बारे में", "फिल्मोग्राफी", "फुटनोट", " स्रोत", "संसाधन", "अन्य परियोजनाएं", "बाहरी लिंक", "लिंक", "नोट्स", "नोट", "वेबलिंक", "ग्रंथ सूची", "संबंधित आइटम", "कार्य", "संदर्भ", "साहित्य", "देखें", "यह भी देखें", "फुटनोट", "अन्य परियोजनाएं"] forbidden_by_language["hr"] = ["navodnici o", "filmografija", "fusnota", "izvori", "izvori", "drugi projekti", "vanjske veze", "veze", "bilješke", "napomena", "weblinks", "bibliografija", "srodne stavke", "radovi", "reference", "literatura", "vidi", "vidi također", "fusnota", "drugi projekti", "također pogledajte", "Bibliografija", "radovi", "Zapaženo", "referenca", "Još jedan o tome", "u obljetnici", "rekli su o tome", "Filmografija", "Kaže se", "linkovi", "Rekli su o njemu", "Jesu li rekao o", "citati", "Veza na", "preporuke", "Najpoznatije knjige", "Vanjski tad", "Vanjske veze", "Izvori:", "o njemu", "ovisni upiti", " Referenca", "Sinhronizacija", "filmografija", "za njega", "O", "Vanjske veze", "predstave", "fusnota", "rečeno je o njemu", "Djela", "Predstave", " na", "Drugi projekti", "O njoj", "O njoj", "Resursi", "Vanjski link", "reference", "Izvori", "Rečeno je o njoj", "fusnote", "Vanjske reference", "Povezane stavke", "Izvor", "Napomene:", "Veze", "Za nju", "Izdanja", "Izjave", "Ne es", "recimo", "resursi na Internetu", "Vidi također", "daveoù", "Veza prema van", "O njemu", "vidi također", "film", "on", "Reference", "Rekli su O.", "Povezano", "vanjska veza", "Izjave o", "o", "Navodi gore", "izvori", "Ambasador", "Rečeno mu je", "književnost", "o sebi", "vanjske veze", "Povezane aplikacije", "Citati s obzirom na", "Vidi", "preko", "Višak veza", "citati o", "filmografija", "fusnota", " izvori", "resursi", "ostali projekti", "vanjske veze", "veze", "bilješke", "bilješka", "web-veze", "bibliografija", "srodne stavke", "radovi", "reference", "književnost", "vidi", "vidi također", "fusnota", "drugi projekti"] forbidden_by_language["is"] = ["tilvitnanir um", "kvikmyndafræði", "neðanmálsgrein", "heimildir", "auðlindir", "önnur verkefni", "ytri tenglar", "tenglar", "aths", "ath", "weblinks", "heimildaskrá", "tengd atriði", "verk", "tilvísanir", "bókmenntir", "sjá", "sjá einnig", "neðanmálsgrein", "önnur verkefni", "Skoðaðu líka", "Heimildaskrá", "verk", "Athyglisvert", "tilvísun", "Annað um það", "í afmælinu", "þeir sögðu um það", "Kvikmyndataka", "Seggja við", "tenglar", "Þeir sögðu um hann", "Eru sagði um", "tilvitnanir", "Tengill á", "tilvísanir", "Frægustu bækurnar", "Ytri tads", "Ytri tengingar", "Heimildir:", "um hann", "háðar fyrirspurnir", " Tilvísun", "talsetning", "kvikmyndataka", "fyrir hann", "O", "Ytri hlekkir", "leikrit", "neðanmálsgrein", "það var sagt um hann", "verk", "leikrit", " á", "Önnur verkefni", "Um", "um hana", "Auðlindir", "Ytri tengill", "tilvísanir", "Heimildir", "Það var sagt um hana", "neðanmálsgrein", "Ytri tilvísanir", "Tengd atriði", "Heimild", "Athugasemdir:", "Tenglar", "Fyrir hana", "Útgáfur", "Vitnisburður", "Ekki es", "segja", "tilföng á internetinu", "Sjá líka", "daveoù", "Tengill að utan", "Um hann", "sjá líka", "kvikmynd", "on", "Tilvísanir", "Þeir sögðu O.", "Tengd", "ytri tengill", "Yfirlýsingar um", "um", "Tilvitnanir að ofan", "heimildir", "sendiherra", "það er sagt við hann", "bókmenntir", "um sjálfa sig", "ytri tenglar", "Tengd forrit", "Tilvitnanir með tilliti til", "Sjá", "yfir", "Umframtenglar", "tilvitnanir um", "kvikmyndafræði", "neðanmáls", " heimildir", "tilföng", "önnur verkefni", "ytri hlekkir", "tenglar", "athugasemdir", "aths", "veftenglar", "heimildaskrá", "tengd atriði", "verk", "tilvísanir", "bókmenntir", "sjá", "sjá einnig", "neðanmálsgrein", "önnur verkefni"] forbidden_by_language["it"] = ["citazioni su", "filmografia", "nota", "fonti", "risorse", "altri progetti", "link esterni", "link", "note", "nota", "link web", "bibliografia", "articoli correlati", "opere", "riferimenti", "letteratura", "vedi", "vedi anche", "nota a piè di pagina", "altri progetti", "guarda anche", "bibliografia", "lavori", "Notevole", "riferimento", "Un altro a riguardo", "nell'anniversario", "hanno detto a riguardo", "Filmografia", "Detto a", "link", "Hanno detto di lui", "Sono ha detto su", "citazioni", "Link a", "riferimenti", "I libri più famosi", "Schede esterne", "Connessioni esterne", "Fonti:", "su di lui", "domande dipendenti", " Riferimento", "Doppiaggio", "filmografia", "per lui", "O", "Link esterni", "ascolta", "nota a piè di pagina", "si diceva di lui", "Lavori", "Riproduzioni", " su", "Altri progetti", "Su", "su di lei", "Risorse", "Link esterno", "riferimenti", "Fonti", "Si diceva di lei", "note a piè di pagina", "Riferimenti esterni", "Articoli correlati", "Fonte", "Note:", "Link", "Per lei", "Pubblicazioni", "Testimonianze", "Non es", "say", "risorse in Internet", "Vedi anche", "daveoù", "Link all'esterno", "Su di lui", "vedi anche", "film", "on", "Riferimenti", "Hanno detto O.", "Correlato", "link esterno", "Dichiarazioni su", "su", "Citazioni sopra", "fonti", "Ambasciatore", "Si dice a lui", "letteratura", "su di sé", "link esterni", "applicazioni correlate", "citazioni rispetto a", "vedere", "sopra", "collegamenti in eccesso", "citazioni su", "filmografia", "nota a piè di pagina", " fonti", "risorse", "altri progetti", "link esterni", "link", "note", "nota", "link web", "bibliografia", "articoli correlati", "lavori", "riferimenti", "letteratura", "vedi", "vedi anche", "nota a piè di pagina", "altri progetti"] forbidden_by_language["ja"] = ["引用'、 '脚注'、 'ソース'、 'リソース'、 'その他のプロジェクト'、 '外部リンク'、 'リンク'、 'ノート'、 '注'、 'ウェブリンク'、 '参考文献' ' '、'作品 '、'参考文献 '、'文学 '、'参照 '、'関連項目 '、'脚注 '、'その他のプロジェクト '、'関連項目 '、'参考文献 '、'作品 ' 、 '注目すべき'、 '参照'、 'それについての別の'、 '記念日'、 '彼らはそれについて言った'、 '映画誌'、 '言っている'、 'リンク'、 '彼らは彼について言った'、 ' '、'引用符 '、'リンク先 '、'参照 '、'最も有名な本 '、'外部接続 '、'外部接続 '、'出典： '、'彼について '、'依存クエリ '、'参照 '、'ダビング '、'フィルムグラフィー '、'彼のために '、' O '、'外部リンク '、'演劇 '、'脚注 '、'彼について言われた '、'作品 '、'演劇 '、' '、'その他のプロジェクト '、'について '、'彼女について '、'リソース '、'外部リンク '、'参照 '、'ソース '、'彼女について言われた '、'脚注 '、'外部参照 ' 、 '関連項目'、 'ソース'、 '注：'、 'リンク'、 '彼女のために'、 'リリース'、 '証言","脚注'、 '言う'、 'インターネットのリソース'、 '関連項目' 、、 '外部へのリンク'、 '彼について'、 '関連項目'、 '映画'、 '参考文献'、 '参照はO '、'リソース'、 '映画リンク'、 'リンク'、 '注'、 '注'、 'レリンク'、 '参照参照'、 '化参照'、 '作品 '、'参照 '、'文献 '、'参照 '、'関連項目 '、'脚注 '、'その他のプロジェクト "] forbidden_by_language["ka"] = ["ციტატები", "ფილმოგრაფია", "სქოლიო", "წყაროები", "რესურსები", "სხვა პროექტები", "გარე ბმულები", "ბმულები", "შენიშვნები", "შენიშვნა", "ვებლინკები", "ბიბლიოგრაფია'", "დაკავშირებული ერთეულები", "ნამუშევრები", "ცნობები", "ლიტერატურა", "იხილეთ", "ასევე იხილეთ", "სქოლიო", "სხვა პროექტები", "ასევე შეხედე", "ბიბლიოგრაფია", "ნამუშევრები' , „აღსანიშნავი“, „მინიშნება“, „კიდევ ერთი ამის შესახებ“, „იუბილეზე“, „ამის შესახებ თქვეს“, „ფილმოგრაფია“, „ამბობენ“, „ბმულები“, „მასზე თქვეს“, „არის. ნათქვამია შესახებ", "ციტატები", "ბმული", "რეფერატები", "ყველაზე ცნობილი წიგნები", "გარე ბავშვები", "გარე კავშირები", "წყაროები:", "მის შესახებ", "დამოკიდებული შეკითხვები", " მითითება“, „დუბლირება“, „ფილმოგრაფია“, „მისთვის“, „ო“, „გარე ბმულები“, „სპექტაკლები“, „სქოლიო“, „მასზე ითქვა“, „ნამუშევრები“, „სპექტაკლები“, „ საფუძველზე", "სხვა პროექტები", "შესახებ", "მის შესახებ", "რესურსები", "გარე ბმული", "ცნობები", "წყაროები", "ითქვა მის შესახებ", "სქოლიოები", "გარე ცნობები", "დაკავშირებული ნივთები", "წყარო", "შენიშვნები:", "ბმულები", "მისთვის", "გამოშვებები", "ჩვენებები", "N ოტესები", "ვთქვათ", "რესურსები ინტერნეტში", "ასევე იხილეთ", "დავეოუ", "გარედან ბმული", "მის შესახებ", "ასევე იხილეთ", "ფილმი", "ჩართული", "ცნობები", "მათ თქვეს ო.", "დაკავშირებული", "გარე ბმული", "განცხადებები", "შესახებ", "ციტატები ზემოთ", "წყაროები", "ელჩი", "მას უთხრეს", "ლიტერატურა", "თავის შესახებ", "გარე ბმულები", "დაკავშირებული აპლიკაციები", "ციტატები დაკავშირებით", "იხილეთ", "ზედა", "ჭარბი ბმულები", "ციტატები", "ფილმოგრაფია", "სქოლიო", " წყაროები", "რესურსები", "სხვა პროექტები", "გარე ბმულები", "ბმულები", "შენიშვნები", "შენიშვნა", "ვებლინკები", "ბიბლიოგრაფია", "დაკავშირებული ერთეულები", "ნამუშევრები", "ცნობები", "ლიტერატურა", "იხილეთ", "ასევე იხილეთ", "სქოლიო", "სხვა პროექტები"] forbidden_by_language["pl"] = ['zobacz też', 'o ', 'Zasoby', 'Wydanie', 'o nim', 'Link do zewnątrz', 'Cytaty w odniesieniu do', 'Bibliografia', 'Najbardziej znane książki', 'powiedzieli o tym', 'Powiedziane są o', 'Powiązane przedmioty', 'na', 'spinki do mankietów', 'Powiązane zastosowania', 'referencja', 'Powiedzieli o nim', 'Również patrzeć', 'Pracuje', 'literatura', 'Link zewnętrzny', 'Referencje.', 'Bibliografia', 'zależało zapytania', 'Daveoù.', 'Powiedział o niej', 'Spinki do mankietów', 'Pracuje', 'Uwagi:', 'Dubbing.', 'przypisy', 'Widzieć', 'Mówiono o nim', 'o niej', 'Ambasador', 'cytaty', 'bawić się', 'film', 'O.', 'Filmografia', 'O nim', 'Związane z', 'Zewnętrzne odniesienia', 'Cytaty powyżej', 'link zewnętrzny', 'Bibliografia', 'Inne projekty', 'Filmografia', 'Outer Tads.', 'Źródło', 'Zewnętrzne linki', 'Zasoby w Internecie.', 'notatka', 'Zobacz też', 'Referencja', 'Powiedzieli O.', 'Notatki', 'Dla niej', 'Znaczny', 'nad', 'Mówi się mu', 'Nadmiarowe linki', 'o', 'O sobie', 'Bawić się', 'ŹRÓDŁA', 'mowić', 'Inny o tym', 'Mówiąc do', 'Połączenia zewnętrzne', 'Zobacz też', 'od', 'O', 'w rocznicy.', 'Łączyć z', 'skierowania', 'dla niego', 'Źródła:', 'Oświadczenia o', 'ŹRÓDŁA', 'Zewnętrzne linki', 'cytaty', 'Filmografia', 'notatka', 'ŹRÓDŁA', 'Surowce', 'inne projekty', 'Zewnętrzne linki', 'spinki do mankietów', 'notatki', 'Notatka', 'linki internetowe', 'bibliografia', 'powiązane przedmioty', 'Pracuje', 'Bibliografia', 'literatura', 'zobaczyć', 'Zobacz też', 'notatka', 'inne projekty'] forbidden_by_language["pt"] = ["Ligações externas","citações sobre ele", "citações sobre ela", "filmografia", "nota de rodapé", "fontes", "recursos", "outros projetos", "links externos", "links", "notas", "nota", "links da web", "bibliografia", "itens relacionados", "obras", "referências", "literatura", "ver", "ver também", "nota de rodapé", "outros projetos" , "Veja também", "Bibliografia", "obras", "Notável", "Referência", "Outra sobre isso", "no aniversário", "foi dito sobre ela", "Filmografia", "Dizendo a "," links "," Disseram sobre ele "," Dizem sobre "," Link para "," referências "," Os livros mais famosos "," Meninos de fora "," Conexões externas "," Fontes: ", "sobre ele", "consultas dependentes", "Referência", "Dublagem", "filmografia", "para ele", "O", "Ligações externas", "peças", "nota de rodapé", "foi falado sobre ele "," Funciona "," Joga "," sobre "," Outros projetos "," Sobre "," sobre ela "," Recursos "," Link externo "," Referências "," Fontes "," Foi dito sobre ela "," notas de rodapé "," Referências externas "," Itens relacionados "," Fonte "," Notas: "," Link s "," Releases "," Notes "," resources in Internet "," See also "," daveoù "," Link to the outside "," About him "," see also "," film ", "Referências", "Disseram sobre ele", "Relacionadas", "link externo", "Declarações sobre" , "Citações acima", "fontes", "Embaixador", "Diz-se sobre ele", "literatura "," Disseram sobre ela "," links externos "," Aplicativos relacionados "," Citações a respeito de "," Ver ", " sobre "," Excesso de links "," citações sobre "," filmografia "," nota de rodapé "," fontes "," recursos "," outros projetos "," links externos "," links "," notas "," nota "," links da web "," bibliografia "," itens relacionados "," trabalhos "," referências "," literatura "," ver "," ver também "," nota de rodapé "," outros projetos "] forbidden_by_language["ro"] = ['legături externe', 'despre', 'NOTE:', 'literatură', 'sa spus despre el', 'despre el', 'Dobbing.', 'Pentru ea', 'Se spune despre', 'Articole conexe', 'Notabil', 'Notele de subsol', 'Aplicații înrudite', 'Filmografie', 'Surse:', 'depinde de interogări', 'Referințe externe', 'Au spus despre el', 'Alte proiecte', 'Vedea', 'Uitați de asemenea la', 'Filmografie', 'Despre', 'pe', 'Legate de', 'O.', 'Ambasador', 'joacă', 'referinţă', 'pentru el', 'TADS OUTER.', 'Bibliografie', 'linkuri externe', 'În aniversare', 'Link-uri', 'Releases.', 'despre ea însăși', 'Link-uri', 'lucrări', 'Referinţă', 'Declarații despre', 'Vezi si', 'Cele mai cunoscute cărți', 'Lucrări', 'Sa spus despre ea', 'Link-uri excesive', 'citate', 'Link-ul la exterior', 'Sursă', 'Altul despre el', 'Spunând', 'film', 'Citate cu privire la', 'Spune', 'Daveoù.', 'Link extern', 'Citări de mai sus', 'Vezi si', 'peste', 'Surse.', 'Îi se spune', 'Au spus O.', 'Referințe', 'despre', 'peste', 'Legătura cu', 'Joacă', 'Referințe', 'despre ea', 'Surse.', 'linkuri externe', 'Au spus despre asta', 'Link extern', 'Mărturii', 'notă de subsol', 'Referințe', 'Note', 'Resurse pe Internet', 'Despre el', 'Resurse', 'Conexiuni externe', 'Citate despre', 'Filmografie', 'notă de subsol', 'Surse.', 'resurse', 'Alte proiecte', 'linkuri externe', 'Link-uri', 'note', 'Notă', 'Link-uri web', 'bibliografie', 'Articole conexe', 'lucrări', 'Referințe', 'literatură', 'vedea', 'Vezi si', 'notă de subsol', 'Alte proiecte'] forbidden_by_language["ru"] = ['Об ', 'Фильмография', 'примечания', 'ссылки ', 'см. также', 'Примечания:', 'литература', 'Было сказано о нем', 'о нем', 'Дублировка', 'Для нее', 'Говорится о', 'Похожие материалы', 'нотенно', 'сноски', 'Похожие приложения', 'Фильмография', 'Источники:', 'Взял запросы', 'Внешние ссылки', 'Они сказали о нем', 'Другие проекты', 'Видеть', 'Также смотрите', 'фильмография', 'О', 'на', 'Связанный', 'О', 'Посол', 'пьесы', 'ссылка', 'для него', 'Внешние тады', 'Библиография', 'внешние ссылки', 'в годовщине', 'Ссылки', 'Релизы', 'о себе', 'ссылки', 'работает', 'Ссылка', 'Утверждение о', 'смотрите также', 'Самые известные книги', 'Работает', 'Было сказано о ней', 'Избыточные ссылки', 'Ссылка на улицу', 'Источник', 'Другой об этом', 'Говорить', 'пленка', 'Цитаты по отношению к', 'сказать', 'Daveoù.', 'Внешняя ссылка', 'Цитаты выше', 'Смотрите также', 'над', 'Источники', 'Это сказано ему', 'Они сказали О.', 'использованная литература', 'о', 'на', 'Ссылка на', 'Пьесы', 'рефералы', 'о ней', 'источники', 'внешние ссылки', 'Они сказали об этом', 'внешняя ссылка', 'Отзывы', 'сноска', 'использованная литература', 'Примечания', 'Ресурсы в интернете', 'О нем', 'Ресурсы', 'Внешние соединения', 'цитаты о', 'фильмография', 'сноска', 'источники', 'Ресурсы', 'другие проекты', 'внешние ссылки', 'ссылки', 'Примечания', 'Примечание', 'веб ссылки', 'Библиография', 'Похожие материалы', 'работает', 'использованная литература', 'литература', 'видеть', 'смотрите также', 'сноска', 'другие проекты'] forbidden_by_language["sk"] = ['Povedali o', 'iné projekty', 'referencie', 'Poznámky:', 'literatúra', 'Hovorilo sa o ňom', 'o ňom', 'Dabovanie', 'Pre ňu', 'Hovoria', 'Súvisiace položky', 'Pozoruhodný', 'poznámky pod čiarou', 'Súvisiace aplikácie', 'Filmograf', 'Zdroje:', 'závislé dotazy', 'Externé referencie', 'Povedali o ňom', 'Ostatné projekty', 'Pozrieť sa', 'Pozrite sa aj na', 'filmograf', 'O', 'zapnutý', 'Súvisiaci', 'O', 'Veľvyslanec', 'hrať', 'referencia', 'pre neho', 'Vonkajšie tads', 'Bibliografia', 'vonkajšie odkazy', 'v výročnom', 'Spojenie', 'Vydania', 'o sebe', 'spojenie', 'Tvorba', 'Referencia', 'Vyhlásenia', 'pozri tiež', 'Najznámejšie knihy', 'Tvorba', 'Povedala sa o ňom', 'Prebytočné odkazy', 'citácie', 'Odkaz na vonkajšiu stranu', 'Zdroj', 'O tom', 'Hovoriť', 'film', 'Citáty s ohľadom na', 'povedať', 'daveoù', 'Externý odkaz', 'Vyššie uvedené citácie', 'Pozri tiež', 'nad', 'Zdroje', 'Hovorí sa mu', 'Povedali o.', 'Referencie', 'o', 'na', 'Odkaz na', 'Hrať', 'referencie', 'o nej', 'zdroje', 'vonkajšie odkazy', 'Povedali o tom', 'externý odkaz', 'Referencie', 'poznámka pod čiarou', 'referencie', 'Poznámky', 'Zdroje na internete', 'O ňom', 'Prostriedky', 'Externé pripojenia', 'cituje', 'filmograf', 'poznámka pod čiarou', 'zdroje', 'prostriedky', 'Ostatné projekty', 'vonkajšie odkazy', 'spojenie', 'poznámky', 'Poznámka', 'weblinks', 'Bibliografia', 'Súvisiace položky', 'Tvorba', 'referencie', 'literatúra', 'pozrieť sa', 'pozri tiež', 'poznámka pod čiarou', 'Ostatné projekty'] forbidden_by_language["sl"] = ['viri', 'sklici', 'Opombe:', 'Literatura.', 'Rečeno je bilo o njem', 'o njem', 'Dubbing.', 'Za njo', 'Rečeno', 'Podobni elementi', 'Opazno', 'Opombe', 'Povezane aplikacije', 'Filmografija', 'Viri:', 'odvisne poizvedbe', 'Zunanje reference', 'Rekli so o njem', 'Drugi projekti', 'Glejte', 'Oglejte si tudi', 'filmografija', 'Približno', 'On.', 'Povezano', 'O.', 'Veleposlanik', 'igra', 'Referenca', 'zanj', 'Zunanji tads.', 'Bibliografija', 'Zunanje povezave', 'V obletnici', 'Povezave', 'Sprosti', 'o sebi', 'Povezave', 'dela', 'Referenca', 'Izjave', 'Poglej tudi', 'Najbolj znane knjige', 'Dela', 'Rečeno je bilo o njej', 'Presežne povezave', 'citate', 'Povezava na zunanjost', 'Vir.', 'Drugo o tem', 'Rekel', 'film', 'Citati v zvezi s tem', 'reči.', 'daveoù.', 'Zunanja povezava', 'Zgoraj', 'Poglej tudi', 'nad', 'Viri', 'Rečeno mu je', 'Rekli so O.', 'Reference', 'približno', 'AN.', 'Povezava do', 'Igra', 'napotitve', 'o njej', 'Viri', 'Zunanje povezave', 'Rekli so o tem', 'Zunanja povezava', 'Pričevanja', 'opomba', 'Reference', 'Opombe', 'Viri na internetu', 'O njem', 'Viri', 'Zunanje povezave', 'navaja', 'filmografija', 'opomba', 'Viri', 'Viri', 'Drugi projekti', 'Zunanje povezave', 'Povezave', 'Opombe', 'Opomba', 'weblinks.', 'Bibliografija', 'Podobni elementi', 'dela', 'Reference', 'Literatura.', 'Glejte', 'Poglej tudi', 'opomba', 'Drugi projekti'] forbidden_by_language["sq"] = ['Thënie për të', 'Referimet', 'Shiko edhe', 'lidhje të jashtme', 'referime', 'Shënime:', 'letërsi', 'U tha për të', 'për të', 'Dublim', 'Për të', 'Janë thënë', 'Artikuj të ngjashëm', 'I dukshëm', 'fusnotat', 'Aplikime të ngjashme', 'Film', 'Burimet:', 'Pyetje të varura', 'Referencat e jashtme', 'Ata thanë për të', 'Projekte të tjera', 'Shiko', 'Gjithashtu shikoni', 'film', 'Rreth', 'në', 'I lidhur', 'O', 'Ambasador', 'luaj', 'referim', 'per atë', 'Tads e jashtme', 'Bibliografi', 'Linqe te jashtme', 'Në përvjetorin', 'Lidhje', 'Liron', 'për veten', 'lidhje', 'vepron', 'Referim', 'Deklaratat rreth', 'Shiko gjithashtu', 'Librat më të famshëm', 'Vepron', 'U tha për të', 'Lidhje të tepërta', 'kuotat', 'Lidhje me pjesën e jashtme', 'Burim', 'Një tjetër për këtë', 'Duke thënë', 'film', 'Kuotat në lidhje me', 'thua', 'daveoù', 'Lidhje e jashtme', 'Citimet e mësipërme', 'Shiko gjithashtu', 'mbi', 'Burime', 'Është thënë atij', 'Ata thanë O.', 'Referencat', 'rreth', 'në', 'Lidh me', 'Luaj', 'referime', 'për të', 'burime', 'Linqe te jashtme', 'ata thanë për këtë', 'lidhje e jashtme', 'Dëshmi', 'shënim shënim', 'referencat', 'Shënim', 'Burimet në Internet', 'Për të', 'Burime', 'Lidhjet e jashtme', 'citon rreth', 'film', 'shënim shënim', 'burime', 'burime', 'Projekte të tjera', 'Linqe te jashtme', 'lidhje', 'shënim', 'shënim', 'weblinks', 'bibliografi', 'Artikuj të ngjashëm', 'vepron', 'referencat', 'letërsi', 'Shiko', 'Shiko gjithashtu', 'shënim shënim', 'Projekte të tjera'] forbidden_by_language["ta"] = ['வெளி இணைப்புகள்', 'சான்றுகள்', 'குறிப்புகள்:', 'இலக்கியம்', 'அது அவரைப் பற்றி கூறப்பட்டது', 'அவரை பற்றி', 'டுபிங்', 'அவளுக்கு', 'பற்றி கூறப்படுகிறது', 'தொடர்புடைய பொருட்கள்', 'குறிப்பிடத்தக்கது', 'அடிக்குறிப்புகள்', 'தொடர்புடைய பயன்பாடுகள்', 'திரைப்படவியல்', 'ஆதாரங்கள்:', 'சார்ந்த கேள்விகள்', 'வெளிப்புற குறிப்புகள்', 'அவர்கள் அவரைப் பற்றி சொன்னார்கள்', 'பிற திட்டங்கள்', 'பார்க்க', 'மேலும் பாருங்கள்', 'திரைப்படவியல்', 'பற்றி', 'மீது', 'தொடர்புடைய', 'ஓ', 'தூதர்', 'நாடகம்', 'குறிப்பு', 'அவருக்கு', 'வெளிப்புற tads.', 'நூலகம்', 'வெளி இணைப்புகள்', 'ஆண்டு விழாவில்', 'இணைப்புகள்', 'வெளியீடுகள்', 'தன்னை பற்றி', 'இணைப்புகள்', 'வேலை', 'குறிப்பு', 'பற்றி அறிக்கைகள்', 'மேலும் காண்க', 'மிகவும் பிரபலமான புத்தகங்கள்', 'வேலை', 'அது அவளைப் பற்றி கூறப்பட்டது', 'அதிக இணைப்புகள்', 'மேற்கோள்கள்', 'வெளியே இணைப்பு', 'மூல', 'அது பற்றி மற்றொரு', 'சொல்லுங்கள்', 'திரைப்படம்', 'மரியாதையுடன் மேற்கோள்கள்', 'சொல்', 'daveoù.', 'வெளிப்புற இணைப்பு', 'மேலே மேற்கோள்கள்', 'மேலும் காண்க', 'மேல்', 'ஆதாரங்கள்', 'அது அவரிடம் கூறப்படுகிறது', 'அவர்கள் ஓ என்று சொன்னார்கள்.', 'குறிப்புகள்', 'பற்றி', 'மீது', 'இணைப்பு', 'நாடகம்', 'பரிந்துரைகளை', 'அவளை பற்றி', 'ஆதாரங்கள்', 'வெளி இணைப்புகள்', 'அவர்கள் அதைப் பற்றி சொன்னார்கள்', 'வெளிப்புற இணைப்பு', 'சான்றுகள்', 'அடிகுறிப்பு', 'குறிப்புகள்', 'குறிப்புகள்', 'இணையத்தில் வளங்கள்', 'அவரை பற்றி', 'வளங்கள்', 'வெளிப்புற இணைப்புகள்', 'மேற்கோள்கள் பற்றி', 'திரைப்படவியல்', 'அடிகுறிப்பு', 'ஆதாரங்கள்', 'வளங்கள்', 'பிற திட்டங்கள்', 'வெளி இணைப்புகள்', 'இணைப்புகள்', 'குறிப்புகள்', 'குறிப்பு', 'weblinks.', 'நூலகம்', 'தொடர்புடைய பொருட்கள்', 'வேலை', 'குறிப்புகள்', 'இலக்கியம்', 'பார்க்க', 'மேலும் காண்க', 'அடிகுறிப்பு', 'பிற திட்டங்கள்'] forbidden_by_language["te"] = ['మూలాలు', 'గమనికలు:', 'సాహిత్యం', 'ఇది అతని గురించి చెప్పబడింది', 'అతని గురించి', 'డబ్బింగ్', 'ఆమె కోసం', 'గురించి చెప్పారు', 'సంబంధిత అంశాలు', 'గుర్తించదగినది', 'ఫుట్నోట్స్', 'సంబంధిత అనువర్తనాలు', 'ఫిల్మోగ్రఫీ', 'సోర్సెస్:', 'వివరించిన ప్రశ్నలు', 'బాహ్య సూచనలు', 'వారు అతని గురించి చెప్పారు', 'ఇతర ప్రాజెక్టులు', 'చూడండి', 'కూడా చూడండి', 'ఫిల్మోగ్రఫీ', 'గురించి', 'పై', 'సంబంధిత', 'O.', 'రాయబారి', 'ప్లేస్', 'సూచన', 'అతనికి', 'ఔటర్ tads.', 'బిబ్లియోగ్రఫీ', 'బాహ్య లింకులు', 'వార్షికోత్సవంలో', 'లింకులు', 'విడుదలలు', 'ఆమె గురించి', 'లింకులు', 'పనిచేస్తుంది', 'సూచన', 'గురించి ప్రకటనలు', 'ఇది కూడ చూడు', 'అత్యంత ప్రసిద్ధ పుస్తకాలు', 'పనిచేస్తుంది', 'ఆమె గురించి చెప్పబడింది', 'అదనపు లింకులు', 'కోట్స్', 'వెలుపల లింక్', 'మూల', 'దాని గురించి మరొకటి', 'చెప్పడం', 'సినిమా', 'సంబంధించి కోట్స్', 'చెప్పండి', 'daveoù.', 'బాహ్య లింక్', 'పైన ఉన్న అనులేఖనాలు', 'ఇది కూడ చూడు', 'పైగా', 'సోర్సెస్', 'అది అతనికి చెప్పబడింది', 'వారు ఓ అన్నారు', 'ప్రస్తావనలు', 'గురించి', 'దీని తరువాత', 'లింక్', 'ప్లేస్', 'రెఫరల్స్', 'ఆమె గురించి', 'సోర్సెస్', 'బాహ్య లింకులు', 'వారు దాని గురించి చెప్పారు', 'బాహ్య లింక్', 'టెస్టిమోనియల్స్', 'ఫుట్నోట్', 'ప్రస్తావనలు', 'గమనికలు', 'ఇంటర్నెట్లో వనరులు', 'అతని గురించి', 'వనరులు', 'బాహ్య కనెక్షన్లు', 'కోట్స్ గురించి', 'ఫిల్మోగ్రఫీ', 'ఫుట్నోట్', 'సోర్సెస్', 'వనరులు', 'ఇతర ప్రాజెక్టులు', 'బాహ్య లింకులు', 'లింకులు', 'గమనికలు', 'గమనిక', 'weblinks.', 'బిబ్లియోగ్రఫీ', 'సంబంధిత అంశాలు', 'పనిచేస్తుంది', 'ప్రస్తావనలు', 'సాహిత్యం', 'చూడండి', 'ఇది కూడ చూడు', 'ఫుట్నోట్', 'ఇతర ప్రాజెక్టులు'] forbidden_by_language["tr"] = ['Hakkında', 'kaynakça', 'Notlar:', 'Edebiyat', 'Onun hakkında söylendi', 'onun hakkında', 'Dublaj', 'Onun için', 'Hakkında söyleniyor', 'İlgili öğeler', 'Dikkate değer', 'dipnotlar', 'İlgili uygulamalar', 'Filmografi', 'Kaynaklar:', 'SORUMLULUKLAR', 'Dış referanslar', 'Onun hakkında söylediler', 'Diğer projeler', 'Görmek', 'Ayrıca bak', 'filmografi', 'Hakkında', 'üzerinde', 'İlgili', 'Ö', 'Büyükelçi', 'oynar', 'referans', 'onun için', 'Dış tads', 'Bibliyografya', 'Dış bağlantılar', 'yıldönümünde', 'Linkler', 'Salıverme', 'kendisi hakkında', 'linkler', 'İşler', 'Referans', 'Hakkında açıklamalar', 'Ayrıca bakınız', 'En ünlü kitaplar', 'İşler', 'Onun hakkında söylendi', 'Aşırı bağlantılar', 'alıntı', 'Dışa bağlantı', 'Kaynak', 'Bunun hakkında başka', 'Söyleyerek', 'film', 'İle ilgili alıntılar', 'söylemek', 'Daveoù', 'Harici bağlantı', 'Yukarıdaki alıntılar', 'Ayrıca bakınız', 'üzerinde', 'Kaynaklar', 'Ona söyleniyor', 'O dediler.', 'Referanslar', 'hakkında', 'üzerine', 'Bağlamak', 'Oynar', 'yönlendirmeler', 'Onun hakkında', 'kaynaklar', 'Dış bağlantılar', 'Bunun hakkında söylediler', 'harici bağlantı', 'Tanıklık', 'dipnot', 'Referanslar', 'Notlar', 'İnternetteki kaynaklar', 'Onun hakkında', 'Kaynaklar', 'Harici Bağlantılar', 'hakkında alıntılar', 'filmografi', 'dipnot', 'kaynaklar', 'Kaynaklar', 'diğer projeler', 'Dış bağlantılar', 'linkler', 'notalar', 'Not', 'İnternet linkleri', 'bibliyografya', 'ilgili öğeler', 'İşler', 'Referanslar', 'Edebiyat', 'görmek', 'Ayrıca bakınız', 'dipnot', 'diğer projeler'] forbidden_by_language["uk"] = ['Про ', 'Джерела', 'примітки', 'література', 'Примітки:', 'література', 'Про це сказано', 'про нього', 'Дублювання', 'Для неї', 'Сказані', "Пов'язані елементи", 'Нотен', 'виноски', "Пов'язані заявки", 'Фільмографія', 'Джерела:', 'залежати від запитів', 'Зовнішні посилання', 'Вони сказали про нього', 'Інші проекти', 'Побачити', 'Також подивіться', 'фільмографія', 'Про', 'на', 'Споріднений', 'O', 'Посла', 'грає', 'довідник', 'для нього', 'Зовнішні tads', 'Бібліографія', 'зовнішні посилання', 'у річницю', 'Посилання', 'Релізи', 'про себе', 'посилання', 'робіт', 'Довідник', 'Заяви про', 'Дивись також', 'Найвідоміші книги', 'Робіт', 'Це було сказано про неї', "Надлишкові зв'язки", 'котирування', 'Посилання назовні', 'Джерело', 'Інше про це', 'Кажуть', 'плівка', 'Цитати по відношенню до', 'казати', 'дав', 'Зовнішня посилання', 'Цитати вище', 'Дивись також', 'надмірно', 'Джерела', 'Йому сказано', 'Вони сказали О.', 'Посилання', 'про', 'на', 'Посилання на', 'Грає', 'рефераль', 'про неї', 'джерела', 'зовнішні посилання', 'вони сказали про це', 'Зовнішня посилання', 'Відгуки', 'виноска', 'посилання', 'Ноти', 'Ресурси в Інтернеті', 'Про нього', 'Ресурси', "Зовнішні з'єднання", 'фільмографія', 'виноска', 'джерела', 'ресурси', 'Інші проекти', 'зовнішні посилання', 'посилання', 'ноти', 'Примітка', 'weblinks', 'бібліографія', "Пов'язані елементи", 'робіт', 'посилання', 'література', 'побачити', 'Дивись також', 'виноска', 'Інші проекти'] forbidden_by_language["ur"] = ['حوالہ جات', 'نوٹ:', 'ادب', 'اس کے بارے میں یہ کہا گیا تھا', 'اس کے بارے میں', 'ڈوبنگ', 'اس لڑکی کے لئے', 'کے بارے میں کہا جاتا ہے', 'متعلقہ اشیاء', 'قابل ذکر', 'فوٹیاں', 'متعلقہ ایپلی کیشنز', 'فلمگرافی', 'ذرائع:', 'منحصر سوالات', 'بیرونی حوالہ جات', 'انہوں نے اس کے بارے میں کہا', 'دیگر منصوبوں', 'دیکھو', 'بھی دیکھو', 'فلمگرافی', 'کے بارے میں', 'پر', 'متعلقہ', 'اے', 'سفیر', 'ادا کرتا ہے', 'حوالہ', 'اس کے لیے', 'بیرونی ٹاد', 'بائبلگرافی', 'بیرونی روابط', 'سالگرہ میں', 'روابط', 'ریلیز', 'خود کے بارے میں', 'روابط', 'کام', 'حوالہ', 'کے بارے میں بیانات', 'بھی دیکھو', 'سب سے مشہور کتابیں', 'کام', 'اس کے بارے میں یہ کہا گیا تھا', 'اضافی لنکس', 'حوالہ جات', 'باہر سے رابطہ کریں', 'ذریعہ', 'اس کے بارے میں ایک اور', 'کہہ رہا ہے', 'فلم', 'احترام کے ساتھ حوالہ جات', 'کہہ دو', 'ڈیویو', 'بیرونی لنک', 'حوالہ اوپر', 'بھی دیکھو', 'زیادہ', 'ذرائع', 'اس سے کہا جاتا ہے', 'انہوں نے کہا اے', 'حوالہ جات', 'کے بارے میں', 'پر', 'سے رابطہ کریں', 'ادا کرتا ہے', 'حوالہ جات', 'اس کے بارے میں', 'ذرائع', 'بیرونی روابط', 'انہوں نے اس کے بارے میں کہا', 'بیرونی لنک', 'تعریف', 'فوٹیوٹ', 'حوالہ جات', 'نوٹس', 'انٹرنیٹ میں وسائل', 'اس کے بارے میں', 'حوالہ جات', 'بیرونی کنکشن', 'کے بارے میں حوالہ جات', 'فلمگرافی', 'فوٹیوٹ', 'ذرائع', 'حوالہ جات', 'دیگر منصوبوں', 'بیرونی روابط', 'روابط', 'نوٹس', 'نوٹ', 'ویب لنکس', 'بائبلگرافی', 'متعلقہ اشیاء', 'کام', 'حوالہ جات', 'ادب', 'دیکھو', 'بھی دیکھو', 'فوٹیوٹ', 'دیگر منصوبوں'] forbidden_by_language["zh"] = ["引用","片目","脚注","来源","资源","其他项目","外部链接","链接","注释","注释","网络链接","参考书目","相关项目","作品","参考文献","文献","参见","另见","脚注","其他项目","另看","参考书目","作品", "著名","参考","他们说的","电影","关于他","相关查询","参考","配音","电影","为他","外部链接","戏剧","脚注","有人说他","作品","戏剧","其他项目"] forbidden = [f.lower() for l in list(forbidden_by_language.values()) for f in l] class EntityWithQuotes: def __init__(self, entity, id, language): def getQuotesFromUnstructuredText(section, id, wikiquote_id): def getQ(section, id): nonlocal quotes nonlocal n nonlocal level nonlocal section_titles section_titles = section_titles[:level] section_titles.append(section.title.text) for line in section.lines: n+=1 quote = untemplatedQuote(section_titles, line, id, n, language, wikiquote_id) quotes.update({quote.id:quote}) for sec in section.sub_sections: if sec.title.text.lower() in forbidden+[i+" "+wikiquote_id.lower() for i in forbidden]: continue level=level+1 getQ(sec, id) level=level-1 # filtering for empty Quotes using __bool__ temp_quotes = dict(quotes) for quote_id in temp_quotes: if not quotes[quote_id]: del quotes[quote_id] quotes = {} n = 1 level = 0 section_titles = [] getQ(section, id) return quotes def getQuotesFromTemplates(section, id, wikiquote_id): def getTempQ(section, id): nonlocal quotes nonlocal n nonlocal level nonlocal section_titles section_titles = section_titles[:level] section_titles.append(section.title.text) for template in section.templates: n+=1 templ = template.values quote = templatedQuote(id, n, language, section_titles, wikiquote_id, **templ) quotes.update({quote.id:quote}) for sec in section.sub_sections: if sec.title.text.lower() in forbidden+[i+" "+wikiquote_id.lower() for i in forbidden]: continue level=level+1 getTempQ(sec, id) level=level-1 # filtering for empty Quotes using __bool__ temp_quotes = dict(quotes) for quote_id in temp_quotes: if not quotes[quote_id]: del quotes[quote_id] quotes = {} n = 1 level = 0 section_titles = [] getTempQ(section, id) return quotes self.lang=language self.entity = entity self.wikiquote_id = entity.wikiquote_id self.wikiquote_page_id = entity.wikiquote_page_id self.wikidata_id = entity.wikidata_id self.wikipedia_id = entity.wikipedia_id self.types = [] self.id = id self.quotes = dict() if self.lang in languages_with_templates: self.quotes = getQuotesFromTemplates(entity.main_section, id, self.wikiquote_id) elif self.lang in hybrid_languages: self.quotes = getQuotesFromTemplates(entity.main_section, id, self.wikiquote_id) self.quotes.update(getQuotesFromUnstructuredText(entity.main_section, self.id, self.wikiquote_id)) else: self.quotes = getQuotesFromUnstructuredText(entity.main_section, self.id, self.wikiquote_id) self.quotes = collections.OrderedDict(sorted(self.quotes.items())) class CompleteEntity(): def __init__(self, id, entities): self.entities = entities self.wikiquoteIds = dict() self.wikiquotePageIds= dict() self.wikipediaIds= dict() for language in self.entities: self.wikiquoteIds.update({language:self.entities[language][0].entity.wikiquote_id}) self.wikiquotePageIds.update({language:self.entities[language][0].entity.wikiquote_page_id}) self.wikipediaIds.update({language:self.entities[language][0].entity.wikipedia_id}) self.wikidata_id = id

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null

0

3

6ab43a62d7fe9c3851fb93216f017948e9264012

4,665

py

Python

vagrant/optraj.istic.univ-rennes1.fr/src/system/Assignment.py

gpierre42/optraj

53beb81c669093b866a786f2c1df9c663bbd7224

[ "Apache-2.0" ]

null

vagrant/optraj.istic.univ-rennes1.fr/src/system/Assignment.py

gpierre42/optraj

53beb81c669093b866a786f2c1df9c663bbd7224

[ "Apache-2.0" ]

null

vagrant/optraj.istic.univ-rennes1.fr/src/system/Assignment.py

gpierre42/optraj

53beb81c669093b866a786f2c1df9c663bbd7224

[ "Apache-2.0" ]

null

# coding=utf8 ''' Created on 29 Oct 2013 @author: Nicolas Poirey ''' from Worker import Worker from Phase import Phase class Assignment(object): ''' Classe Assignment définissant une affectation attributs publics : - num : l'id de l'affectation (int) - worker : l'ouvrier de l'affectation (Worker.Worker) - phase : la phase de l'affectation (Phase.Phase) ''' def __init__(self, num=-1, worker=Worker(), phase=Phase()): ''' Constructeur d'une affectation d'un ouvrier à un chantier Args: num : le numero unique identifiant l'affectation. (int) worker : l'ouvrier concerné. (Worker.Worker) phase : la phase de l'affectation (Phase.Phase) ''' self._num = num self._worker = worker self._phase = phase def __str__(self): ''' Retourne l'affecation sous une forme lisible pour l'humain Returns: l'affectation sous forme de string. Examples: >>> p.__str__() >>> "L'affectation, d'id 3, de l'ouvrier (id 5) Doe John, sur la phase d'id 4" ''' return "L'affectation, d'id {}, de l'ouvrier (id {}) {} {}, sur la phase d'id {}".format(self.num, self.worker.num, self.worker.firstName, self.worker.name, \ self.phase.num) + " " + str(self._phase) ''' /// @cond ========================= Setters/accesseurs ============================== ''' #ifndef DOXYGEN_SHOULD_SKIP_THIS @property def num(self): """ Getter du num """ return self._num @num.setter def num(self, value): """ Setter du num """ self._num = value @property def worker(self): """ Getter du worker """ return self._worker @worker.setter def worker(self, value): """ Setter du worker """ self._worker = value @property def phase(self): """ Getter de la phase """ return self._phase @phase.setter def phase(self, value): """ Setter de la phase """ self._phase = value #endif /* DOXYGEN_SHOULD_SKIP_THIS */ ''' /// @endcond ================ Méthodes publiques ================ ''' def serial(self): ''' Sérialise une affectation Returns: un dict contenant l'affectation serialisé Example: >>> {'phase': {'needs': {33: {'num': 33, '__class__': 'Need', 'need': 10, 'craft': {'num': 2, '__class__': 'Craft', 'name': u'Macon'}, 'qualification': {'num': 4, '__class__': 'Qualification', 'name': u'N3P2'}, 'phase': 19}, 34: {'num': 34, '__class__': 'Need', 'need': 20, 'craft': {'num': 2, '__class__': 'Craft', 'name': u'Macon'}, 'qualification': {'num': 5, '__class__': 'Qualification', 'name': u'N3P1'}, 'phase': 19}, 92: {'num': 92, '__class__': 'Need', 'need': 2, 'craft': {'num': 7, '__class__': 'Craft', 'name': u"Agent d'entretien"}, 'qualification': {'num': 6, '__class__': 'Qualification', 'name': u'N2'}, 'phase': 19}, 79: {'num': 79, '__class__': 'Need', 'need': 2, 'craft': {'num': 10, '__class__': 'Craft', 'name': u"Chef d'\xe9quipe"}, 'qualification': {'num': 2, '__class__': 'Qualification', 'name': u'N4P2'}, 'phase': 19}}, 'num': 19, 'numYear': 2014, 'numWeek': 15, 'totalWorkers': 0, 'nbWorkers': 0, '__class__': 'Phase', 'numSite': 4}, 'num': 391, 'worker': {'num': 101, 'licence': u' ', 'name': u'JOUSSEAUME', 'firstName': u'MICKAEL', 'birthdateY': '1972', '__class__': 'Worker', 'birthdateM': '11', 'craft': {'num': 2, '__class__': 'Craft', 'name': u'Macon'}, 'qualification': {'num': 4, '__class__': 'Qualification', 'name': u'N3P2'}, 'position': {'latitude': 47.9292, '__class__': 'Position', 'longitude': -1.94175, 'address': u'6 RUE DE RENNES 35330 LA CHAPELLE BOUEXIC'}, 'birthdateD': '26'}, '__class__': 'Assignment'} ''' return {"__class__": "Assignment", "num": self.num, "worker": self.worker.serial(), "phase": self.phase.serial() } def phaseNumber(self): ''' Retourne le numéro de la phase associée Returns: numPhase (int). ''' return self._phase.num def workerNumber(self): ''' retourne le numéro de l'ouvrier associé Returns: numWorker (int). ''' return self._worker.num

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null

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1

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1

0

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6abef106632056c480a54511ff7725bfc1193a55

4,116

py

Python

scripts/automation/trex_control_plane/interactive/trex/examples/stl/ndr_plugin.py

timgates42/trex-core

efe94752fcb2d0734c83d4877afe92a3dbf8eccd

[ "Apache-2.0" ]

956

2015-06-24T15:04:55.000Z

2022-03-30T06:25:04.000Z

scripts/automation/trex_control_plane/interactive/trex/examples/stl/ndr_plugin.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

782

2015-09-20T15:19:00.000Z

2022-03-31T23:52:05.000Z

scripts/automation/trex_control_plane/interactive/trex/examples/stl/ndr_plugin.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

429

2015-06-27T19:34:21.000Z

2022-03-23T11:02:51.000Z

import stl_path class MyNDRPlugin(): def __init__(self): pass def pre_iteration(self, finding_max_rate, run_results=None, **kwargs): """ Function ran before each iteration. :parameters: finding_max_rate: boolean Indicates whether we are running for the first time, trying to find the max rate. In this is the case, the run_results will be None. run_results: dict A dictionary that contains the following keys: queue_full_percentage: Percentage of packets that are queued. drop_rate_percentage: Percentage of packets that were dropped. rate_tx_bps: TX rate in bps. rate_rx_bps: RX rate in bps. tx_util: TX utilization percentage. latency: Latency groups. cpu_util: CPU utilization percentage. tx_pps: TX in pps. rx_pps: RX in pps. tx_bps: TX in bps. rx_bps: RX in bps. bw_per_core: Bandwidth per core. rate_p: Running rate in percentage out of max. total_tx_L1: Total TX L1. total_rx_L1: Total RX L1. iteration: Description of iteration (not necessarily a number) Pay attention: The rate is of the upcoming iteration. All the rest are of the previous iteration. kwargs: dict List of tunables passed as parameters. """ # Pre iteration function. This function will run before TRex transmits to the DUT. # Could use this to better prepare the DUT, for example define shapers, policers, increase buffers and queues. # You can receive tunables in the command line, through the kwargs argument. pass def post_iteration(self, finding_max_rate, run_results, **kwargs): """ Function ran after each iteration. :parameters: finding_max_rate: boolean Indicates whether we are running for the first time, trying to find the max rate. If this is the case, some values of run_results (like iteration for example) are not relevant. run_results: dict A dictionary that contains the following keys: queue_full_percentage: Percentage of packets that are queued. drop_rate_percentage: Percentage of packets that were dropped. rate_tx_bps: TX rate in bps. rate_rx_bps: RX rate in bps. tx_util: TX utilization percentage. latency: Latency groups. cpu_util: CPU utilization percentage. tx_pps: TX in pps. rx_pps: RX in pps. tx_bps: TX in bps. rx_bps: RX in bps. bw_per_core: Bandwidth per core. rate_p: Running rate in percentage out of max. total_tx_L1: Total TX L1. total_rx_L1: Total RX L1. iteration: Description of iteration (not necessarily a number) kwargs: dict List of tunables passed as parameters. :returns: bool: should stop the benchmarking or not. """ # Post iteration function. This function will run after TRex transmits to the DUT. # Could use this to decide if to continue the benchmark after querying the DUT post run. The DUT might be overheated or any other thing that might make you want to stop the run. # You can receive tunables in the command line, through the kwargs argument. should_stop = False return should_stop # dynamic load of python module def register(): return MyNDRPlugin()

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null

0

1

0

1

0

3

6ac6979dc72c67c44ef423ebf8b3a34cc0b6d4cc

539

py

Python

gva/data/validator/is_valid_enum.py

gva-jjoyce/gva_data

cda990d0abb4b175025aaf16e75192bd9cc213af

[ "Apache-2.0" ]

null

gva/data/validator/is_valid_enum.py

gva-jjoyce/gva_data

cda990d0abb4b175025aaf16e75192bd9cc213af

[ "Apache-2.0" ]

24

2020-12-24T12:21:42.000Z

2021-01-28T14:22:38.000Z

gva/data/validator/is_valid_enum.py

gva-jjoyce/gva_data

cda990d0abb4b175025aaf16e75192bd9cc213af

[ "Apache-2.0" ]

null

""" Enumerator Test """ from typing import Any class is_valid_enum(): """ Test if a variable is on a list of valid values """ __slots__ = ('symbols') def __init__(self, **kwargs): """ -> "type": "enum", "symbols": ["up", "down"] symbols: list of allowed values (case sensitive) """ self.symbols = kwargs.get('symbols', ()) def __call__(self, value: Any) -> bool: return value and value in self.symbols def __str__(self): return f'enum {self.symbols}'

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0.222222

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null

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null

0

1

0

1

0

3

6ac89300a5b9e4ad6f97864631998446abb69eb0

313

py

Python

proto_3/ddq/topics/logics/topic.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

proto_3/ddq/topics/logics/topic.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

6

2021-03-19T12:06:56.000Z

2022-03-12T00:23:09.000Z

proto_3/ddq/topics/logics/topic.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

from typing import List from ddq.taxonomy.reference import Reference from ddq.topics.topic import Topic class Logic(Topic): def references(self) -> List[Reference]: return [ Reference("Classical and Nonclassical Logics", [("Eric", "Schechter")]) ]

26.083333

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0.111111

0.666667

0

null

0

null

0

1

0

3

6ad38265801ddbc75fcce3bfaba00694854f353b

690

py

Python

PyGame/pygame1/tutorial1/startercode.py

hoppfull/Legacy-Python

43f465bfdb76c91f2ac16aabb0783fdf5f459adb

[ "MIT" ]

null

PyGame/pygame1/tutorial1/startercode.py

hoppfull/Legacy-Python

43f465bfdb76c91f2ac16aabb0783fdf5f459adb

[ "MIT" ]

null

PyGame/pygame1/tutorial1/startercode.py

hoppfull/Legacy-Python

43f465bfdb76c91f2ac16aabb0783fdf5f459adb

[ "MIT" ]

null

from pygamehelper import * from pygame import * from pygame.locals import * from vec2d import * from random import uniform import numpy as np class Starter(PygameHelper): def __init__(self): self.w, self.h = 800, 600 PygameHelper.__init__(self, size=(self.w, self.h), fill=((0,0,0))) def update(self): pass def keyUp(self, key): pass def mouseUp(self, button, pos): pass def mouseMotion(self, buttons, pos, rel): pass def draw(self): self.screen.fill((np.random.random()*255, np.random.random()*255, np.random.random()*255)) s = Starter() s.mainLoop(40)

22.258065

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0.588406

90

690

4.422222

0.433333

0.100503

0.105528

0.128141

0

0.042857

0.289855

690

30

99

23

0.769388

0

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0.272727

false

0.181818

0.272727

0

0.590909

0

null

0

null

0

1

0

1

0

1

0

3

0a750b96f7d83d3d539bea6b3d201533cd437b4f

832

py

Python

redirink/insights/tests/test_models.py

Egor4ik325/redirink

17ef85f48145ee6112f2fcbab60dcd9d65ba78bf

[ "MIT" ]

null

redirink/insights/tests/test_models.py

Egor4ik325/redirink

17ef85f48145ee6112f2fcbab60dcd9d65ba78bf

[ "MIT" ]

null

redirink/insights/tests/test_models.py

Egor4ik325/redirink

17ef85f48145ee6112f2fcbab60dcd9d65ba78bf

[ "MIT" ]

1

2021-12-31T00:46:31.000Z

"""Test insight model is working the way it should.""" import pytest from django.core.exceptions import ValidationError from django.db import DataError from .factories import InsightFactory pytestmark = pytest.mark.django_db def test_create_new_fake_visitor_instance_using_factory(visitor): pass def test_create_new_instance_using_model_factory(insight): pass def test_fake_instance_is_valid(insight): # Should not raise ValidationError insight.full_clean() def test_fake_instance_have_right_fields(insight): assert isinstance(insight.id, int) assert insight.time is not None def test_invalid_ip_address(): with pytest.raises(DataError): InsightFactory(visitor__ip_address="invalid ip") def test_valid_fake_ip_v6_address(faker): InsightFactory(visitor__ip_address=faker.ipv6())

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0

null

0

1

0

1

0

1

0

3

0a78179deb3bba9140ba6fad7537f792839802d1

826

py

Python

compyle/api.py

nauaneed/compyle

218c76de8aa684e1fb198072e40cb97a5e6845b3

[ "BSD-3-Clause" ]

null

compyle/api.py

nauaneed/compyle

218c76de8aa684e1fb198072e40cb97a5e6845b3

[ "BSD-3-Clause" ]

null

compyle/api.py

nauaneed/compyle

218c76de8aa684e1fb198072e40cb97a5e6845b3

[ "BSD-3-Clause" ]

null

from .array import Array, wrap from .ast_utils import (get_symbols, get_assigned, get_unknown_names_and_calls, has_return, has_node) from .config import get_config, set_config, use_config, Config from .cython_generator import ( CythonGenerator, get_func_definition ) from .ext_module import ExtModule from .extern import Extern from .low_level import Kernel, LocalMem, Cython, cast from .parallel import ( Elementwise, Reduction, Scan, elementwise ) from .profile import ( get_profile_info, named_profile, profile, profile_ctx, print_profile, profile_kernel, ProfileContext, profile2csv ) from .translator import ( CConverter, CStructHelper, OpenCLConverter, detect_type, ocl_detect_type, py2c ) from .types import KnownType, annotate, declare from .utils import ArgumentParser

34.416667

77

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0.043831

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0.002886

0.161017

826

23

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0.886003

0

1

0

true

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0.521739

0.043478

0

null

0

null

0

1

0

1

0

1

0

3

0a8392531b265c3630ab7efd862cf9bb543e8116

126

py

Python

py_tdlib/constructors/get_chat_member.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/get_chat_member.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/get_chat_member.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Method class getChatMember(Method): chat_id = None # type: "int53" user_id = None # type: "int32"

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126

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0.25

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null

0

1

0

1

0

null

0

3

0a8510d776fffba4b52eff1b8a24d1b7d723d4dd

1,836

py

Python

ooobuild/csslo/xml/__init__.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/csslo/xml/__init__.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/csslo/xml/__init__.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from ...lo.xml.attribute import Attribute as Attribute from ...lo.xml.attribute_container import AttributeContainer as AttributeContainer from ...lo.xml.attribute_data import AttributeData as AttributeData from ...lo.xml.export_filter import ExportFilter as ExportFilter from ...lo.xml.fast_attribute import FastAttribute as FastAttribute from ...lo.xml.import_filter import ImportFilter as ImportFilter from ...lo.xml.namespace_container import NamespaceContainer as NamespaceContainer from ...lo.xml.para_user_defined_attributes_supplier import ParaUserDefinedAttributesSupplier as ParaUserDefinedAttributesSupplier from ...lo.xml.text_user_defined_attributes_supplier import TextUserDefinedAttributesSupplier as TextUserDefinedAttributesSupplier from ...lo.xml.user_defined_attributes_supplier import UserDefinedAttributesSupplier as UserDefinedAttributesSupplier from ...lo.xml.x_export_filter import XExportFilter as XExportFilter from ...lo.xml.x_import_filter import XImportFilter as XImportFilter from ...lo.xml.x_import_filter2 import XImportFilter2 as XImportFilter2 from ...lo.xml.xml_export_filter import XMLExportFilter as XMLExportFilter from ...lo.xml.xml_import_filter import XMLImportFilter as XMLImportFilter

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6.169421

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1,836

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0

null

0

1

0

1

0

1

0

3

0a91aa29e60075c1d841a9fa42cfbeabf426976a

2,225

py

Python

timm/models/layers/__init__.py

kkahatapitiya/pytorch-image-models

94f9d54ac22354f3cf7ada9a7304ac97143deb14

[ "Apache-2.0" ]

null

timm/models/layers/__init__.py

kkahatapitiya/pytorch-image-models

94f9d54ac22354f3cf7ada9a7304ac97143deb14

[ "Apache-2.0" ]

null

timm/models/layers/__init__.py

kkahatapitiya/pytorch-image-models

94f9d54ac22354f3cf7ada9a7304ac97143deb14

[ "Apache-2.0" ]

null

from .activations import * from .adaptive_avgmax_pool import \ adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d from .blur_pool import BlurPool2d from .classifier import ClassifierHead, create_classifier from .cond_conv2d import CondConv2d, get_condconv_initializer from .config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\ set_layer_config from .conv2d_same import Conv2dSame, conv2d_same from .conv_bn_act import ConvBnAct from .create_act import create_act_layer, get_act_layer, get_act_fn from .create_attn import get_attn, create_attn from .create_conv2d import create_conv2d from .create_norm_act import get_norm_act_layer, create_norm_act, convert_norm_act from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn from .evo_norm import EvoNormBatch2d, EvoNormSample2d from .gather_excite import GatherExcite from .global_context import GlobalContext from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible from .inplace_abn import InplaceAbn from .involution import Involution from .linear import Linear from .mixed_conv2d import MixedConv2d from .mlp import Mlp, GluMlp, GatedMlp, ConvMlpGeneral, ConvMlpGeneralv2 from .non_local_attn import NonLocalAttn, BatNonLocalAttn from .norm import GroupNorm, LayerNorm2d from .norm_act import BatchNormAct2d, GroupNormAct from .padding import get_padding, get_same_padding, pad_same from .patch_embed import PatchEmbed from .pool2d_same import AvgPool2dSame, create_pool2d from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite from .selective_kernel import SelectiveKernel from .separable_conv import SeparableConv2d, SeparableConvBnAct from .space_to_depth import SpaceToDepthModule from .split_attn import SplitAttn from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame from .test_time_pool import TestTimePoolHead, apply_test_time_pool from .weight_init import trunc_normal_, variance_scaling_, lecun_normal_

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6.368056

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0.019084

0.008724

0.015267

0

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0.094382

2,225

40

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0

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true

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null

0

null

0

1

0

1

0

1

0

3

0a94bf94b86f2d0bf5c868fffdac3fa72c685955

19,040

py

Python

cfgov/ask_cfpb/tests/test_views.py

atuggle/cfgov-refresh

5a9cfd92b460b9be7befb39f5845abf56857aeac

[ "CC0-1.0" ]

null

cfgov/ask_cfpb/tests/test_views.py

atuggle/cfgov-refresh

5a9cfd92b460b9be7befb39f5845abf56857aeac

[ "CC0-1.0" ]

1

2016-09-14T21:11:19.000Z

cfgov/ask_cfpb/tests/test_views.py

atuggle/cfgov-refresh

5a9cfd92b460b9be7befb39f5845abf56857aeac

[ "CC0-1.0" ]

null

from __future__ import unicode_literals import json from django.apps import apps from django.core.urlresolvers import NoReverseMatch, reverse from django.http import Http404, HttpRequest, QueryDict from django.test import TestCase, override_settings from django.utils import timezone from wagtail.wagtailcore.models import Site from wagtailsharing.models import SharingSite import mock from model_mommy import mommy from ask_cfpb.models import ENGLISH_PARENT_SLUG, SPANISH_PARENT_SLUG from ask_cfpb.views import annotate_links, ask_search, redirect_ask_search from v1.util.migrations import get_or_create_page now = timezone.now() class AnswerPagePreviewCase(TestCase): def setUp(self): from v1.models import HomePage from ask_cfpb.models import Answer self.ROOT_PAGE = HomePage.objects.get(slug='cfgov') self.english_parent_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerLandingPage', 'Ask CFPB', ENGLISH_PARENT_SLUG, self.ROOT_PAGE, language='en', live=True) self.spanish_parent_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerLandingPage', 'Obtener respuestas', SPANISH_PARENT_SLUG, self.ROOT_PAGE, language='es', live=True) self.test_answer = mommy.make( Answer, answer="Test answer.", question="Test question.", slug='test-question', update_english_page=True, update_spanish_page=False) self.site = mommy.make( Site, root_page=self.ROOT_PAGE, hostname='localhost', port=8000, is_default_site=True) self.sharing_site = mommy.make( SharingSite, site=self.site, hostname='preview.localhost', port=8000) @mock.patch('ask_cfpb.views.ServeView.serve_latest_revision') def test_preview_page(self, mock_serve): from ask_cfpb.views import view_answer page = self.test_answer.english_page revision = page.save_revision() revision.publish() test_request = HttpRequest() test_request.META['SERVER_NAME'] = 'preview.localhost' test_request.META['SERVER_PORT'] = 8000 view_answer( test_request, 'test-question', 'en', self.test_answer.pk) self.assertEqual(mock_serve.call_count, 1) def test_answer_page_not_live(self): from ask_cfpb.views import view_answer page = self.test_answer.english_page page.live = False page.save() test_request = HttpRequest() with self.assertRaises(Http404): view_answer( test_request, 'test-question', 'en', self.test_answer.pk) class AnswerViewTestCase(TestCase): def setUp(self): from v1.models import HomePage self.ROOT_PAGE = HomePage.objects.get(slug='cfgov') self.english_parent_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerLandingPage', 'Ask CFPB', ENGLISH_PARENT_SLUG, self.ROOT_PAGE, language='en', live=True) self.spanish_parent_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerLandingPage', 'Obtener respuestas', SPANISH_PARENT_SLUG, self.ROOT_PAGE, language='es', live=True) def test_annotate_links(self): mock_answer = ( 'Answer with a <a href="http://fake.com">fake link.</a>') (annotated_answer, links) = annotate_links(mock_answer) self.assertEqual( annotated_answer, '<html><body>Answer with a <a href="http://fake.com">fake ' 'link.</a>1</body></html>') self.assertEqual(links, [(1, str('http://fake.com'))]) def test_annotate_links_no_href(self): mock_answer = ( 'Answer with a <a>fake link.</a>') (annotated_answer, links) = annotate_links(mock_answer) self.assertEqual(links, []) def test_annotate_links_no_site(self): site = Site.objects.get(is_default_site=True) site.is_default_site = False site.save() with self.assertRaises(RuntimeError) as context: annotate_links('answer') self.assertIn('no default wagtail site', str(context.exception)) def test_bad_language_search(self): with self.assertRaises(NoReverseMatch): self.client.get(reverse( 'ask-search-en', kwargs={'language': 'zz'}), {'q': 'payday'}) @mock.patch('ask_cfpb.views.SearchQuerySet.filter') def test_en_search_results_page_not_created(self, mock_filter): mock_queryset = mock.Mock() mock_queryset.count.return_value = 0 mock_filter.return_value = [mock_queryset] response = self.client.get(reverse( 'ask-search-en'), {'q': 'payday'}) self.assertEqual(mock_filter.call_count, 1) self.assertTrue(mock_filter.called_with(language='en', q='payday')) self.assertEqual(response.status_code, 404) @mock.patch('ask_cfpb.views.SearchQuerySet') def test_en_search(self, mock_sqs): from v1.util.migrations import get_or_create_page mock_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock results page', 'ask-cfpb-search-results', self.ROOT_PAGE, language='en') mock_return = mock.Mock() mock_return.url = 'mockcfpb.gov' mock_return.autocomplete = 'A mock question' mock_return.text = 'Mock answer text.' mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 1 mock_sqs_instance = mock_sqs.return_value.models.return_value mock_sqs_instance.filter.return_value = mock_queryset mock_sqs_instance.spelling_suggestion.return_value = 'payday' response = self.client.get(reverse( 'ask-search-en'), {'q': 'payday'}) self.assertEqual(response.status_code, 200) self.assertEqual( response.context_data['page'], mock_page) self.assertEqual( response.context_data['page'].suggestion, None) self.assertEqual(mock_sqs_instance.filter.call_count, 1) self.assertTrue(mock_sqs_instance.filter.called_with( language='en', q='payday')) @mock.patch('ask_cfpb.views.SearchQuerySet') def test_en_search_no_term(self, mock_sqs): from v1.util.migrations import get_or_create_page mock_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock results page', 'ask-cfpb-search-results', self.ROOT_PAGE, language='en') response = self.client.get(reverse( 'ask-search-en'), {'q': ''}) self.assertEqual(response.status_code, 200) self.assertEqual( response.context_data['page'], mock_page) self.assertEqual( response.context_data['page'].query, '') self.assertEqual( response.context_data['page'].result_query, '') @override_settings(FLAGS={'ASK_SEARCH_TYPOS': {'boolean': True}}) @mock.patch('ask_cfpb.views.SearchQuerySet') def test_en_search_suggestion(self, mock_sqs): from v1.util.migrations import get_or_create_page mock_page = get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock results page', 'ask-cfpb-search-results', self.english_parent_page, language='en', live=True) mock_return = mock.Mock() mock_return.url = 'mockcfpb.gov' mock_return.autocomplete = 'A mock question' mock_return.text = 'Mock answer text.' mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 0 mock_sqs_instance = mock_sqs.return_value.models.return_value mock_sqs_instance.filter.return_value = mock_queryset mock_sqs_instance.spelling_suggestion.return_value = 'payday' response = self.client.get(reverse( 'ask-search-en'), {'q': 'paydya'}) self.assertEqual(response.status_code, 200) response_page = response.context_data['page'] self.assertEqual(response_page, mock_page) self.assertEqual(response_page.suggestion, 'paydya') self.assertEqual(response_page.result_query, 'payday') self.assertEqual(response_page.query, 'paydya') @mock.patch('ask_cfpb.views.redirect_ask_search') def test_ask_search_encounters_facets(self, mock_redirect): request = HttpRequest() request.GET['selected_facets'] = 'category_exact:my_category' ask_search(request) self.assertEqual(mock_redirect.call_count, 1) @mock.patch('ask_cfpb.views.redirect') def test_redirect_ask_search_passes_query_string(self, mock_redirect): request = HttpRequest() request.GET['q'] = 'hoodoo' redirect_ask_search(request) self.assertEqual(mock_redirect.call_count, 1) @mock.patch('ask_cfpb.views.redirect') def test_spanish_redirect_ask_search_passes_query_string( self, mock_redirect): request = HttpRequest() request.GET['selected_facets'] = 'category_exact:my_categoria' redirect_ask_search(request, language='es') self.assertEqual(mock_redirect.call_count, 1) @mock.patch('ask_cfpb.views.SearchQuerySet.filter') def test_es_search(self, mock_filter): get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock Spanish results page', 'respuestas', self.spanish_parent_page, language='es', live=True) mock_return = mock.Mock() mock_return.url = 'mockcfpb.gov' mock_return.autocomplete = 'A mock question' mock_return.text = 'Mock answer text.' mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 1 mock_filter.return_value = mock_queryset self.client.get(reverse( 'ask-search-es', kwargs={'language': 'es'}), {'q': 'payday'}) self.assertEqual(mock_filter.call_count, 1) self.assertTrue(mock_filter.called_with(language='es', q='payday')) @mock.patch('ask_cfpb.views.SearchQuerySet.filter') def test_search_page_en_selection(self, mock_filter): page = get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock results page', 'ask-cfpb-search-results', self.english_parent_page, language='en', live=True) mock_return = mock.Mock() mock_return.url = 'url' mock_return.autocomplete = 'question text' mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 1 mock_filter.return_value = mock_queryset self.client.get(reverse( 'ask-search-en'), {'q': 'tuition'}) self.assertEqual(mock_filter.call_count, 1) self.assertEqual(page.language, 'en') self.assertEqual(page.answers, []) self.assertEqual( page.get_template(HttpRequest()), 'ask-cfpb/answer-search-results.html') @mock.patch('ask_cfpb.views.SearchQuerySet.filter') def test_search_page_es_selection(self, mock_filter): page = get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock Spanish results page', 'respuestas', self.spanish_parent_page, language='es', live=True) mock_return = mock.Mock() mock_return.url = 'url' mock_return.autocomplete = 'question text' mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 1 mock_filter.return_value = mock_queryset self.client.get(reverse( 'ask-search-es', kwargs={'language': 'es'}), {'q': 'hipotecas'}) self.assertEqual(mock_filter.call_count, 1) self.assertEqual(page.language, 'es') self.assertEqual(page.answers, []) self.assertEqual( page.get_template(HttpRequest()), 'ask-cfpb/answer-search-spanish-results.html') @mock.patch('ask_cfpb.views.SearchQuerySet.filter') def test_json_response(self, mock_filter): get_or_create_page( apps, 'ask_cfpb', 'AnswerResultsPage', 'Mock results page', 'ask-cfpb-search-results', self.english_parent_page, language='en', live=True) mock_return = mock.Mock() mock_return.url = "inscisive_url.com" mock_return.autocomplete = "inscisive question" mock_return.text = "inscisive text" mock_queryset = mock.Mock() mock_queryset.__iter__ = mock.Mock(return_value=iter([mock_return])) mock_queryset.count.return_value = 1 mock_filter.return_value = mock_queryset response = self.client.get(reverse( 'ask-search-en-json', kwargs={'as_json': 'json'}), {'q': 'tuition'}) self.assertEqual(response.status_code, 200) self.assertEqual(mock_filter.call_count, 1) self.assertEqual(json.loads(response.content)['query'], 'tuition') def test_autocomplete_en_blank_term(self): result = self.client.get(reverse( 'ask-autocomplete-en'), {'term': ''}) output = json.loads(result.content) self.assertEqual(output, []) def test_autocomplete_es_blank_term(self): result = self.client.get(reverse( 'ask-autocomplete-es', kwargs={'language': 'es'}), {'term': ''}) output = json.loads(result.content) self.assertEqual(output, []) @mock.patch('ask_cfpb.views.SearchQuerySet.autocomplete') def test_autocomplete_en(self, mock_autocomplete): mock_search_result = mock.Mock() mock_search_result.autocomplete = 'question' mock_search_result.url = 'url' mock_autocomplete.return_value = [mock_search_result] result = self.client.get(reverse( 'ask-autocomplete-en'), {'term': 'question'}) self.assertEqual(mock_autocomplete.call_count, 1) output = json.loads(result.content) self.assertEqual( sorted(output[0].keys()), ['question', 'url']) @mock.patch('ask_cfpb.views.SearchQuerySet.autocomplete') def test_autocomplete_es(self, mock_autocomplete): mock_search_result = mock.Mock() mock_search_result.autocomplete = 'question' mock_search_result.url = 'url' mock_autocomplete.return_value = [mock_search_result] result = self.client.get(reverse( 'ask-autocomplete-es', kwargs={'language': 'es'}), {'term': 'question'}) self.assertEqual(mock_autocomplete.call_count, 1) output = json.loads(result.content) self.assertEqual( sorted(output[0].keys()), ['question', 'url']) class RedirectAskSearchTestCase(TestCase): def test_redirect_search_no_facets(self): request = HttpRequest() with self.assertRaises(Http404): redirect_ask_search(request) def test_redirect_search_blank_facets(self): request = HttpRequest() request.GET['selected_facets'] = '' with self.assertRaises(Http404): redirect_ask_search(request) def test_redirect_search_no_query(self): request = HttpRequest() request.GET['q'] = ' ' with self.assertRaises(Http404): redirect_ask_search(request) def test_redirect_search_with_category(self): category_querystring = ( 'selected_facets=category_exact:my_category' '&selected_facets=category_exact:my_category2' '&selected_facets=audience_exact:Older+Americans' '&selected_facets=audience_exact:my_audience2' '&selected_facets=tag_exact:mytag1' '&selected_facets=tag_exact:mytag2') request = HttpRequest() request.GET = QueryDict(category_querystring) result = redirect_ask_search(request) self.assertEqual(result.get('location'), '/ask-cfpb/category-my_category/') def test_redirect_search_with_audience(self): audience_querystring = ( 'selected_facets=audience_exact:Older+Americans' '&selected_facets=audience_exact:my_audience2') request = HttpRequest() request.GET = QueryDict(audience_querystring) result = redirect_ask_search(request) self.assertEqual( result.get('location'), '/ask-cfpb/audience-older-americans/') def test_spanish_redirect_search_with_tag(self): target_tag = 'spanishtag1' tag_querystring = ( 'selected_facets=tag_exact:{}' '&selected_facets=tag_exact:spanishtag2'.format(target_tag)) request = HttpRequest() request.GET = QueryDict(tag_querystring) result = redirect_ask_search(request, language='es') self.assertEqual( result.get('location'), '/es/obtener-respuestas/buscar-por-etiqueta/{}/'.format( target_tag)) def test_english_redirect_search_with_tag(self): target_tag = 'englishtag1' tag_querystring = ( 'selected_facets=tag_exact:{}' '&selected_facets=tag_exact:englishtag2'.format(target_tag)) request = HttpRequest() request.GET = QueryDict(tag_querystring) result = redirect_ask_search(request, language='en') self.assertEqual( result.get('location'), '/ask-cfpb/search-by-tag/{}/'.format( target_tag)) def test_redirect_search_with_unrecognized_facet_raises_404(self): querystring = \ 'sort=-updated_at&selected_facets=imtkfidycqszgfdb&page=60' request = HttpRequest() request.GET = QueryDict(querystring) with self.assertRaises(Http404): redirect_ask_search(request)

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0a96db7bc8255b1b1b651c9085fc3a06e4243461

1,753

py

Python

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

from __future__ import division from mmtbx.tls import tools import math import time pdb_str_1 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 3.000 0.000 0.000 1.00 0.00 C """ pdb_str_2 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 0.000 3.000 0.000 1.00 0.00 C """ pdb_str_3 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 0.000 0.000 3.000 1.00 0.00 C """ pdb_str_4 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 1.000 2.000 3.000 1.00 0.00 C """ def exercise_03(): sqrt = math.sqrt vs = [] vs.append( [(sqrt(2)/2, sqrt(2)/2, 0), (-sqrt(2)/2, sqrt(2)/2, 0), (0,0,1)] ) vs.append( [(1,0,0), (0, sqrt(2)/2, sqrt(2)/2), (0, -sqrt(2)/2, sqrt(2)/2)] ) vs.append( [(sqrt(3)/2, 1/2, 0), (-1/2, sqrt(3)/2, 0), (0,0,1)] ) vs.append( [(1,0,0), (0, sqrt(3)/2, 1/2), (0, -1/2, sqrt(3)/2)] ) for pdb_str in [pdb_str_1, pdb_str_2, pdb_str_3, pdb_str_4]: for vs_ in vs: vx,vy,vz = vs_ print vx,vy,vz tools.u_tls_vs_u_ens(pdb_str=pdb_str, tx=0.05,ty=0.07,tz=0.09, vx=vx, vy=vy, vz=vz, n_models=1000) if (__name__ == "__main__"): t0 = time.time() exercise_03() print "Time: %6.4f"%(time.time()-t0) print "OK"

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0aa458014e027a9ad777515ef9c0b45d42da4384

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py

Python

archiveis/__init__.py

palewire/archiveis

11b2f1a4be4e7fbdcd52d874733cf20bc2d4f480

[ "MIT" ]

6

2021-11-09T11:00:56.000Z

2022-01-14T03:44:52.000Z

archiveis/__init__.py

palewire/archiveis

11b2f1a4be4e7fbdcd52d874733cf20bc2d4f480

[ "MIT" ]

4

2022-03-28T23:39:23.000Z

2022-03-28T23:39:24.000Z

archiveis/__init__.py

palewire/archiveis

11b2f1a4be4e7fbdcd52d874733cf20bc2d4f480

[ "MIT" ]

null

#!/usr/bin/env python from .api import capture __version__ = "0.0.7" __all__ = ("capture",)

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py

Python

edmundbotadder/cogs/webhook.py

thebeanogamer/edmund-botadder

91e71ce572f3206b99e1f7a68d40bc37b947daf5

[ "MIT" ]

null

edmundbotadder/cogs/webhook.py

thebeanogamer/edmund-botadder

91e71ce572f3206b99e1f7a68d40bc37b947daf5

[ "MIT" ]

null

edmundbotadder/cogs/webhook.py

thebeanogamer/edmund-botadder

91e71ce572f3206b99e1f7a68d40bc37b947daf5

[ "MIT" ]

null

from discord.ext.commands import Bot, Cog class Webhook(Cog): """ Webhook functionality """ def __init__(self, bot: Bot): self.bot = bot def setup(bot): bot.add_cog(Webhook(bot))

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null

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1

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3

0ab73f13b7fac0bb315926410cb3d00950f04053

114

py

Python

classes/settings.py

johnyburd/glucometer

075a48cff38e0570960fc2b8968bcb8b5ddd647f

[ "MIT" ]

12

2016-11-02T09:15:32.000Z

2021-04-08T18:42:01.000Z

classes/settings.py

johnyburd/glucometer

075a48cff38e0570960fc2b8968bcb8b5ddd647f

[ "MIT" ]

null

classes/settings.py

johnyburd/glucometer

075a48cff38e0570960fc2b8968bcb8b5ddd647f

[ "MIT" ]

3

2018-10-18T15:59:57.000Z

2021-01-20T21:03:48.000Z

def init(): global brightness global calibration_mode brightness = 500 calibration_mode = False

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0abdfc5e117d17fbbf96aa6e5e9c1b706bacee2c

95

py

Python

interface/app/__init__.py

caglorithm/accel

7fe5c13ea9559565c599633bdb3318c8fbc57088

[ "MIT" ]

31

2019-12-07T01:27:19.000Z

2021-12-19T08:12:18.000Z

interface/app/__init__.py

caglorithm/accel

7fe5c13ea9559565c599633bdb3318c8fbc57088

[ "MIT" ]

null

interface/app/__init__.py

caglorithm/accel

7fe5c13ea9559565c599633bdb3318c8fbc57088

[ "MIT" ]

null

from flask import Flask app = Flask(__name__, static_folder='static') from app import routes

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0ac44ba5690cb44ecf9e208ad61f69b8762610fd

634

py

Python

tools/leetcode.112.Path Sum/leetcode.112.Path Sum.submission10.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

4

2015-10-10T00:30:55.000Z

2020-07-27T19:45:54.000Z

tools/leetcode.112.Path Sum/leetcode.112.Path Sum.submission10.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

null

tools/leetcode.112.Path Sum/leetcode.112.Path Sum.submission10.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

null

# Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: # @param {TreeNode} root # @param {integer} sum # @return {boolean} def hasPathSum(self, root, sum): if not root: return False if not root.right and not root.left: return sum == root.val r = False l = False if root.right: r = self.hasPathSum(root.right,sum-root.val) if root.left: l = self.hasPathSum(root.left,sum-root.val) return r or l

634

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null

0

3

0acd83639363e1e8109b480a9d0f9a0898831b8f

54,720

py

Python

tests/python/relay/test_op_level2.py

ravikumarvc/incubator-tvm

9826947ffce0ed40e9d47a0db2abb033e394279e

[ "Apache-2.0" ]

3

2021-02-23T22:06:01.000Z

2021-09-30T09:59:17.000Z

tests/python/relay/test_op_level2.py

ravikumarvc/incubator-tvm

9826947ffce0ed40e9d47a0db2abb033e394279e

[ "Apache-2.0" ]

4

2021-03-30T11:59:59.000Z

2022-03-12T00:40:23.000Z

tests/python/relay/test_op_level2.py

ravikumarvc/incubator-tvm

9826947ffce0ed40e9d47a0db2abb033e394279e

[ "Apache-2.0" ]

3

2021-07-20T07:40:15.000Z

2021-08-03T08:39:17.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """ Support level2 operator test cases. """ import numpy as np import tvm from tvm import autotvm from tvm import relay from tvm.relay import transform from tvm.relay.testing import ctx_list, run_infer_type from tvm.contrib import util import topi.testing def test_conv1d_infer_type(): # symbolic in batch dimension n, c, w = tvm.var("n"), 10, 224 x = relay.var("x", relay.ty.TensorType((n, c, w), "float32")) w = relay.var("w") y = relay.nn.conv1d(x, w, kernel_size=3, padding=(1, 1), channels=2) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 224), "float32") assert yy.args[1].checked_type == relay.TensorType( (2, 10, 3), "float32") # infer by shape of w, mixed precision n, c, w = tvm.var("n"), 10, 224 x = relay.var("x", relay.TensorType((n, c, w), "int8")) w = relay.var("w", relay.TensorType((2, 10, 3), "int8")) y = relay.nn.conv1d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222), "int32") # infer shape in case of different dtypes for input and weight. n, c, w = tvm.var("n"), 10, 224 x = relay.var("x", relay.TensorType((n, c, w), "uint8")) w = relay.var("w", relay.TensorType((2, 10, 3), "int8")) y = relay.nn.conv1d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222), "int32") # Infer with NWC n, c, w = 4, 32, 224 x = relay.var("x", relay.TensorType((n, w, c), "int8")) wt = relay.var("w") y = relay.nn.conv1d(x, wt, kernel_size=3, padding=(1, 1), channels=16, data_layout="NWC", out_dtype="int32") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, w, 16), "int32") def test_conv1d_run(): def run_test_conv1d(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1), fref=None, dilation=1, except_targets=None, **attrs): if except_targets is None: except_targets = [] x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", dtype=dtype) y = relay.nn.conv1d(x, w, padding=padding, dilation=dilation, **attrs) func = relay.Function([x, w], y) data = np.random.uniform(-scale, scale, size=dshape).astype(dtype) kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) ref_res = topi.testing.conv1d_ncw_python( data.astype(out_dtype), kernel.astype(out_dtype), 1, padding, dilation) for target, ctx in ctx_list(): if target in except_targets: continue intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) # normal conv1d dshape = (1, 3, 224) kshape = (10, 3, 3) run_test_conv1d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=3) # mixed precision run_test_conv1d("int8", "int32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=3) # dilated conv2d dshape = (1, 3, 18) kshape = (10, 3, 3) run_test_conv1d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=3, dilation=3) def test_conv2d_infer_type(): # symbolic in batch dimension n, c, h, w = tvm.size_var("n"), 10, 224, 224 x = relay.var("x", relay.ty.TensorType((n, c, h, w), "float32")) w = relay.var("w") y = relay.nn.conv2d(x, w, kernel_size=(3, 3), padding=(1, 1), channels=2) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 224, 224), "float32") assert yy.args[1].checked_type == relay.TensorType( (2, 10, 3, 3), "float32") # infer by shape of w, mixed precision n, c, h, w = tvm.size_var("n"), 10, 224, 224 x = relay.var("x", relay.TensorType((n, c, h, w), "int8")) w = relay.var("w", relay.TensorType((2, 10, 3, 3), "int8")) y = relay.nn.conv2d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222, 222), "int32") # infer shape in case of different dtypes for input and weight. n, c, h, w = tvm.size_var("n"), 10, 224, 224 x = relay.var("x", relay.TensorType((n, c, h, w), "uint8")) w = relay.var("w", relay.TensorType((2, 10, 3, 3), "int8")) y = relay.nn.conv2d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222, 222), "int32") # Infer with a different layout n, c, h, w = 4, 32, 224, 224 x = relay.var("x", relay.TensorType((n//4, c//4, h, w, 4, 4), "int8")) wt = relay.var("w") y = relay.nn.conv2d(x, wt, kernel_size=(3, 3), padding=(1, 1), channels=16, data_layout="NCHW4n4c", kernel_layout="OIHW4o4i", out_dtype="int32") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (1, 4, 224, 224, 4, 4), "int32") assert yy.args[1].checked_type == relay.TensorType( (4, 8, 3, 3, 4, 4), "int8") # Infer with NHWC n, c, h, w = 4, 32, 224, 224 x = relay.var("x", relay.TensorType((n, h, w, c), "int8")) wt = relay.var("w") y = relay.nn.conv2d(x, wt, kernel_size=(3, 3), padding=(1, 1), channels=16, data_layout="NHWC", out_dtype="int32") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, h, w, 16), "int32") def test_conv2d_run(): def run_test_conv2d(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1), fref=None, groups=1, dilation=(1, 1), except_targets=None, **attrs): if except_targets is None: except_targets = [] x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", dtype=dtype) y = relay.nn.conv2d(x, w, padding=padding, dilation=dilation, groups=groups, **attrs) func = relay.Function([x, w], y) data = np.random.uniform(-scale, scale, size=dshape).astype(dtype) kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) dkernel = topi.testing.dilate_python(kernel, (1, 1) + dilation) if fref is None: ref_res = topi.testing.conv2d_nchw_python( data.astype(out_dtype), dkernel.astype(out_dtype), 1, padding, groups=groups) else: ref_res = fref(data.astype(out_dtype), dkernel.astype(out_dtype)) for target, ctx in ctx_list(): if target in except_targets: continue intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def compile_test_conv2d_arm_cpu(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1), groups=1, dilation=(1, 1), **attrs): x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", dtype=dtype) y = relay.nn.conv2d(x, w, padding=padding, dilation=dilation, groups=groups, **attrs) func = relay.Function([x, w], y) mod = tvm.relay.Module() mod["main"] = func test_schedule='{"i": ["llvm -device=arm_cpu", "topi_nn_depthwise_conv2d_nchw", \ [["TENSOR", [1, 512, 32, 32], "float32"], \ ["TENSOR", [512, 1, 3, 3], "float32"], \ [1, 1], [1, 1], [1, 1], "float32"], {}, \ ["depthwise_conv2d_nchw", [1, 512, 32, 32, "float32"], \ [512, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], \ {"i": 743640, "t": "contrib_spatial_pack", "c": null, \ "e": [["tile_co", "sp", [32, 16]], ["tile_oh", "sp", [8, 1]], \ ["tile_ow", "sp", [1, 8]], \ ["reorder_0", "re", [0, 1, 2, 3, 4, 5, 8, 6, 7]], \ ["reorder_1", "re", [0, 1, 2, 3, 6, 4, 5]], \ ["ann_reduce", "an", ["unroll", "none"]], \ ["ann_spatial", "an", ["unroll", "unroll", "vec"]], \ ["data_pad_inline", "ot", 4], ["data_vec_inline", "ot", 1], \ ["conv_inline", "ot", 0]]}], "r": [[0.0002933163], \ 0, 3.1976189613342285, 1570811630.6058347], "v": 0.1}' temp = util.tempdir() with open(temp.relpath("temp.log"), "w") as log_file: log_file.write(test_schedule) with autotvm.apply_history_best(temp.relpath("temp.log")): with relay.build_config(opt_level=3): print('Compiling...') graph_json, mod, params = tvm.relay.build(mod, target="llvm -device=arm_cpu") # depthwise conv2d dshape = (1, 32, 18, 18) kshape = (32, 1, 3, 3) run_test_conv2d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=32, groups=32, kernel_size=(3 ,3), fref=lambda x, w: topi.testing.depthwise_conv2d_python_nchw( x, w, (1, 1), "SAME")) # depthwise conv2d for arm_cpu dshape = (1, 512, 32, 32) kshape = (512, 1, 3, 3) compile_test_conv2d_arm_cpu("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=512, groups=512, kernel_size=(3 ,3)) # CUDA is disabled for 'direct' schedule: # https://github.com/apache/incubator-tvm/pull/3070#issuecomment-486597553 # group conv2d dshape = (1, 32, 18, 18) kshape = (32, 4, 3, 3) run_test_conv2d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=32, groups=8, kernel_size=(3 ,3), except_targets=['cuda']) # also group conv2d dshape = (1, 32, 18, 18) kshape = (64, 1, 3, 3) run_test_conv2d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=64, groups=32, kernel_size=(3 ,3), except_targets=['cuda']) # normal conv2d dshape = (1, 3, 224, 224) kshape = (10, 3, 3, 3) run_test_conv2d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(3 ,3)) # mixed precision run_test_conv2d("int8", "int32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(3 ,3)) kshape = (10, 3, 1, 3) # mixed precision. run_test_conv2d("int8", "int32", 1, dshape, kshape, padding=(0, 1), channels=10, kernel_size=(1 ,3)) # dilated conv2d dshape = (1, 3, 18, 18) kshape = (10, 3, 3, 3) run_test_conv2d("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(3 ,3), dilation=(3, 3)) def test_conv2d_winograd(): class WinogradFallback(autotvm.FallbackContext): def _query_inside(self, target, workload): key = (target, workload) if key in self.memory: return self.memory[key] cfg = autotvm.task.space.FallbackConfigEntity() cfg.template_key = 'winograd' cfg.is_fallback = False cfg['tile_b'] = autotvm.task.space.SplitEntity([-1, 1, 1, 1]) cfg['tile_y'] = autotvm.task.space.SplitEntity([-1, 1, 1, 1]) cfg['tile_x'] = autotvm.task.space.SplitEntity([-1, 1, 1, 1]) cfg['tile_rc'] = autotvm.task.space.SplitEntity([-1, 1]) cfg['auto_unroll_max_setp'] = autotvm.task.space.OtherOptionEntity(1500) cfg['unroll_explicit'] = autotvm.task.space.OtherOptionEntity(1) self.memory[key] = cfg return cfg def run_test_conv2d_cuda(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1), groups=1, dilation=(1, 1), **attrs): x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", shape=kshape, dtype=dtype) y = relay.nn.conv2d(x, w, padding=padding, dilation=dilation, groups=groups, **attrs) func = relay.Function([x, w], y) mod = relay.Module() mod['main'] = func mod = relay.transform.InferType()(mod) data = np.random.uniform(-scale, scale, size=dshape).astype(dtype) kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) ref_res = topi.testing.conv2d_nchw_python( data.astype(out_dtype), kernel.astype(out_dtype), 1, padding, groups=groups) with WinogradFallback(), relay.build_config(opt_level=3): for target, ctx in ctx_list(): if target != 'cuda': continue params = {'w': tvm.nd.array(kernel)} graph, lib, params = relay.build_module.build(mod, target=target, params=params) module = tvm.contrib.graph_runtime.create(graph, lib, ctx) module.set_input('x', tvm.nd.array(data)) module.set_input(**params) module.run() op_res1 = module.get_output(0) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-3, atol=1e-3) # normal winograd: stride 1, padding 1, kernel 3x3 dshape = (1, 80, 73, 73) kshape = (192, 80, 3, 3) run_test_conv2d_cuda("float32", "float32", 1, dshape, kshape, padding=(1, 1), channels=192, kernel_size=(3, 3)) # extended winograd: stride 1, padding N, kernel 3x3 run_test_conv2d_cuda("float32", "float32", 1, dshape, kshape, padding=(0, 0), channels=192, kernel_size=(3, 3)) run_test_conv2d_cuda("float32", "float32", 1, dshape, kshape, padding=(2, 2), channels=192, kernel_size=(3, 3)) # extended winograd: stride 1, padding N, kernel NxN kshape = (192, 80, 7, 7) run_test_conv2d_cuda("float32", "float32", 1, dshape, kshape, padding=(2, 2), channels=192, kernel_size=(7, 7)) def test_conv3d_infer_type(): # symbolic in batch dimension n, c, d, h, w = tvm.size_var("n"), 10, 224, 224, 224 x = relay.var("x", relay.ty.TensorType((n, c, d, h, w), "float32")) w = relay.var("w") y = relay.nn.conv3d(x, w, kernel_size=(3, 3, 3), padding=(1, 1, 1), channels=2) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 224, 224, 224), "float32") assert yy.args[1].checked_type == relay.TensorType( (2, 10, 3, 3, 3), "float32") # infer by shape of w, mixed precision n, c, d, h, w = tvm.size_var("n"), 10, 224, 224, 224 x = relay.var("x", relay.TensorType((n, c, d, h, w), "int8")) w = relay.var("w", relay.TensorType((2, 10, 3, 3, 3), "int8")) y = relay.nn.conv3d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222, 222, 222), "int32") # infer shape in case of different dtypes for input and weight. n, c, d, h, w = tvm.size_var("n"), 10, 224, 224, 224 x = relay.var("x", relay.TensorType((n, c, d, h, w), "uint8")) w = relay.var("w", relay.TensorType((2, 10, 3, 3, 3), "int8")) y = relay.nn.conv3d(x, w, out_dtype="int32") assert "out_dtype=\"int32\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 2, 222, 222, 222), "int32") # Infer with NDHWC n, c, d, h, w = 4, 32, 224, 224, 224 x = relay.var("x", relay.TensorType((n, d, h, w, c), "int8")) wt = relay.var("w") y = relay.nn.conv3d(x, wt, kernel_size=(3, 3, 3), padding=(1, 1, 1), channels=16, data_layout="NDHWC", out_dtype="int32") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, d, h, w, 16), "int32") def test_conv3d_run(): def run_test_conv3d(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1, 1), fref=None, groups=1, dilation=(1, 1, 1), except_targets=None, **attrs): if except_targets is None: except_targets = [] x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", dtype=dtype) y = relay.nn.conv3d(x, w, padding=padding, dilation=dilation, groups=groups, **attrs) func = relay.Function([x, w], y) data = np.random.uniform(-scale, scale, size=dshape).astype(dtype) kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) dkernel = topi.testing.dilate_python(kernel, (1, 1) + dilation) if fref is None: ref_res = topi.testing.conv3d_ncdhw_python( data.astype(out_dtype), dkernel.astype(out_dtype), 1, padding, groups=groups) else: ref_res = fref(data.astype(out_dtype), dkernel.astype(out_dtype)) for target, ctx in ctx_list(): if target in except_targets: continue intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) # normal conv3d dshape = (1, 3, 5, 224, 224) kshape = (10, 3, 3, 3, 3) run_test_conv3d("float32", "float32", 1, dshape, kshape, padding=(1, 1, 1), channels=10, kernel_size=(3, 3 ,3)) def test_conv3d_ndhwc_run(): def run_test_conv3d(dtype, out_dtype, scale, dshape, kshape, padding=(1, 1, 1), fref=None, groups=1, dilation=(1, 1, 1), except_targets=None, **attrs): if except_targets is None: except_targets = [] x = relay.var("x", shape=dshape, dtype=dtype) w = relay.var("w", dtype=dtype) y = relay.nn.conv3d(x, w, padding=padding, dilation=dilation, groups=groups, data_layout="NDHWC", kernel_layout="DHWIO", **attrs) func = relay.Function([x, w], y) data = np.random.uniform(-scale, scale, size=dshape).astype(dtype) kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) dkernel = topi.testing.dilate_python(kernel, (1, 1) + dilation) if fref is None: ref_res = topi.testing.conv3d_ndhwc_python( data.astype(out_dtype), dkernel.astype(out_dtype), 1, padding) else: ref_res = fref(data.astype(out_dtype), dkernel.astype(out_dtype)) for target, ctx in ctx_list(): if target in except_targets: continue intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) # normal conv3d dshape = (1, 5, 224, 224, 6) kshape = (3, 3, 3, 6, 10) run_test_conv3d("float32", "float32", 1, dshape, kshape, padding=(1, 1, 1), channels=10, kernel_size=(3, 3 ,3), except_targets=["cuda"]) def test_conv2d_transpose_infer_type(): # symbolic in batch dimension n, c, h, w = tvm.size_var("n"), 10, 10, 12 x = relay.var("x", relay.TensorType((n, c, h, w), "float32")) w = relay.var("w", relay.IncompleteType()) y = relay.nn.conv2d_transpose(x, w, kernel_size=(3, 3), padding=(1, 1), channels=15) assert "channels=15" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 15, 10, 12), "float32") assert yy.args[1].checked_type == relay.TensorType( (10, 15, 3, 3), "float32") # infer by shape of w, mixed precision n, h, w, c = tvm.size_var("n"), 10, 10, 12 x = relay.var("x", relay.TensorType((n, h, w, c), "float32")) w = relay.var("w", relay.TensorType((12, 11, 5, 5), "float32")) y = relay.nn.conv2d_transpose(x, w, output_padding=(1, 1), channels=11, data_layout="NHWC") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 15, 15, 11), "float32") def test_conv2d_transpose_nchw_run(): dshape = (1, 3, 18, 18) kshape = (3, 10, 3, 3) oshape = (1, 10, 37, 37) x = relay.var("x", shape=dshape) w = relay.var("w") y = relay.nn.conv2d_transpose(x, w, channels=10, kernel_size=(3,3), strides=(2,2), padding=(1,1), output_padding=(2, 2)) func = relay.Function([x, w], y) dtype = "float32" data = np.random.uniform(size=dshape).astype(dtype) kernel = np.random.uniform(size=kshape).astype(dtype) c_np = topi.testing.conv2d_transpose_nchw_python( data, kernel, 2, 1) d_np = np.zeros(shape=oshape) d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np ref_res = d_np for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def test_conv2d_transpose_nhwc_run(): dshape_nhwc = (1, 18, 18, 3) kshape_hwoi = (3, 3, 10, 3) oshape_nhwc = (1, 37, 37, 10) x = relay.var("x", shape=dshape_nhwc) w = relay.var("w") # kshape and kernel_layout should have swapped IO. # kshape is HWOI and kernel_layout is HWIO y = relay.nn.conv2d_transpose(x, w, channels=10, kernel_size=(3, 3), strides=(2, 2), padding=(1, 1), output_padding=(2, 2), data_layout="NHWC", kernel_layout="HWIO") func = relay.Function([x, w], y) dtype = "float32" data = np.random.uniform(size=dshape_nhwc).astype(dtype) kernel = np.random.uniform(size=kshape_hwoi).astype(dtype) # use true kshape layout here - HWOI c_np = topi.testing.conv2d_transpose_nhwc_python(data, kernel, 'HWOI', 2, 1) d_np = np.zeros(shape=oshape_nhwc) d_np[:,0:c_np.shape[1],0:c_np.shape[2],:] = c_np def test_conv1d_transpose_ncw_run(): dshape = (1, 3, 18) kshape = (3, 10, 3) oshape = (1, 10, 37) x = relay.var("x", shape=dshape) w = relay.var("w") y = relay.nn.conv1d_transpose(x, w, channels=10, kernel_size=(3,), strides=(2,), padding=(1,), output_padding=(2,)) func = relay.Function([x, w], y) dtype = "float32" data = np.random.uniform(size=dshape).astype(dtype) kernel = np.random.uniform(size=kshape).astype(dtype) c_np = topi.testing.conv1d_transpose_ncw_python( data, kernel, 2, 1) d_np = np.zeros(shape=oshape) d_np[:,:,0:c_np.shape[2]] = c_np ref_res = d_np for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data, kernel) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def test_upsampling_infer_type(): n, c , h, w = tvm.size_var("n"), tvm.size_var("c"), tvm.size_var("h"), tvm.size_var("w") scale = tvm.const(2.0, "float64") x = relay.var("x", relay.TensorType((n, c, h, w), "float32")) y = relay.nn.upsampling(x, scale_h=2, scale_w=2, layout="NCHW", method="bilinear") "method=\"BINLINEAR\"" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c, tvm.expr.Cast("int32", tvm.round(h*scale)), tvm.expr.Cast("int32", tvm.round(w*scale))), "float32") n, c = tvm.size_var("n"), tvm.size_var("c") x = relay.var("x", relay.TensorType((n, c, 100, 200), "float32")) y = relay.nn.upsampling(x, scale_h=2, scale_w=2, layout="NCHW", method="bilinear") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c, 200, 400), "float32") def test_upsampling3d_infer_type(): n, c, d, h, w = tvm.size_var("n"), tvm.size_var("c"),\ tvm.size_var("d"), tvm.size_var("h"), tvm.size_var("w") scale = tvm.const(2.0, "float64") x = relay.var("x", relay.TensorType((n, c, d, h, w), "float32")) y = relay.nn.upsampling3d(x, scale_d=2, scale_h=2, scale_w=2, layout="NCDHW", method="trilinear") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c, tvm.expr.Cast("int32", tvm.round(d*scale)), tvm.expr.Cast("int32", tvm.round(h*scale)), tvm.expr.Cast("int32", tvm.round(w*scale))), "float32") n, c = tvm.size_var("n"), tvm.size_var("c") x = relay.var("x", relay.TensorType((n, c, 100, 100, 200), "float32")) y = relay.nn.upsampling3d(x, scale_d=2, scale_h=2, scale_w=2, layout="NCDHW", method="trilinear") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c, 200, 200, 400), "float32") def _test_pool2d(opfunc, reffunc): n, c, h, w = tvm.size_var("n"), 10, 224, 224 x = relay.var("x", relay.TensorType((n, c, h, w), "float32")) y = opfunc(x, pool_size=(1, 1)) assert "pool_size=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, 10, 224, 224), "float32") # test execution dtype = "float32" dshape = (1, 3, 28, 28) x = relay.var("x", shape=dshape) y = opfunc(x, pool_size=(2, 2), strides=(2, 2), padding=(0, 0)) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) ref_res = reffunc(data.reshape(1, 3, 14, 2, 14, 2), axis=(3, 5)) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def _test_pool2d_int(opfunc, reffunc, dtype): n, c, h, w = tvm.size_var("n"), 10, 224, 224 x = relay.var("x", relay.TensorType((n, c, h, w), dtype)) y = opfunc(x, pool_size=(1, 1)) assert "pool_size=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, 10, 224, 224), dtype) # test execution dtype = "int32" dshape = (1, 3, 28, 28) x = relay.var("x", shape=dshape, dtype=dtype) y = opfunc(x, pool_size=(2, 2), strides=(2, 2), padding=(0, 0)) func = relay.Function([x], y) data = np.random.random_integers(low=-128, high=128, size=dshape) ref_res = reffunc(data.reshape(1,3,14,2,14,2), axis=(3,5)).astype(dtype) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def _test_global_pool2d(opfunc, reffunc): n, c, h, w = tvm.size_var("n"), tvm.size_var("c"), 224, 224 x = relay.var("x", relay.TensorType((n, h, w, c), "float32")) y = opfunc(x, layout="NHWC") yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, 1, 1, c), "float32") n, c, h, w = tvm.size_var("n"), tvm.size_var("c"), tvm.size_var("h"), tvm.size_var("w") x = relay.var("x", relay.TensorType((n, c, h, w), "float32")) y = opfunc(x) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c, 1, 1), "float32") # test execution dtype = "float32" dshape = (1, 1024, 7, 7) x = relay.var("x", shape=dshape) y = opfunc(x) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) ref_res = reffunc(data, axis=(2,3), keepdims=True) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def test_pool2d(): _test_pool2d(relay.nn.max_pool2d, np.max) _test_pool2d(relay.nn.avg_pool2d, np.mean) _test_pool2d_int(relay.nn.avg_pool2d, np.mean, 'int32') _test_pool2d_int(relay.nn.avg_pool2d, np.mean, 'uint16') _test_global_pool2d(relay.nn.global_max_pool2d, np.max) _test_global_pool2d(relay.nn.global_avg_pool2d, np.mean) def test_pool1d(): def _test_pool1d(opfunc): n, c, w = tvm.var("n"), 10, 224 x = relay.var("x", relay.TensorType((n, c, w), "float32")) y = opfunc(x, pool_size=(1,)) assert "pool_size=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, 10, 224), "float32") # test execution dtype = "float32" dshape = (1, 3, 32) x = relay.var("x", shape=dshape) pool_type = 'max' if 'max' in str(opfunc) else 'avg' y = opfunc(x, pool_size=(2,), strides=(2,), padding=(0, 0)) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) ref_res = topi.testing.pool1d_ncw_python(data, (2,), (2,), (0, 0), (1, 3, 16), pool_type, False) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) _test_pool1d(relay.nn.max_pool1d) _test_pool1d(relay.nn.avg_pool1d) def test_pool3d(): def _test_pool3d(opfunc, padding=(0, 0, 0, 0, 0, 0), out_shape=(1, 3, 16, 16, 16)): n, c, d, h, w = tvm.size_var("n"), 10, 5, 224, 224 x = relay.var("x", relay.TensorType((n, c, d, h, w), "float32")) y = opfunc(x, pool_size=(1, 1, 1)) assert "pool_size=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, 10, 5, 224, 224), "float32") # test execution dtype = "float32" dshape = (1, 3, 32, 32, 32) x = relay.var("x", shape=dshape) pool_type = 'max' if 'max' in str(opfunc) else 'avg' y = opfunc(x, pool_size=(2, 2, 2), strides=(2, 2, 2), padding=padding) func = relay.Function([x], y) # check output shape f_out_shape = tuple(map(lambda x: int(x), run_infer_type(func).ret_type.shape)) assert out_shape == f_out_shape, \ "Output shape mismatch. expected {}, actual {}".format(out_shape, f_out_shape) data = np.random.uniform(size=dshape).astype(dtype) ref_res = topi.testing.pool3d_ncdhw_python(data, (2, 2, 2), (2, 2, 2), padding, out_shape, pool_type, False) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) _test_pool3d(relay.nn.max_pool3d) _test_pool3d(relay.nn.max_pool3d, padding=(2, 0, 0, 2, 0, 0), out_shape=(1, 3, 18, 16, 16)) _test_pool3d(relay.nn.max_pool3d, padding=(0, 3, 0, 0, 3, 0), out_shape=(1, 3, 16, 19, 16)) _test_pool3d(relay.nn.max_pool3d, padding=(0, 0, 4, 0, 0, 4), out_shape=(1, 3, 16, 16, 20)) _test_pool3d(relay.nn.avg_pool3d) _test_pool3d(relay.nn.avg_pool3d, padding=(2, 0, 0, 2, 0, 0), out_shape=(1, 3, 18, 16, 16)) _test_pool3d(relay.nn.avg_pool3d, padding=(0, 3, 0, 0, 3, 0), out_shape=(1, 3, 16, 19, 16)) _test_pool3d(relay.nn.avg_pool3d, padding=(0, 0, 4, 0, 0, 4), out_shape=(1, 3, 16, 16, 20)) def test_avg_pool2d_no_count_pad(): kh, kw = (4, 4) sh, sw = (2, 2) ph, pw = (2, 2) n = 1 (ic, ih, iw) = (3, 28, 28) (oc, oh, ow) = (3, 15, 15) dshape = (n, ic, ih, iw) x = relay.var("x", shape=dshape) y = relay.nn.avg_pool2d(x, pool_size=(kh, kw), strides=(sw, sw), padding=(ph, pw), count_include_pad=False) func = relay.Function([x], y) dtype = "float32" a_np = np.random.uniform(low=0.001, size=(n, ic, ih, iw)).astype(dtype) pad_np = np.zeros(shape=(n, ic, ih+2*ph, iw+2*pw)).astype(dtype) no_zero = (range(n), range(ic), (range(ph, ih+ph)), (range(pw, iw+pw))) pad_np[np.ix_(*no_zero)] = a_np b_np = np.zeros(shape=(n, oc, oh, ow)).astype(dtype) for i in range(oh): for j in range(ow): pad_count = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw] > 0, axis=(2,3)) b_np[:,:,i,j] = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3)) / np.maximum(pad_count, 1) ref_res = np.maximum(b_np, 0.0) data = a_np for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) def test_flatten_infer_type(): d1, d2, d3, d4 = tvm.size_var("d1"), tvm.size_var("d2"), tvm.size_var("d3"), tvm.size_var("d4") x = relay.var("x", relay.TensorType((d1, d2, d3, d4), "float32")) y = relay.nn.batch_flatten(x) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((d1, ((d2*d3)*d4)), "float32") x = relay.var("x", relay.TensorType((3, 2, 4, 3), "float32")) y = relay.nn.batch_flatten(x) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((3, 24), "float32") x = relay.var("x", relay.TensorType((d1, 2, d3, 3), "float32")) y = relay.nn.batch_flatten(x) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((d1, ((2*d3)*3)), "float32") shape = (1, 5, 10, 10) o_shape = (1, 500) dtype = "float32" x = relay.var("x", relay.TensorType(shape, dtype)) z = relay.nn.batch_flatten(x) yy = run_infer_type(z) assert yy.checked_type == relay.TensorType(o_shape, dtype) func = relay.Function([x], z) x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype) ref_res = x_data.flatten().reshape(o_shape) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) intrp2 = relay.create_executor("debug", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5) op_res2 = intrp2.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5) def test_pad_infer_type(): # entirely concrete case n, c, h, w = 1, 2, 3, 4 t = relay.var("t", relay.TensorType((n, c, h, w), "float32")) y = relay.nn.pad(t, ((1, 1), (2, 2), (3, 3), (4, 4))) "pad_width=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((3, 6, 9, 12), "float32") # some symbolic values n, c, h, w = tvm.size_var("n"), 2, 3, tvm.size_var("w") t = relay.var("t", relay.TensorType((n, c, h, w), "float32")) y = relay.nn.pad(t, ((1, 1), (2, 2), (3, 3), (4, 4))) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n + 2, 6, 9, w + 8), "float32") def test_pad_run(): def _test_run(dtype): dshape = (4, 10, 7, 7) x = relay.var("x", shape=dshape) y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4))) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) ref_res = np.pad(data, ((1, 1), (2, 2), (3, 3), (4, 4)), 'constant') for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5) _test_run('float32') _test_run('int32') def test_lrn(): n, c , h, w = tvm.size_var("n"), tvm.size_var("c"), tvm.size_var("h"), tvm.size_var("w") x = relay.var("x", shape=(n, c , h, w)) y = relay.nn.lrn(x, size=10, axis=2, bias=0.5, alpha=.00001, beta=0.75) "alpha=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c , h, w)) shape = (1, 5, 10, 10) dtype = "float32" x = relay.var("x", relay.TensorType(shape, dtype)) size=5 axis=1 bias=0.5 alpha=.00001 beta=0.75 z = relay.nn.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta) yy = run_infer_type(z) assert yy.checked_type == relay.TensorType(shape, dtype) func = relay.Function([x], z) x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype) ref_res = topi.testing.lrn_python(x_data, size, axis, bias, alpha, beta) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) intrp2 = relay.create_executor("debug", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5) op_res2 = intrp2.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5) def test_l2_normalize(): n, c , h, w = tvm.size_var("n"), tvm.size_var("c"), tvm.size_var("h"), tvm.size_var("w") x = relay.var("x", shape=(n, c , h, w)) y = relay.nn.l2_normalize(x, eps=0.001, axis=[1]) "axis=" in y.astext() yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n, c , h, w)) shape = (1, 5, 10, 10) dtype = "float32" x = relay.var("x", relay.TensorType(shape, dtype)) eps=0.001 axis=1 z = relay.nn.l2_normalize(x, eps=0.001, axis=[axis]) yy = run_infer_type(z) assert yy.checked_type == relay.TensorType(shape, dtype) func = relay.Function([x], z) x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype) ref_res = topi.testing.l2_normalize_python(x_data, eps, axis) for target, ctx in ctx_list(): intrp1 = relay.create_executor("graph", ctx=ctx, target=target) intrp2 = relay.create_executor("debug", ctx=ctx, target=target) op_res1 = intrp1.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5) op_res2 = intrp2.evaluate(func)(x_data) tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5) def batch_flatten(data): shape = data.shape target_dim = 1 for i in range(len(shape) - 1): target_dim = target_dim * shape[i + 1] return np.reshape(data, (shape[0], target_dim)) def test_batch_flatten(): t1 = relay.TensorType((5, 10, 5)) x = relay.Var("x", t1) func = relay.Function([x], relay.nn.batch_flatten(x)) data = np.random.rand(5, 10, 5).astype(t1.dtype) ref_res = batch_flatten(data) for target, ctx in ctx_list(): intrp = relay.create_executor("graph", ctx=ctx, target=target) op_res = intrp.evaluate(func)(data) np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01) def _test_upsampling(layout, method, align_corners=False): n, c, h, w = tvm.size_var("n"), 16, 32, 32 scale_h = 2.0 scale_w = 2.0 dtype = "float32" def get_shape(): if layout == "NCHW": return (c, h, w), (c, int(round(h*scale_h)), int(round(w*scale_w))) else: return (h, w, c), (int(round(h*scale_h)), int(round(w*scale_w)), c) ishape, oshape = get_shape() x = relay.var("x", relay.TensorType((n,) + ishape, dtype)) y = relay.nn.upsampling(x, scale_h=scale_h, scale_w=scale_w, layout=layout, method=method, align_corners=align_corners) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n,) + oshape, dtype) dshape = (1,) + ishape x = relay.var("x", shape=dshape) y = relay.nn.upsampling(x, scale_h=scale_h, scale_w=scale_w, layout=layout, method=method, align_corners=align_corners) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) if method == "nearest_neighbor": ref = topi.testing.upsampling_python(data, (scale_h, scale_w), layout) else: ref = topi.testing.bilinear_resize_python(data, (int(round(h*scale_h)), int(round(w*scale_w))), layout) for target, ctx in ctx_list(): executor = relay.create_executor("graph", ctx=ctx, target=target) out = executor.evaluate(func)(data) tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5, atol=1e-5) def test_upsampling(): _test_upsampling("NCHW", "nearest_neighbor") _test_upsampling("NCHW", "bilinear", True) _test_upsampling("NHWC", "nearest_neighbor") _test_upsampling("NHWC", "bilinear", True) def _test_upsampling3d(layout, method, coordinate_transformation_mode="half_pixel"): n, c, d, h, w = tvm.size_var("n"), 8, 16, 16, 16 scale_d = 2.0 scale_h = 2.0 scale_w = 2.0 dtype = "float32" def get_shape(): if layout == "NCDHW": return (c, d, h, w), (c, int(round(d*scale_d)), int(round(h*scale_h)),\ int(round(w*scale_w))) else: return (d, h, w, c), (int(round(d*scale_d)), int(round(h*scale_h)),\ int(round(w*scale_w)), c) ishape, oshape = get_shape() x = relay.var("x", relay.TensorType((n,) + ishape, dtype)) y = relay.nn.upsampling3d(x, scale_d=scale_d, scale_h=scale_h, scale_w=scale_w,\ layout=layout, method=method,\ coordinate_transformation_mode=coordinate_transformation_mode) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType((n,) + oshape, dtype) dshape = (1,) + ishape x = relay.var("x", shape=dshape) y = relay.nn.upsampling3d(x, scale_d=scale_d, scale_h=scale_h, scale_w=scale_w,\ layout=layout, method=method,\ coordinate_transformation_mode=coordinate_transformation_mode) func = relay.Function([x], y) data = np.random.uniform(size=dshape).astype(dtype) if method == "nearest_neighbor": ref = topi.testing.upsampling3d_python(data, (scale_d, scale_h, scale_w), layout) else: ref = topi.testing.trilinear_resize3d_python(data, (int(round(d*scale_d)),\ int(round(h*scale_h)),\ int(round(w*scale_w))), layout) for target, ctx in ctx_list(): executor = relay.create_executor("graph", ctx=ctx, target=target) out = executor.evaluate(func)(data) tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5, atol=1e-5) def test_upsampling3d(): _test_upsampling3d("NCDHW", "nearest_neighbor") _test_upsampling3d("NCDHW", "trilinear", "align_corners") _test_upsampling3d("NDHWC", "nearest_neighbor") _test_upsampling3d("NDHWC", "trilinear", "align_corners") def test_conv2d_int8_intrinsics(): def _compile(ic, oc, target, data_layout, kernel_layout, dtypes): input_dtype, weight_dtype, output_dtype = dtypes n, h, w, ch, cw = 1, 64, 64, 3, 3 if data_layout == 'NCHW': data_shape = (n, ic, h, w) x = relay.var("x", relay.TensorType(data_shape, input_dtype)) elif data_layout == 'NHWC': data_shape = (n, h, w, ic) x = relay.var("x", relay.TensorType(data_shape, input_dtype)) else: raise ValueError('Not supported') if kernel_layout == 'OIHW': kernel_shape = (oc, ic, ch, cw) elif kernel_layout == 'HWIO': kernel_shape = (ch, cw, ic, oc) else: raise ValueError('Not supported') weight = relay.var("weight", relay.TensorType(kernel_shape, weight_dtype)) y = relay.nn.conv2d(x, weight, kernel_size=(ch, cw), channels=oc, padding=(1, 1), dilation=(1, 1), data_layout=data_layout, kernel_layout=kernel_layout, out_dtype=output_dtype) func = relay.Function([x, weight], y) wdata = np.random.rand(*kernel_shape) * 10 parameters = {"weight": tvm.nd.array(wdata.astype(weight_dtype))} with relay.build_config(opt_level=3): graph, lib, params = relay.build(func, target, params=parameters) assembly = lib.get_source("asm") return assembly def _has_fast_int8_instructions(asm, target): if 'skylake-avx512' in target: return "pmaddubs" in asm elif 'cascadelake' in target: return "vpdpbusd" in asm else: assert False, "Target should be Skylake or Cascadelake" # compile conv2d for x86 (skylake, cascadelake) and test assembly contains *pmadd* instructions targets = ["llvm -mcpu=skylake-avx512", "llvm -mcpu=cascadelake"] llvm_version = tvm.codegen.llvm_version_major() for target in targets: if llvm_version >= 8: dtypes = ('uint8', 'int8', 'int32') # Sweep the input channels to check int8 robustness # Input channels should be a multiple of 4 internally. for ic in [1, 4, 6]: asm = _compile(ic=ic, oc=16, target=target, data_layout="NCHW", kernel_layout='OIHW', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) for ic in [1, 4, 6]: asm = _compile(ic=ic, oc=16, target=target, data_layout="NHWC", kernel_layout='HWIO', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) # Sweep the output channels to check int8 robustness # Output channels should be a multiple of 16 internally. for oc in [4, 16, 20]: asm = _compile(ic=8, oc=oc, target=target, data_layout="NCHW", kernel_layout='OIHW', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) for oc in [4, 16, 20]: asm = _compile(ic=8, oc=oc, target=target, data_layout="NHWC", kernel_layout='HWIO', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) # Check that both non-divisible oc and ic work asm = _compile(ic=17, oc=29, target=target, data_layout="NCHW", kernel_layout='OIHW', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) asm = _compile(ic=17, oc=29, target=target, data_layout="NHWC", kernel_layout='HWIO', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) # Check that int8 x int8 goes through legalization so that fast instructions can be picked up. for target in targets: if llvm_version >= 8: dtypes = (('int8', 'int8', 'int32')) # Check that both non-divisible oc and ic work asm = _compile(ic=17, oc=29, target=target, data_layout="NCHW", kernel_layout='OIHW', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) asm = _compile(ic=17, oc=29, target=target, data_layout="NHWC", kernel_layout='HWIO', dtypes=dtypes) assert _has_fast_int8_instructions(asm, target) # Ensure that code is generated when datatypes are not HW supported. dtypes = ('uint8', 'uint8', 'int32') asm = _compile(ic=16, oc=32, target=target, data_layout="NHWC", kernel_layout='HWIO', dtypes=dtypes) # Check that intrinisic is not present in the assembly. assert not _has_fast_int8_instructions(asm, target) # Check that a vectorized instruction is generated for older Intel # generations, because we default to NCHWc layout. target = "llvm -mcpu=core-avx2" fast_int8_dtypes = ('uint8', 'int8', 'int32') asm = _compile(ic=16, oc=32, target=target, data_layout="NCHW", kernel_layout='OIHW', dtypes=fast_int8_dtypes) # Check that vector int mult and add instructions are generated. assert "vpmulld" in asm and "vpadd" in asm def test_depthwise_conv2d_int8(): input_dtype = 'uint8' weight_dtype = 'int8' output_dtype = 'int32' data_shape = (1, 64, 56, 56) x = relay.var("x", relay.TensorType(data_shape, input_dtype)) kernel_shape = (64, 1, 3, 3) weight = relay.var("weight", relay.TensorType(kernel_shape, weight_dtype)) y = relay.nn.conv2d(x, weight, kernel_size=(3, 3), groups=64, padding=(1, 1), dilation=(1, 1), out_dtype=output_dtype) func = relay.Function([x, weight], y) wdata = np.random.rand(*kernel_shape) * 10 parameters = {"weight": tvm.nd.array(wdata.astype(weight_dtype))} targets = ["llvm -mcpu=skylake-avx512", "llvm -mcpu=cascadelake"] llvm_version = tvm.codegen.llvm_version_major() for target in targets: if llvm_version >= 8: with relay.build_config(opt_level=3): graph, lib, params = relay.build(func, target, params=parameters) def test_bitserial_conv2d_infer_type(): # Basic shape test with ambiguous batch. n, c, h, w = tvm.size_var("n"), 32, 224, 224 x = relay.var("x", relay.ty.TensorType((n, c, h, w), "int16")) w = relay.var("w", relay.ty.TensorType((32, 32, 3, 3), "int16")) y = relay.nn.bitserial_conv2d( x, w, kernel_size=(3, 3), padding=(0, 0), channels=32) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (n, 32, 222, 222), "int16") def test_bitpack_infer_type(): # Test axis packing shape inference. o, i, h, w = 32, 32, 128, 128 x = relay.var("x", relay.ty.TensorType((o, i, h, w), "int16")) y = relay.nn.bitpack(x, bit_axis=4, pack_axis=1, pack_type='uint16', bits=1) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType( (32, 2, 128, 128, 1), "uint16") if __name__ == "__main__": test_pool1d() test_pool2d() test_pool3d() test_avg_pool2d_no_count_pad() test_lrn() test_l2_normalize() test_conv1d_infer_type() test_conv2d_infer_type() test_conv3d_infer_type() test_bitpack_infer_type() test_upsampling_infer_type() test_upsampling3d_infer_type() test_flatten_infer_type() test_pad_infer_type() test_pad_run() test_conv2d_transpose_infer_type() test_conv2d_transpose_nchw_run() test_conv2d_transpose_nhwc_run() test_conv1d_transpose_ncw_run() test_conv1d_run() test_conv2d_run() test_conv2d_winograd() test_conv3d_run() test_conv3d_ndhwc_run() test_bitserial_conv2d_infer_type() test_batch_flatten() test_upsampling() test_upsampling3d() test_conv2d_int8_intrinsics() test_depthwise_conv2d_int8()

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0ad33111935325f80d27dfada02fe97074254f24

2,206

py

Python

qf_lib/containers/futures/future_contract.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

198

2019-08-16T15:09:23.000Z

2022-03-30T12:44:00.000Z

qf_lib/containers/futures/future_contract.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

13

2021-01-07T10:15:19.000Z

2022-03-29T13:01:47.000Z

qf_lib/containers/futures/future_contract.py

webclinic017/qf-lib

96463876719bba8a76c8269cef76addf3a2d836d

[ "Apache-2.0" ]

29

2019-08-16T15:21:28.000Z

2022-02-23T09:53:49.000Z

# Copyright 2016-present CERN – European Organization for Nuclear Research # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from datetime import datetime from qf_lib.common.tickers.tickers import Ticker from qf_lib.containers.dataframe.prices_dataframe import PricesDataFrame class FutureContract(object): """ Class representing a single future contract. The FutureContract is a simple class representing one futures contract. The FutureContract objects are used by the FuturesChain, in order to provide the contracts chaining possibilities. It requires 3 parameters: ticker, which is the symbol of the specific future contract (e.g. BloombergFutureTicker(“CTZ9 Comdty”)), expiration date of the contract and a PricesDataFrame, containing dates with price field values. Parameters ---------- ticker: Ticker symbol of the future contract exp_date: datetime expiration date data: PricesDataFrame data frame containing dates with price fields values """ def __init__(self, ticker: Ticker, exp_date: datetime, data: PricesDataFrame): self.ticker = ticker self.exp_date = exp_date self.data = data def __str__(self): return 'Contract: ticker: {}, expiration date: {}'.format( self.ticker, self.exp_date) def __eq__(self, other): if self is other: return True if not isinstance(other, FutureContract): return False return (self.ticker, self.exp_date, self.data) == (other.ticker, other.exp_date, other.data) def __hash__(self): return hash((self.ticker, self.exp_date, self.data))

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null

0

1

0

1

0

3

0ae277577c0d9cf0180a37747d11d8dcd292baa5

57

py

Python

player.py

Drayux/Battlematus

1709a15b58d9274b99ec36eff1a181014d155037

[ "MIT" ]

null

player.py

Drayux/Battlematus

1709a15b58d9274b99ec36eff1a181014d155037

[ "MIT" ]

null

player.py

Drayux/Battlematus

1709a15b58d9274b99ec36eff1a181014d155037

[ "MIT" ]

null

# PLAYER class player: def __init__(self):

9.5

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null

0

1

0

3

0ae3d125da916faaaf9490284cbbfda3ebc0f150

1,735

py

Python

soupy/approximations/taylor/backup/__init__.py

cpempire/soupy

9f65e3329fa126619c893daa4cd80478d83f840c

[ "MIT" ]

1

2021-12-07T15:22:23.000Z

soupy/approximations/taylor/backup/__init__.py

cpempire/soupy

9f65e3329fa126619c893daa4cd80478d83f840c

[ "MIT" ]

null

soupy/approximations/taylor/backup/__init__.py

cpempire/soupy

9f65e3329fa126619c893daa4cd80478d83f840c

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term # 1. implement linear # 2. implement quadratic # 3. impelement SAA # to do list # 1. SAA does not run well in ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint solver works # 2. quadratic approximation does not converge well, even without variance, does not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction # what to show tomorrow # 1. variance reduction by mean square error # 2. trace estimation by MC and randomized SVD # 3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show the design and state, for both disk and submarine # 5. random sample and state at different design # April 9, 2018, work on reporting results # 1. random samples and states at different design # 2. table for variance reduction # 3. plot trace estimation # 4. plot #bfgs iterations # obtain all results as planned

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0afd820091335019ca4a87a89952513413136cc0

69

py

Python

src/metarl/tf/plotter/__init__.py

icml2020submission6857/metarl

9b66cefa2b6bcb6a38096d629ce8853b47c7171d

[ "MIT" ]

2

2020-03-15T14:35:15.000Z

2021-02-15T16:38:00.000Z

src/metarl/tf/plotter/__init__.py

neurips2020submission11699/metarl

ae4825d21478fa1fd0aa6b116941ea40caa152a5

[ "MIT" ]

null

src/metarl/tf/plotter/__init__.py

neurips2020submission11699/metarl

ae4825d21478fa1fd0aa6b116941ea40caa152a5

[ "MIT" ]

1

2020-02-24T03:04:23.000Z

from metarl.tf.plotter.plotter import Plotter __all__ = ['Plotter']

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45

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null

0

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null

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1

0

3

7c159cac6567c00ed5a82a064ec8c65b30f68447

1,595

py

Python

economist/migrations/0003_auto_20170406_1402.py

xingjianpan/news_reader_backend

c892e157460ef22720bfcbad5a7d2bfe9bcd4aa9

[ "MIT" ]

1

2017-11-01T02:12:24.000Z

economist/migrations/0003_auto_20170406_1402.py

xingjianpan/news_reader_backend

c892e157460ef22720bfcbad5a7d2bfe9bcd4aa9

[ "MIT" ]

null

economist/migrations/0003_auto_20170406_1402.py

xingjianpan/news_reader_backend

c892e157460ef22720bfcbad5a7d2bfe9bcd4aa9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.6 on 2017-04-06 06:02 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('economist', '0002_auto_20170406_1153'), ] operations = [ migrations.AlterField( model_name='article', name='alternativename', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='article', name='category', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='article', name='fly_title', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='article', name='headline', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='article', name='project', field=models.TextField(editable=False), ), migrations.AlterField( model_name='article', name='source', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='article', name='source_url', field=models.URLField(editable=False), ), migrations.AlterField( model_name='article', name='spider', field=models.TextField(editable=False), ), ]

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1

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null

0

3

7c16097e2ba8634058cfc608cf9a3d535fa94016

2,051

py

Python

test/test_ethereum.py

coinplus-sa/coinplus-solo

e4f385a3d9eb7b72e14e397761fd9a113938917a

[ "MIT" ]

1

2018-08-21T06:28:36.000Z

test/test_ethereum.py

coinplus-sa/coinplus-solo

e4f385a3d9eb7b72e14e397761fd9a113938917a

[ "MIT" ]

1

2019-05-30T06:23:41.000Z

2019-09-03T09:49:06.000Z

test/test_ethereum.py

coinplus-sa/coinplus-solo

e4f385a3d9eb7b72e14e397761fd9a113938917a

[ "MIT" ]

1

2021-06-30T12:36:25.000Z

import unittest from coinplus_solo_redeem.common import wif_export_bitcoin, compute_public_key_sec256k1, address_from_publickey_ethereum class TestEthereum(unittest.TestCase): """test of the bitcoin conversion from private key to wif""" def setUp(self): self.test_add_vector = [("03cb3e5f30245658e1e3615f1620e5b40f7d9016c0edb3611dd786327dd5e40caa", "0xfd965bB8907566c550D8C0325207a1cB744f2fc2"), ("03c2773e19b0cd4175832d781d521390e5aac7b0841904f93211bf114786f5a145", "0xDB1F8a8B668F15B9e696dDfF30Ce233703f9eC97"), ("0277c3757e791426b7fa43cf64197bfd5c2fe277ece721b12558a52729f6b68b8a", "0x6C4DCd1f900d89a7A70C9A5bA9F7a24a4Bd70878"), ("02d93dfcd93a76d7bac5b0fa394ad4bfd6cd92d10a64728b4b5f707d87db9cd2aa", "0x42F7C7ccD753055c219B85ddc5F05512b3f94528"), ("037049004c5ad576beb518dcc74506df3faf520109a489886b7d1435a63b9b0b88", "0x0af4DbEf58063AEd75e6fF57610348E55954E8FB"), ("0260bbacc03555af21f062ff04e9fbde36bcf0ae7396812d336e7f2e5292306f2b", "0xd13AA41456549AAf4F00C681e014E8CEd8c04d60"), ("0343710601de0710dd81a0b7102bf1b794809a330caf4e1b4ae6567923c00df6a5", "0x011934E5d9EE8C230BBFccF33Ab83c62E5486d91"), ("028c48ff458287f34cc1ad5c58a441500f8f315e9cabe34ff1601a5a0f791e4d0a", "0x98447B7aC721BDeb197a7e72780f6f41BECA2919"), ("0258cdabe1dad468dda6a7d62bee9e0cddadfe87d664e62df9143e769c017dd651", "0xaA5EacE5be0D09B09BAf66df62b0D85EA20b4ee4"), ("0289a6d2272382ceec291674530eebb1b05dadab88ebf1bc45569ba612a4e3973a", "0x79B4044CeB2DFAa123FbE5B4da43BF7cFF01718c")] def test_address_testvector(self): for publickey_hex, address_expected in self.test_add_vector: publickey = bytearray.fromhex(publickey_hex) address = address_from_publickey_ethereum(publickey) self.assertEqual(address, address_expected)

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1

null

0

1

0

1

null

0

3

7c1ee1ca0bd0d4b48cc0fd831915fd050efb4c03

7,323

py

Python

clients/kratos/python/test/test_v0alpha1_api.py

kolotaev/sdk

0dda1becd70be8d7b9d678321ebe780c1ba00485

[ "Apache-2.0" ]

null

clients/kratos/python/test/test_v0alpha1_api.py

kolotaev/sdk

0dda1becd70be8d7b9d678321ebe780c1ba00485

[ "Apache-2.0" ]

null

clients/kratos/python/test/test_v0alpha1_api.py

kolotaev/sdk

0dda1becd70be8d7b9d678321ebe780c1ba00485

[ "Apache-2.0" ]

null

""" Ory Kratos API Documentation for all public and administrative Ory Kratos APIs. Public and administrative APIs are exposed on different ports. Public APIs can face the public internet without any protection while administrative APIs should never be exposed without prior authorization. To protect the administative API port you should use something like Nginx, Ory Oathkeeper, or any other technology capable of authorizing incoming requests. # noqa: E501 The version of the OpenAPI document: v0.7.0-alpha.1 Contact: [email protected] Generated by: https://openapi-generator.tech """ import unittest import ory_kratos_client from ory_kratos_client.api.v0alpha1_api import V0alpha1Api # noqa: E501 class TestV0alpha1Api(unittest.TestCase): """V0alpha1Api unit test stubs""" def setUp(self): self.api = V0alpha1Api() # noqa: E501 def tearDown(self): pass def test_admin_create_identity(self): """Test case for admin_create_identity Create an Identity # noqa: E501 """ pass def test_admin_create_self_service_recovery_link(self): """Test case for admin_create_self_service_recovery_link Create a Recovery Link # noqa: E501 """ pass def test_admin_delete_identity(self): """Test case for admin_delete_identity Delete an Identity # noqa: E501 """ pass def test_admin_get_identity(self): """Test case for admin_get_identity Get an Identity # noqa: E501 """ pass def test_admin_list_identities(self): """Test case for admin_list_identities List Identities # noqa: E501 """ pass def test_admin_update_identity(self): """Test case for admin_update_identity Update an Identity # noqa: E501 """ pass def test_create_self_service_logout_flow_url_for_browsers(self): """Test case for create_self_service_logout_flow_url_for_browsers Create a Logout URL for Browsers # noqa: E501 """ pass def test_get_json_schema(self): """Test case for get_json_schema """ pass def test_get_self_service_error(self): """Test case for get_self_service_error Get Self-Service Errors # noqa: E501 """ pass def test_get_self_service_login_flow(self): """Test case for get_self_service_login_flow Get Login Flow # noqa: E501 """ pass def test_get_self_service_recovery_flow(self): """Test case for get_self_service_recovery_flow Get Recovery Flow # noqa: E501 """ pass def test_get_self_service_registration_flow(self): """Test case for get_self_service_registration_flow Get Registration Flow # noqa: E501 """ pass def test_get_self_service_settings_flow(self): """Test case for get_self_service_settings_flow Get Settings Flow # noqa: E501 """ pass def test_get_self_service_verification_flow(self): """Test case for get_self_service_verification_flow Get Verification Flow # noqa: E501 """ pass def test_initialize_self_service_login_flow_for_browsers(self): """Test case for initialize_self_service_login_flow_for_browsers Initialize Login Flow for Browsers # noqa: E501 """ pass def test_initialize_self_service_login_flow_without_browser(self): """Test case for initialize_self_service_login_flow_without_browser Initialize Login Flow for APIs, Services, Apps, ... # noqa: E501 """ pass def test_initialize_self_service_recovery_flow_for_browsers(self): """Test case for initialize_self_service_recovery_flow_for_browsers Initialize Recovery Flow for Browsers # noqa: E501 """ pass def test_initialize_self_service_recovery_flow_without_browser(self): """Test case for initialize_self_service_recovery_flow_without_browser Initialize Recovery Flow for APIs, Services, Apps, ... # noqa: E501 """ pass def test_initialize_self_service_registration_flow_for_browsers(self): """Test case for initialize_self_service_registration_flow_for_browsers Initialize Registration Flow for Browsers # noqa: E501 """ pass def test_initialize_self_service_registration_flow_without_browser(self): """Test case for initialize_self_service_registration_flow_without_browser Initialize Registration Flow for APIs, Services, Apps, ... # noqa: E501 """ pass def test_initialize_self_service_settings_flow_for_browsers(self): """Test case for initialize_self_service_settings_flow_for_browsers Initialize Settings Flow for Browsers # noqa: E501 """ pass def test_initialize_self_service_settings_flow_without_browser(self): """Test case for initialize_self_service_settings_flow_without_browser Initialize Settings Flow for APIs, Services, Apps, ... # noqa: E501 """ pass def test_initialize_self_service_verification_flow_for_browsers(self): """Test case for initialize_self_service_verification_flow_for_browsers Initialize Verification Flow for Browser Clients # noqa: E501 """ pass def test_initialize_self_service_verification_flow_without_browser(self): """Test case for initialize_self_service_verification_flow_without_browser Initialize Verification Flow for APIs, Services, Apps, ... # noqa: E501 """ pass def test_submit_self_service_login_flow(self): """Test case for submit_self_service_login_flow Submit a Login Flow # noqa: E501 """ pass def test_submit_self_service_logout_flow(self): """Test case for submit_self_service_logout_flow Complete Self-Service Logout # noqa: E501 """ pass def test_submit_self_service_logout_flow_without_browser(self): """Test case for submit_self_service_logout_flow_without_browser Perform Logout for APIs, Services, Apps, ... # noqa: E501 """ pass def test_submit_self_service_recovery_flow(self): """Test case for submit_self_service_recovery_flow Complete Recovery Flow # noqa: E501 """ pass def test_submit_self_service_registration_flow(self): """Test case for submit_self_service_registration_flow Submit a Registration Flow # noqa: E501 """ pass def test_submit_self_service_settings_flow(self): """Test case for submit_self_service_settings_flow Complete Settings Flow # noqa: E501 """ pass def test_submit_self_service_verification_flow(self): """Test case for submit_self_service_verification_flow Complete Verification Flow # noqa: E501 """ pass def test_to_session(self): """Test case for to_session Check Who the Current HTTP Session Belongs To # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

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1

0

1

0

3

7c29df3316dce7638b4588f6021b4bc59ffb4cfc

151

py

Python

base3_plus.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

2

2019-01-10T03:44:03.000Z

2019-05-24T08:50:14.000Z

base3_plus.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

base3_plus.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

""" @Time : 201/21/19 10:47 @Author : TaylorMei @Email : [email protected] @Project : iccv @File : base3_plus.py @Function: """

15.1

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null

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1

0

null

0

1

0

3

7c2c2ee21f857be97b79a37957d75b5c80b83234

421

py

Python

docker/setup.py

sreynit02/RunestoneServer

2d72fd1c26264a8d7d88e2bccfe9bfbb4d8b9a98

[ "MIT" ]

null

docker/setup.py

sreynit02/RunestoneServer

2d72fd1c26264a8d7d88e2bccfe9bfbb4d8b9a98

[ "MIT" ]

null

docker/setup.py

sreynit02/RunestoneServer

2d72fd1c26264a8d7d88e2bccfe9bfbb4d8b9a98

[ "MIT" ]

null

# ****************************************************************** # |docname| - Provide `docker_tools.py` as the script `docker-tools` # ****************************************************************** from setuptools import setup setup( name="runestone-docker-tools", version="0.1", install_requires=["click"], entry_points={ "console_scripts": ["docker-tools = docker_tools:cli"] }, )

30.071429

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true

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0.111111

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null

1

0

1

0

null

0

1

0

3

7c30b20fb26e70f99e3a1516c799910198cc11b1

17,421

py

Python

mango/__init__.py

kronael/mango-explorer

6292c089c2a3d1ff2cf0b50b815849451a50ec39

[ "MIT" ]

null

mango/__init__.py

kronael/mango-explorer

6292c089c2a3d1ff2cf0b50b815849451a50ec39

[ "MIT" ]

null

mango/__init__.py

kronael/mango-explorer

6292c089c2a3d1ff2cf0b50b815849451a50ec39

[ "MIT" ]

null

# In --strict mode, mypy complains about imports unless they're done this way. # # It complains 'Module has no attribute ABC' or 'Module "mango" does not explicitly export # attribute "XYZ"; implicit reexport disabled'. We could dial that back by using the # --implicit-reexport parameter, but let's keep things strict. # # Each import then *must* be of the form `from .file import X as X`. (Until/unless there's # a better way.) # from .account import Account as Account from .account import AccountSlot as AccountSlot from .accountflags import AccountFlags as AccountFlags from .accountinfo import AccountInfo as AccountInfo from .accountinfoconverter import build_account_info_converter as build_account_info_converter from .accountinstrumentvalues import AccountInstrumentValues as AccountInstrumentValues from .accountinstrumentvalues import PricedAccountInstrumentValues as PricedAccountInstrumentValues from .accountliquidator import AccountLiquidator as AccountLiquidator from .accountliquidator import NullAccountLiquidator as NullAccountLiquidator from .accountscout import AccountScout as AccountScout from .accountscout import ScoutReport as ScoutReport from .addressableaccount import AddressableAccount as AddressableAccount from .arguments import parse_args as parse_args from .arguments import output as output from .balancesheet import BalanceSheet as BalanceSheet from .cache import Cache as Cache from .cache import MarketCache as MarketCache from .cache import PerpMarketCache as PerpMarketCache from .cache import PriceCache as PriceCache from .cache import RootBankCache as RootBankCache from .client import BetterClient as BetterClient from .client import BlockhashNotFoundException as BlockhashNotFoundException from .client import ClientException as ClientException from .client import CompoundException as CompoundException from .client import CompoundRPCCaller as CompoundRPCCaller from .client import FailedToFetchBlockhashException as FailedToFetchBlockhashException from .client import NodeIsBehindException as NodeIsBehindException from .client import RateLimitException as RateLimitException from .client import RPCCaller as RPCCaller from .client import SlotHolder as SlotHolder from .client import TooManyRequestsRateLimitException as TooManyRequestsRateLimitException from .client import TooMuchBandwidthRateLimitException as TooMuchBandwidthRateLimitException from .client import TransactionException as TransactionException from .combinableinstructions import CombinableInstructions as CombinableInstructions from .constants import MangoConstants as MangoConstants from .constants import DATA_PATH as DATA_PATH from .constants import SOL_DECIMAL_DIVISOR as SOL_DECIMAL_DIVISOR from .constants import SOL_DECIMALS as SOL_DECIMALS from .constants import SOL_MINT_ADDRESS as SOL_MINT_ADDRESS from .constants import SYSTEM_PROGRAM_ADDRESS as SYSTEM_PROGRAM_ADDRESS from .constants import WARNING_DISCLAIMER_TEXT as WARNING_DISCLAIMER_TEXT from .constants import version as version from .context import Context as Context from .contextbuilder import ContextBuilder as ContextBuilder from .createmarketoperations import create_market_instruction_builder as create_market_instruction_builder from .createmarketoperations import create_market_operations as create_market_operations from .encoding import decode_binary as decode_binary from .encoding import encode_binary as encode_binary from .encoding import encode_key as encode_key from .encoding import encode_int as encode_int from .ensuremarketloaded import ensure_market_loaded as ensure_market_loaded from .ensuremarketloaded import load_market_by_symbol as load_market_by_symbol from .group import Group as Group from .group import GroupSlot as GroupSlot from .group import GroupSlotPerpMarket as GroupSlotPerpMarket from .group import GroupSlotSpotMarket as GroupSlotSpotMarket from .healthcheck import HealthCheck as HealthCheck from .idl import IdlParser as IdlParser from .idl import lazy_load_cached_idl_parser as lazy_load_cached_idl_parser from .idsjsonmarketlookup import IdsJsonMarketLookup as IdsJsonMarketLookup from .inventory import Inventory as Inventory from .inventory import PerpInventoryAccountWatcher as PerpInventoryAccountWatcher from .inventory import SpotInventoryAccountWatcher as SpotInventoryAccountWatcher from .instructions import build_cancel_perp_order_instructions as build_cancel_perp_order_instructions from .instructions import build_cancel_spot_order_instructions as build_cancel_spot_order_instructions from .instructions import build_close_spl_account_instructions as build_close_spl_account_instructions from .instructions import build_create_account_instructions as build_create_account_instructions from .instructions import build_create_associated_spl_account_instructions as build_create_associated_spl_account_instructions from .instructions import build_create_solana_account_instructions as build_create_solana_account_instructions from .instructions import build_create_spl_account_instructions as build_create_spl_account_instructions from .instructions import build_create_serum_open_orders_instructions as build_create_serum_open_orders_instructions from .instructions import build_deposit_instructions as build_deposit_instructions from .instructions import build_faucet_airdrop_instructions as build_faucet_airdrop_instructions from .instructions import build_mango_consume_events_instructions as build_mango_consume_events_instructions from .instructions import build_place_perp_order_instructions as build_place_perp_order_instructions from .instructions import build_redeem_accrued_mango_instructions as build_redeem_accrued_mango_instructions from .instructions import build_serum_consume_events_instructions as build_serum_consume_events_instructions from .instructions import build_serum_place_order_instructions as build_serum_place_order_instructions from .instructions import build_serum_settle_instructions as build_serum_settle_instructions from .instructions import build_spot_place_order_instructions as build_spot_place_order_instructions from .instructions import build_transfer_spl_tokens_instructions as build_transfer_spl_tokens_instructions from .instructions import build_withdraw_instructions as build_withdraw_instructions from .instructionreporter import InstructionReporter as InstructionReporter from .instructionreporter import SerumInstructionReporter as SerumInstructionReporter from .instructionreporter import MangoInstructionReporter as MangoInstructionReporter from .instructionreporter import CompoundInstructionReporter as CompoundInstructionReporter from .instructiontype import InstructionType as InstructionType from .instrumentlookup import InstrumentLookup as InstrumentLookup from .instrumentlookup import NullInstrumentLookup as NullInstrumentLookup from .instrumentlookup import CompoundInstrumentLookup as CompoundInstrumentLookup from .instrumentlookup import IdsJsonTokenLookup as IdsJsonTokenLookup from .instrumentlookup import NonSPLInstrumentLookup as NonSPLInstrumentLookup from .instrumentlookup import SPLTokenLookup as SPLTokenLookup from .instrumentvalue import InstrumentValue as InstrumentValue from .liquidatablereport import LiquidatableState as LiquidatableState from .liquidatablereport import LiquidatableReport as LiquidatableReport from .liquidationevent import LiquidationEvent as LiquidationEvent from .liquidationprocessor import LiquidationProcessor as LiquidationProcessor from .liquidationprocessor import LiquidationProcessorState as LiquidationProcessorState from .loadedmarket import LoadedMarket as LoadedMarket from .logmessages import expand_log_messages as expand_log_messages from .lotsizeconverter import LotSizeConverter as LotSizeConverter from .mangoinstruction import MangoInstruction as MangoInstruction from .lotsizeconverter import NullLotSizeConverter as NullLotSizeConverter from .market import DryRunMarket as DryRunMarket from .market import InventorySource as InventorySource from .market import Market as Market from .marketlookup import CompoundMarketLookup as CompoundMarketLookup from .marketlookup import MarketLookup as MarketLookup from .marketlookup import NullMarketLookup as NullMarketLookup from .marketoperations import MarketInstructionBuilder as MarketInstructionBuilder from .marketoperations import MarketOperations as MarketOperations from .marketoperations import NullMarketInstructionBuilder as NullMarketInstructionBuilder from .marketoperations import NullMarketOperations as NullMarketOperations from .metadata import Metadata as Metadata from .modelstate import ModelState as ModelState from .notification import CompoundNotificationTarget as CompoundNotificationTarget from .notification import ConsoleNotificationTarget as ConsoleNotificationTarget from .notification import CsvFileNotificationTarget as CsvFileNotificationTarget from .notification import DiscordNotificationTarget as DiscordNotificationTarget from .notification import FilteringNotificationTarget as FilteringNotificationTarget from .notification import MailjetNotificationTarget as MailjetNotificationTarget from .notification import NotificationHandler as NotificationHandler from .notification import NotificationTarget as NotificationTarget from .notification import TelegramNotificationTarget as TelegramNotificationTarget from .notification import parse_notification_target as parse_notification_target from .observables import CaptureFirstItem as CaptureFirstItem from .observables import CollectingObserverSubscriber as CollectingObserverSubscriber from .observables import DisposePropagator as DisposePropagator from .observables import DisposeWrapper as DisposeWrapper from .observables import EventSource as EventSource from .observables import FunctionObserver as FunctionObserver from .observables import LatestItemObserverSubscriber as LatestItemObserverSubscriber from .observables import NullObserverSubscriber as NullObserverSubscriber from .observables import PrintingObserverSubscriber as PrintingObserverSubscriber from .observables import TimestampedPrintingObserverSubscriber as TimestampedPrintingObserverSubscriber from .observables import create_backpressure_skipping_observer as create_backpressure_skipping_observer from .observables import debug_print_item as debug_print_item from .observables import log_subscription_error as log_subscription_error from .observables import observable_pipeline_error_reporter as observable_pipeline_error_reporter from .openorders import OpenOrders as OpenOrders from .oracle import Oracle as Oracle from .oracle import OracleProvider as OracleProvider from .oracle import OracleSource as OracleSource from .oracle import Price as Price from .oracle import SupportedOracleFeature as SupportedOracleFeature from .orderbookside import OrderBookSideType as OrderBookSideType from .orderbookside import PerpOrderBookSide as PerpOrderBookSide from .orders import Order as Order from .orders import OrderType as OrderType from .orders import OrderBook as OrderBook from .orders import Side as Side from .ownedinstrumentvalue import OwnedInstrumentValue as OwnedInstrumentValue from .oraclefactory import create_oracle_provider as create_oracle_provider from .parse_account_info_to_orders import parse_account_info_to_orders as parse_account_info_to_orders from .perpaccount import PerpAccount as PerpAccount from .perpeventqueue import PerpEvent as PerpEvent from .perpeventqueue import PerpEventQueue as PerpEventQueue from .perpeventqueue import PerpFillEvent as PerpFillEvent from .perpeventqueue import PerpOutEvent as PerpOutEvent from .perpeventqueue import PerpUnknownEvent as PerpUnknownEvent from .perpeventqueue import UnseenPerpEventChangesTracker as UnseenPerpEventChangesTracker from .perpmarket import PerpMarket as PerpMarket from .perpmarket import PerpMarketStub as PerpMarketStub from .perpmarketdetails import PerpMarketDetails as PerpMarketDetails from .perpmarketoperations import PerpMarketInstructionBuilder as PerpMarketInstructionBuilder from .perpmarketoperations import PerpMarketOperations as PerpMarketOperations from .perpopenorders import PerpOpenOrders as PerpOpenOrders from .placedorder import PlacedOrder as PlacedOrder from .placedorder import PlacedOrdersContainer as PlacedOrdersContainer from .publickey import encode_public_key_for_sorting as encode_public_key_for_sorting from .reconnectingwebsocket import ReconnectingWebsocket as ReconnectingWebsocket from .retrier import RetryWithPauses as RetryWithPauses from .retrier import retry_context as retry_context from .serumeventqueue import SerumEventQueue as SerumEventQueue from .serumeventqueue import UnseenSerumEventChangesTracker as UnseenSerumEventChangesTracker from .serummarket import SerumMarket as SerumMarket from .serummarket import SerumMarketStub as SerumMarketStub from .serummarketlookup import SerumMarketLookup as SerumMarketLookup from .serummarketoperations import SerumMarketInstructionBuilder as SerumMarketInstructionBuilder from .serummarketoperations import SerumMarketOperations as SerumMarketOperations from .spotmarket import SpotMarket as SpotMarket from .spotmarket import SpotMarketStub as SpotMarketStub from .spotmarketoperations import SpotMarketInstructionBuilder as SpotMarketInstructionBuilder from .spotmarketoperations import SpotMarketOperations as SpotMarketOperations from .text import indent_collection_as_str as indent_collection_as_str from .text import indent_item_by as indent_item_by from .token import Instrument as Instrument from .token import SolToken as SolToken from .token import Token as Token from .tokenaccount import TokenAccount as TokenAccount from .tokenbank import BankBalances as BankBalances from .tokenbank import InterestRates as InterestRates from .tokenbank import NodeBank as NodeBank from .tokenbank import RootBank as RootBank from .tokenbank import TokenBank as TokenBank from .tradeexecutor import ImmediateTradeExecutor as ImmediateTradeExecutor from .tradeexecutor import NullTradeExecutor as NullTradeExecutor from .tradeexecutor import TradeExecutor as TradeExecutor from .tradehistory import TradeHistory as TradeHistory from .transactionscout import TransactionScout as TransactionScout from .transactionscout import fetch_all_recent_transaction_signatures as fetch_all_recent_transaction_signatures from .transactionscout import mango_instruction_from_response as mango_instruction_from_response from .valuation import AccountValuation as AccountValuation from .valuation import TokenValuation as TokenValuation from .valuation import Valuation as Valuation from .version import Version as Version from .wallet import Wallet as Wallet from .walletbalancer import FilterSmallChanges as FilterSmallChanges from .walletbalancer import FixedTargetBalance as FixedTargetBalance from .walletbalancer import LiveAccountBalancer as LiveAccountBalancer from .walletbalancer import LiveWalletBalancer as LiveWalletBalancer from .walletbalancer import NullWalletBalancer as NullWalletBalancer from .walletbalancer import PercentageTargetBalance as PercentageTargetBalance from .walletbalancer import TargetBalance as TargetBalance from .walletbalancer import WalletBalancer as WalletBalancer from .walletbalancer import calculate_required_balance_changes as calculate_required_balance_changes from .walletbalancer import parse_fixed_target_balance as parse_fixed_target_balance from .walletbalancer import parse_target_balance as parse_target_balance from .walletbalancer import sort_changes_for_trades as sort_changes_for_trades from .watcher import LamdaUpdateWatcher as LamdaUpdateWatcher from .watcher import ManualUpdateWatcher as ManualUpdateWatcher from .watcher import Watcher as Watcher from .watchers import build_group_watcher as build_group_watcher from .watchers import build_account_watcher as build_account_watcher from .watchers import build_cache_watcher as build_cache_watcher from .watchers import build_spot_open_orders_watcher as build_spot_open_orders_watcher from .watchers import build_serum_open_orders_watcher as build_serum_open_orders_watcher from .watchers import build_perp_open_orders_watcher as build_perp_open_orders_watcher from .watchers import build_price_watcher as build_price_watcher from .watchers import build_serum_inventory_watcher as build_serum_inventory_watcher from .watchers import build_orderbook_watcher as build_orderbook_watcher from .websocketsubscription import IndividualWebSocketSubscriptionManager as IndividualWebSocketSubscriptionManager from .websocketsubscription import SharedWebSocketSubscriptionManager as SharedWebSocketSubscriptionManager from .websocketsubscription import WebSocketAccountSubscription as WebSocketAccountSubscription from .websocketsubscription import WebSocketLogSubscription as WebSocketLogSubscription from .websocketsubscription import WebSocketProgramSubscription as WebSocketProgramSubscription from .websocketsubscription import WebSocketSubscription as WebSocketSubscription from .websocketsubscription import WebSocketSubscriptionManager as WebSocketSubscriptionManager from .layouts import layouts import decimal # Increased precision from 18 to 36 because for a decimal like: # val = Decimal("17436036573.2030800") # # The following rounding operations would both throw decimal.InvalidOperation: # val.quantize(Decimal('.000000001')) # round(val, 9) decimal.getcontext().prec = 36

66.747126

126

0.89306

1,901

17,421

7.996844

0.192004

0.020984

0.027496

0.033746

0.160242

0.064465

0.045191

0.010854

0

0.002142

0.08903

17,421

260

127

67.003846

0.955766

0.03668

0

1

0

true

0

0.995868

0

0.995868

0.004132

0

null

0

null

0

1

0

1

0

1

0

3

7c31ff3f832fdd4d6ba0dc485287be931476d8a3

1,017

py

Python

BB/bbObjects/items/bbTurret.py

mwaitzman/GOF2BountyBot

b66026228b752b07ac4734ca74b60730dbd74995

[ "MIT" ]

null

BB/bbObjects/items/bbTurret.py

mwaitzman/GOF2BountyBot

b66026228b752b07ac4734ca74b60730dbd74995

[ "MIT" ]

null

BB/bbObjects/items/bbTurret.py

mwaitzman/GOF2BountyBot

b66026228b752b07ac4734ca74b60730dbd74995

[ "MIT" ]

null

from .bbItem import bbItem from ...bbConfig import bbData class bbTurret(bbItem): dps = 0.0 def __init__(self, name, aliases, dps=0.0, value=0, wiki="", manufacturer="", icon="", emoji=""): super(bbTurret, self).__init__(name, aliases, value=value, wiki=wiki, manufacturer=manufacturer, icon=icon, emoji=emoji) self.dps = dps def statsStringShort(self): return "*Dps: " + str(self.dps) + "*" def getType(self): return bbTurret def fromDict(turretDict): if turretDict["builtIn"]: return bbData.builtInTurretObjs[turretDict["name"]] else: return bbTurret(turretDict["name"], turretDict["aliases"], dps=turretDict["dps"], value=turretDict["value"], wiki=turretDict["wiki"] if "wiki" in turretDict else "", manufacturer=turretDict["manufacturer"] if "manufacturer" in turretDict else "", icon=turretDict["icon"] if "icon" in turretDict else bbData.rocketIcon, emoji=turretDict["emoji"] if "emoji" in turretDict else "")

36.321429

145

0.66765

118

1,017

5.686441

0.271186

0.071535

0.09538

0

0.006031

0.184857

1,017

27

146

37.666667

0.803378

0

0.085546

0

1

0.222222

false

0

0.111111

0.666667

0

null

0

null

0

1

0

1

0

3

7c42dc9f24c848eb5660235f34da5faf02dd1e33

2,192

py

Python

signin/tests.py

pptnz/swa_team2

253ae83d73c00245d359574d6a16f4eba9830950

[ "MIT" ]

null

signin/tests.py

pptnz/swa_team2

253ae83d73c00245d359574d6a16f4eba9830950

[ "MIT" ]

3

2018-06-07T17:18:16.000Z

2021-06-10T20:19:27.000Z

signin/tests.py

pptnz/swa_team2

253ae83d73c00245d359574d6a16f4eba9830950

[ "MIT" ]

1

2018-06-25T23:52:57.000Z

import json from django.test import TestCase from django.contrib.auth.models import User from .models import CustomUser from django.apps import apps from .apps import SigninConfig class SignInTest(TestCase): def setUp(self): self.django_user = User.objects.create_user(username='testusername', password='testpassword') self.custom_user = CustomUser.objects.create(django_user=self.django_user) def test_apps(self): self.assertEqual(SigninConfig.name, 'signin') self.assertEqual(apps.get_app_config('signin').name, 'signin') def test_sign_in_redirect_page(self): response = self.client.get('/') self.assertRedirects(response, '/sign_in/') def test_get(self): response = self.client.get('/sign_in/') self.assertEqual(response.status_code, 200) def test_wrong_username(self): response = self.client.post('/sign_in/', {'username': 'wrongusername', 'password': 'testpassword'}) self.assertEqual(response.status_code, 200) def test_wrong_password(self): response = self.client.post('/sign_in/', {'username': 'testusername', 'password': 'wrongpassword'}) self.assertEqual(response.status_code, 200) def test_login(self): response = self.client.post('/sign_in/', {'username': 'testusername', 'password': 'testpassword'}) self.assertRedirects(response, '/habitmaker/') # todo: change this link def test_login_other_page(self): response = self.client.post('/sign_in/?next=/habitmaker/', {'username': 'testusername', 'password': 'testpassword'}) self.assertRedirects(response, '/habitmaker/') def test_form_not_valid(self): response = self.client.post('/sign_in/', {'username': 'testusername'}) self.assertEqual(response.status_code, 200) def test_email_verification(self): self.custom_user.authenticate_email() self.assertTrue(self.custom_user.is_email_authenticated) def test_already_signed_in(self): self.client.login(username='testusername', password='testpassword') response = self.client.get('/sign_in/') self.assertRedirects(response, '/habitmaker/')

39.142857

124

0.696624

252

2,192

5.876984

0.253968

0.047265

0.097232

0.103984

0.466577

0.411884

0.351114

0.180959

0.081026

0

0.006583

0.168339

2,192

55

125

39.854545

0.805815

0.010037

0

0.219512

0

0.17297

0.012454

0

0.018182

0.268293

1

0.268293

false

0.170732

0.146341

0

0.439024

0

null

0

null

0

1

0

1

0

1

0

3

7c44dd7ee7dcb34a8c6b443486c1190c2f8b538a

707

py

Python

tree/list/BinaryNode.py

EliHar/BinaryTree-ADT

bf220eb8ccb04f6fee7d7a67ef7e9cd00cc6a4c1

[ "MIT" ]

null

tree/list/BinaryNode.py

EliHar/BinaryTree-ADT

bf220eb8ccb04f6fee7d7a67ef7e9cd00cc6a4c1

[ "MIT" ]

null

tree/list/BinaryNode.py

EliHar/BinaryTree-ADT

bf220eb8ccb04f6fee7d7a67ef7e9cd00cc6a4c1

[ "MIT" ]

null

__author__ = 'Elias Haroun' class BinaryNode(object): def __init__(self, data, left, right): self.data = data self.left = left self.right = right def getData(self): return self.data def getLeft(self): return self.left def getRight(self): return self.right def setData(self, data): self.data = data def setLeft(self, aNode): self.left = aNode def setRight(self, aNode): self.right = aNode def hasLeft(self): return self.getLeft() is not None def hasRight(self): return self.getRight() is not None def isLeaf(self): return not(self.hasLeft() | self.hasRight())

19.108108

52

0.589816

87

707

4.701149

0.298851

0.146699

0.171149

0.05868

0

0.308345

707

36

53

19.638889

0.836401

0

0.083333

0

0.016997

0

1

0.416667

false

0

0.25

0.708333

0

null

0

null

0

1

0

1

0

3

7c5bc0acf118170063960ef1b43392c65c34384f

1,421

py

Python

TM1py/Objects/ElementAttribute.py

damirishpreet/TM1py

8482d0787fd5a9e5eb05a0288c41b75fc1fc93ac

[ "MIT" ]

19

2016-03-04T19:21:40.000Z

2021-12-10T02:39:51.000Z

TM1py/Objects/ElementAttribute.py

damirishpreet/TM1py

8482d0787fd5a9e5eb05a0288c41b75fc1fc93ac

[ "MIT" ]

11

2016-08-24T19:27:11.000Z

2017-07-30T01:10:28.000Z

TM1py/Objects/ElementAttribute.py

damirishpreet/TM1py

8482d0787fd5a9e5eb05a0288c41b75fc1fc93ac

[ "MIT" ]

6

2016-08-03T19:28:45.000Z

2017-01-30T12:25:05.000Z

# -*- coding: utf-8 -*- import json from TM1py.Objects.TM1Object import TM1Object class ElementAttribute(TM1Object): """ Abstraction of TM1 Element Attributes """ valid_types = ['NUMERIC', 'STRING', 'ALIAS'] def __init__(self, name, attribute_type): self.name = name self.attribute_type = attribute_type @property def name(self): return self._name @name.setter def name(self, value): self._name = value @property def attribute_type(self): return self._attribute_type @attribute_type.setter def attribute_type(self, value): if value.upper() in ElementAttribute.valid_types: self._attribute_type = value else: raise Exception('{} not a valid Attribute Type.'.format(value)) @property def body_as_dict(self): return {"Name": self._name, "Type": self._attribute_type} @property def body(self): return json.dumps(self.body_as_dict, ensure_ascii=False) @classmethod def from_json(cls, element_attribute_as_json): return cls.from_dict(json.loads(element_attribute_as_json)) @classmethod def from_dict(cls, element_attribute_as_dict): return cls(name=element_attribute_as_dict['Name'], attribute_type=element_attribute_as_dict['Type']) def __eq__(self, other): return self.name == other

25.375

75

0.655172

171

1,421

5.157895

0.304094

0.162132

0.102041

0.07483

0.068027

0

0.005587

0.244194

1,421

55

76

25.836364

0.815642

0.049261

0

0.162162

0

0.047619

0

1

0.27027

false

0

0.054054

0.189189

0.567568

0

null

0

null

0

1

0

1

0

3

7c7069a54d49756f83e36923521eba70ab74f6c7

139

py

Python

demo/demo/accounts/urls.py

caravancoop/rest-auth-toolkit

425bf293987f7128d9538f27a5eca7e47ba84217

[ "MIT" ]

1

2019-12-23T21:51:06.000Z

demo/demo/accounts/urls.py

caravancoop/rest-framework-auth-toolkit

425bf293987f7128d9538f27a5eca7e47ba84217

[ "MIT" ]

127

2017-10-27T15:20:01.000Z

2022-03-07T04:09:15.000Z

demo/demo/accounts/urls.py

caravancoop/rest-auth-toolkit

425bf293987f7128d9538f27a5eca7e47ba84217

[ "MIT" ]

2

2018-01-03T16:22:51.000Z

2019-12-23T21:51:54.000Z

from django.urls import path from .views import ProfileView urlpatterns = [ path('', ProfileView.as_view(), name='user-profile'), ]

15.444444

57

0.705036

17

139

5.705882

0.764706

0

0.158273

139

8

58

17.375

0.82906

0

0.086331

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

7c872854a67dcbee173ef18681a5116e43865d52

53,677

py

Python

automl/google/cloud/automl_v1beta1/gapic/auto_ml_client.py

erikwebb/google-cloud-python

288a878e9a07239015c78a193eca1cc15e926127

[ "Apache-2.0" ]

1

2019-04-16T08:13:06.000Z

automl/google/cloud/automl_v1beta1/gapic/auto_ml_client.py

erikwebb/google-cloud-python

288a878e9a07239015c78a193eca1cc15e926127

[ "Apache-2.0" ]

null

automl/google/cloud/automl_v1beta1/gapic/auto_ml_client.py

erikwebb/google-cloud-python

288a878e9a07239015c78a193eca1cc15e926127

[ "Apache-2.0" ]

1

2020-11-15T11:44:36.000Z

# -*- coding: utf-8 -*- # # Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Accesses the google.cloud.automl.v1beta1 AutoMl API.""" import functools import pkg_resources import warnings from google.oauth2 import service_account import google.api_core.gapic_v1.client_info import google.api_core.gapic_v1.config import google.api_core.gapic_v1.method import google.api_core.grpc_helpers import google.api_core.operation import google.api_core.operations_v1 import google.api_core.page_iterator import google.api_core.path_template import grpc from google.cloud.automl_v1beta1.gapic import auto_ml_client_config from google.cloud.automl_v1beta1.gapic import enums from google.cloud.automl_v1beta1.gapic.transports import auto_ml_grpc_transport from google.cloud.automl_v1beta1.proto import data_items_pb2 from google.cloud.automl_v1beta1.proto import dataset_pb2 from google.cloud.automl_v1beta1.proto import io_pb2 from google.cloud.automl_v1beta1.proto import model_evaluation_pb2 from google.cloud.automl_v1beta1.proto import model_pb2 from google.cloud.automl_v1beta1.proto import operations_pb2 as proto_operations_pb2 from google.cloud.automl_v1beta1.proto import prediction_service_pb2 from google.cloud.automl_v1beta1.proto import prediction_service_pb2_grpc from google.cloud.automl_v1beta1.proto import service_pb2 from google.cloud.automl_v1beta1.proto import service_pb2_grpc from google.longrunning import operations_pb2 as longrunning_operations_pb2 from google.protobuf import empty_pb2 _GAPIC_LIBRARY_VERSION = pkg_resources.get_distribution("google-cloud-automl").version class AutoMlClient(object): """ AutoML Server API. The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted. An ID of a resource is the last element of the item's resource name. For ``projects/{project_id}/locations/{location_id}/datasets/{dataset_id}``, then the id for the item is ``{dataset_id}``. """ SERVICE_ADDRESS = "automl.googleapis.com:443" """The default address of the service.""" # The name of the interface for this client. This is the key used to # find the method configuration in the client_config dictionary. _INTERFACE_NAME = "google.cloud.automl.v1beta1.AutoMl" @classmethod def from_service_account_file(cls, filename, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: AutoMlClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs) from_service_account_json = from_service_account_file @classmethod def location_path(cls, project, location): """Return a fully-qualified location string.""" return google.api_core.path_template.expand( "projects/{project}/locations/{location}", project=project, location=location, ) @classmethod def dataset_path(cls, project, location, dataset): """Return a fully-qualified dataset string.""" return google.api_core.path_template.expand( "projects/{project}/locations/{location}/datasets/{dataset}", project=project, location=location, dataset=dataset, ) @classmethod def model_path(cls, project, location, model): """Return a fully-qualified model string.""" return google.api_core.path_template.expand( "projects/{project}/locations/{location}/models/{model}", project=project, location=location, model=model, ) @classmethod def model_evaluation_path(cls, project, location, model, model_evaluation): """Return a fully-qualified model_evaluation string.""" return google.api_core.path_template.expand( "projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}", project=project, location=location, model=model, model_evaluation=model_evaluation, ) def __init__( self, transport=None, channel=None, credentials=None, client_config=None, client_info=None, ): """Constructor. Args: transport (Union[~.AutoMlGrpcTransport, Callable[[~.Credentials, type], ~.AutoMlGrpcTransport]): A transport instance, responsible for actually making the API calls. The default transport uses the gRPC protocol. This argument may also be a callable which returns a transport instance. Callables will be sent the credentials as the first argument and the default transport class as the second argument. channel (grpc.Channel): DEPRECATED. A ``Channel`` instance through which to make calls. This argument is mutually exclusive with ``credentials``; providing both will raise an exception. credentials (google.auth.credentials.Credentials): The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. This argument is mutually exclusive with providing a transport instance to ``transport``; doing so will raise an exception. client_config (dict): DEPRECATED. A dictionary of call options for each method. If not specified, the default configuration is used. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. """ # Raise deprecation warnings for things we want to go away. if client_config is not None: warnings.warn( "The `client_config` argument is deprecated.", PendingDeprecationWarning, stacklevel=2, ) else: client_config = auto_ml_client_config.config if channel: warnings.warn( "The `channel` argument is deprecated; use " "`transport` instead.", PendingDeprecationWarning, stacklevel=2, ) # Instantiate the transport. # The transport is responsible for handling serialization and # deserialization and actually sending data to the service. if transport: if callable(transport): self.transport = transport( credentials=credentials, default_class=auto_ml_grpc_transport.AutoMlGrpcTransport, ) else: if credentials: raise ValueError( "Received both a transport instance and " "credentials; these are mutually exclusive." ) self.transport = transport else: self.transport = auto_ml_grpc_transport.AutoMlGrpcTransport( address=self.SERVICE_ADDRESS, channel=channel, credentials=credentials ) if client_info is None: client_info = google.api_core.gapic_v1.client_info.ClientInfo( gapic_version=_GAPIC_LIBRARY_VERSION ) else: client_info.gapic_version = _GAPIC_LIBRARY_VERSION self._client_info = client_info # Parse out the default settings for retry and timeout for each RPC # from the client configuration. # (Ordinarily, these are the defaults specified in the `*_config.py` # file next to this one.) self._method_configs = google.api_core.gapic_v1.config.parse_method_configs( client_config["interfaces"][self._INTERFACE_NAME] ) # Save a dictionary of cached API call functions. # These are the actual callables which invoke the proper # transport methods, wrapped with `wrap_method` to add retry, # timeout, and the like. self._inner_api_calls = {} # Service calls def create_dataset( self, parent, dataset, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Creates a dataset. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> parent = client.location_path('[PROJECT]', '[LOCATION]') >>> >>> # TODO: Initialize `dataset`: >>> dataset = {} >>> >>> response = client.create_dataset(parent, dataset) Args: parent (str): The resource name of the project to create the dataset for. dataset (Union[dict, ~google.cloud.automl_v1beta1.types.Dataset]): The dataset to create. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.automl_v1beta1.types.Dataset` retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.Dataset` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "create_dataset" not in self._inner_api_calls: self._inner_api_calls[ "create_dataset" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.create_dataset, default_retry=self._method_configs["CreateDataset"].retry, default_timeout=self._method_configs["CreateDataset"].timeout, client_info=self._client_info, ) request = service_pb2.CreateDatasetRequest(parent=parent, dataset=dataset) return self._inner_api_calls["create_dataset"]( request, retry=retry, timeout=timeout, metadata=metadata ) def get_dataset( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Gets a dataset. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]') >>> >>> response = client.get_dataset(name) Args: name (str): The resource name of the dataset to retrieve. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.Dataset` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "get_dataset" not in self._inner_api_calls: self._inner_api_calls[ "get_dataset" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.get_dataset, default_retry=self._method_configs["GetDataset"].retry, default_timeout=self._method_configs["GetDataset"].timeout, client_info=self._client_info, ) request = service_pb2.GetDatasetRequest(name=name) return self._inner_api_calls["get_dataset"]( request, retry=retry, timeout=timeout, metadata=metadata ) def list_datasets( self, parent, filter_=None, page_size=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Lists datasets in a project. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> parent = client.location_path('[PROJECT]', '[LOCATION]') >>> >>> # Iterate over all results >>> for element in client.list_datasets(parent): ... # process element ... pass >>> >>> >>> # Alternatively: >>> >>> # Iterate over results one page at a time >>> for page in client.list_datasets(parent).pages: ... for element in page: ... # process element ... pass Args: parent (str): The resource name of the project from which to list datasets. filter_ (str): An expression for filtering the results of the request. - ``dataset_metadata`` - for existence of the case. An example of using the filter is: - ``translation_dataset_metadata:*`` --> The dataset has translation\_dataset\_metadata. page_size (int): The maximum number of resources contained in the underlying API response. If page streaming is performed per- resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.gax.PageIterator` instance. By default, this is an iterable of :class:`~google.cloud.automl_v1beta1.types.Dataset` instances. This object can also be configured to iterate over the pages of the response through the `options` parameter. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "list_datasets" not in self._inner_api_calls: self._inner_api_calls[ "list_datasets" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.list_datasets, default_retry=self._method_configs["ListDatasets"].retry, default_timeout=self._method_configs["ListDatasets"].timeout, client_info=self._client_info, ) request = service_pb2.ListDatasetsRequest( parent=parent, filter=filter_, page_size=page_size ) iterator = google.api_core.page_iterator.GRPCIterator( client=None, method=functools.partial( self._inner_api_calls["list_datasets"], retry=retry, timeout=timeout, metadata=metadata, ), request=request, items_field="datasets", request_token_field="page_token", response_token_field="next_page_token", ) return iterator def delete_dataset( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Deletes a dataset and all of its contents. Returns empty response in the ``response`` field when it completes, and ``delete_details`` in the ``metadata`` field. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]') >>> >>> response = client.delete_dataset(name) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: name (str): The resource name of the dataset to delete. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "delete_dataset" not in self._inner_api_calls: self._inner_api_calls[ "delete_dataset" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.delete_dataset, default_retry=self._method_configs["DeleteDataset"].retry, default_timeout=self._method_configs["DeleteDataset"].timeout, client_info=self._client_info, ) request = service_pb2.DeleteDatasetRequest(name=name) operation = self._inner_api_calls["delete_dataset"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.OperationMetadata, ) def import_data( self, name, input_config, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Imports data into a dataset. Returns an empty response in the ``response`` field when it completes. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]') >>> >>> # TODO: Initialize `input_config`: >>> input_config = {} >>> >>> response = client.import_data(name, input_config) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: name (str): Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. input_config (Union[dict, ~google.cloud.automl_v1beta1.types.InputConfig]): Required. The desired input location. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.automl_v1beta1.types.InputConfig` retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "import_data" not in self._inner_api_calls: self._inner_api_calls[ "import_data" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.import_data, default_retry=self._method_configs["ImportData"].retry, default_timeout=self._method_configs["ImportData"].timeout, client_info=self._client_info, ) request = service_pb2.ImportDataRequest(name=name, input_config=input_config) operation = self._inner_api_calls["import_data"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.OperationMetadata, ) def export_data( self, name, output_config, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Exports dataset's data to a Google Cloud Storage bucket. Returns an empty response in the ``response`` field when it completes. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]') >>> >>> # TODO: Initialize `output_config`: >>> output_config = {} >>> >>> response = client.export_data(name, output_config) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: name (str): Required. The resource name of the dataset. output_config (Union[dict, ~google.cloud.automl_v1beta1.types.OutputConfig]): Required. The desired output location. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.automl_v1beta1.types.OutputConfig` retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "export_data" not in self._inner_api_calls: self._inner_api_calls[ "export_data" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.export_data, default_retry=self._method_configs["ExportData"].retry, default_timeout=self._method_configs["ExportData"].timeout, client_info=self._client_info, ) request = service_pb2.ExportDataRequest(name=name, output_config=output_config) operation = self._inner_api_calls["export_data"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.OperationMetadata, ) def create_model( self, parent, model, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Creates a model. Returns a Model in the ``response`` field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> parent = client.location_path('[PROJECT]', '[LOCATION]') >>> >>> # TODO: Initialize `model`: >>> model = {} >>> >>> response = client.create_model(parent, model) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: parent (str): Resource name of the parent project where the model is being created. model (Union[dict, ~google.cloud.automl_v1beta1.types.Model]): The model to create. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.automl_v1beta1.types.Model` retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "create_model" not in self._inner_api_calls: self._inner_api_calls[ "create_model" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.create_model, default_retry=self._method_configs["CreateModel"].retry, default_timeout=self._method_configs["CreateModel"].timeout, client_info=self._client_info, ) request = service_pb2.CreateModelRequest(parent=parent, model=model) operation = self._inner_api_calls["create_model"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, model_pb2.Model, metadata_type=proto_operations_pb2.OperationMetadata, ) def get_model( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Gets a model. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]') >>> >>> response = client.get_model(name) Args: name (str): Resource name of the model. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.Model` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "get_model" not in self._inner_api_calls: self._inner_api_calls[ "get_model" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.get_model, default_retry=self._method_configs["GetModel"].retry, default_timeout=self._method_configs["GetModel"].timeout, client_info=self._client_info, ) request = service_pb2.GetModelRequest(name=name) return self._inner_api_calls["get_model"]( request, retry=retry, timeout=timeout, metadata=metadata ) def list_models( self, parent, filter_=None, page_size=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Lists models. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> parent = client.location_path('[PROJECT]', '[LOCATION]') >>> >>> # Iterate over all results >>> for element in client.list_models(parent): ... # process element ... pass >>> >>> >>> # Alternatively: >>> >>> # Iterate over results one page at a time >>> for page in client.list_models(parent).pages: ... for element in page: ... # process element ... pass Args: parent (str): Resource name of the project, from which to list the models. filter_ (str): An expression for filtering the results of the request. - ``model_metadata`` - for existence of the case. - ``dataset_id`` - for = or !=. Some examples of using the filter are: - ``image_classification_model_metadata:*`` --> The model has image\_classification\_model\_metadata. - ``dataset_id=5`` --> The model was created from a sibling dataset with ID 5. page_size (int): The maximum number of resources contained in the underlying API response. If page streaming is performed per- resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.gax.PageIterator` instance. By default, this is an iterable of :class:`~google.cloud.automl_v1beta1.types.Model` instances. This object can also be configured to iterate over the pages of the response through the `options` parameter. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "list_models" not in self._inner_api_calls: self._inner_api_calls[ "list_models" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.list_models, default_retry=self._method_configs["ListModels"].retry, default_timeout=self._method_configs["ListModels"].timeout, client_info=self._client_info, ) request = service_pb2.ListModelsRequest( parent=parent, filter=filter_, page_size=page_size ) iterator = google.api_core.page_iterator.GRPCIterator( client=None, method=functools.partial( self._inner_api_calls["list_models"], retry=retry, timeout=timeout, metadata=metadata, ), request=request, items_field="model", request_token_field="page_token", response_token_field="next_page_token", ) return iterator def delete_model( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Deletes a model. If a model is already deployed, this only deletes the model in AutoML BE, and does not change the status of the deployed model in the production environment. Returns ``google.protobuf.Empty`` in the ``response`` field when it completes, and ``delete_details`` in the ``metadata`` field. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]') >>> >>> response = client.delete_model(name) >>> >>> def callback(operation_future): ... # Handle result. ... result = operation_future.result() >>> >>> response.add_done_callback(callback) >>> >>> # Handle metadata. >>> metadata = response.metadata() Args: name (str): Resource name of the model being deleted. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "delete_model" not in self._inner_api_calls: self._inner_api_calls[ "delete_model" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.delete_model, default_retry=self._method_configs["DeleteModel"].retry, default_timeout=self._method_configs["DeleteModel"].timeout, client_info=self._client_info, ) request = service_pb2.DeleteModelRequest(name=name) operation = self._inner_api_calls["delete_model"]( request, retry=retry, timeout=timeout, metadata=metadata ) return google.api_core.operation.from_gapic( operation, self.transport._operations_client, empty_pb2.Empty, metadata_type=proto_operations_pb2.OperationMetadata, ) def deploy_model( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Deploys model. Returns a ``DeployModelResponse`` in the ``response`` field when it completes. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]') >>> >>> response = client.deploy_model(name) Args: name (str): Resource name of the model to deploy. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.Operation` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "deploy_model" not in self._inner_api_calls: self._inner_api_calls[ "deploy_model" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.deploy_model, default_retry=self._method_configs["DeployModel"].retry, default_timeout=self._method_configs["DeployModel"].timeout, client_info=self._client_info, ) request = service_pb2.DeployModelRequest(name=name) return self._inner_api_calls["deploy_model"]( request, retry=retry, timeout=timeout, metadata=metadata ) def undeploy_model( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Undeploys model. Returns an ``UndeployModelResponse`` in the ``response`` field when it completes. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]') >>> >>> response = client.undeploy_model(name) Args: name (str): Resource name of the model to undeploy. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.Operation` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "undeploy_model" not in self._inner_api_calls: self._inner_api_calls[ "undeploy_model" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.undeploy_model, default_retry=self._method_configs["UndeployModel"].retry, default_timeout=self._method_configs["UndeployModel"].timeout, client_info=self._client_info, ) request = service_pb2.UndeployModelRequest(name=name) return self._inner_api_calls["undeploy_model"]( request, retry=retry, timeout=timeout, metadata=metadata ) def get_model_evaluation( self, name, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Gets a model evaluation. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> name = client.model_evaluation_path('[PROJECT]', '[LOCATION]', '[MODEL]', '[MODEL_EVALUATION]') >>> >>> response = client.get_model_evaluation(name) Args: name (str): Resource name for the model evaluation. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.automl_v1beta1.types.ModelEvaluation` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "get_model_evaluation" not in self._inner_api_calls: self._inner_api_calls[ "get_model_evaluation" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.get_model_evaluation, default_retry=self._method_configs["GetModelEvaluation"].retry, default_timeout=self._method_configs["GetModelEvaluation"].timeout, client_info=self._client_info, ) request = service_pb2.GetModelEvaluationRequest(name=name) return self._inner_api_calls["get_model_evaluation"]( request, retry=retry, timeout=timeout, metadata=metadata ) def list_model_evaluations( self, parent, filter_=None, page_size=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Lists model evaluations. Example: >>> from google.cloud import automl_v1beta1 >>> >>> client = automl_v1beta1.AutoMlClient() >>> >>> parent = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]') >>> >>> # Iterate over all results >>> for element in client.list_model_evaluations(parent): ... # process element ... pass >>> >>> >>> # Alternatively: >>> >>> # Iterate over results one page at a time >>> for page in client.list_model_evaluations(parent).pages: ... for element in page: ... # process element ... pass Args: parent (str): Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location. filter_ (str): An expression for filtering the results of the request. - ``annotation_spec_id`` - for =, != or existence. See example below for the last. Some examples of using the filter are: - ``annotation_spec_id!=4`` --> The model evaluation was done for annotation spec with ID different than 4. - ``NOT annotation_spec_id:*`` --> The model evaluation was done for aggregate of all annotation specs. page_size (int): The maximum number of resources contained in the underlying API response. If page streaming is performed per- resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.gax.PageIterator` instance. By default, this is an iterable of :class:`~google.cloud.automl_v1beta1.types.ModelEvaluation` instances. This object can also be configured to iterate over the pages of the response through the `options` parameter. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "list_model_evaluations" not in self._inner_api_calls: self._inner_api_calls[ "list_model_evaluations" ] = google.api_core.gapic_v1.method.wrap_method( self.transport.list_model_evaluations, default_retry=self._method_configs["ListModelEvaluations"].retry, default_timeout=self._method_configs["ListModelEvaluations"].timeout, client_info=self._client_info, ) request = service_pb2.ListModelEvaluationsRequest( parent=parent, filter=filter_, page_size=page_size ) iterator = google.api_core.page_iterator.GRPCIterator( client=None, method=functools.partial( self._inner_api_calls["list_model_evaluations"], retry=retry, timeout=timeout, metadata=metadata, ), request=request, items_field="model_evaluation", request_token_field="page_token", response_token_field="next_page_token", ) return iterator

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5.388543

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0

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0

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0

null

0

1

0

1

0

null

0

3

7cde0e155e222f52e34bae521e25a21b28caf52a

550

py

Python

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

Code/extract_method3.py

AbdullahNoori/CS-2.1-Trees-Sorting

59ba182d60abe6171a3d7d64981f79ee192de3bb

[ "MIT" ]

null

# Written by Kamran Bigdely # Example for Compose Methods: Extract Method. import math def get_distance(xc1=5, xc2=7.25, yc1=22, yc2=-4.84): # Calculate the distance between the two circle return math.sqrt((xc1-xc2)**2 + (yc1 - yc2)**2) print('distance', get_distance()) # *** somewhere else in your program *** def get_length(xa=-50, ya=99, xb=.67, yb=.26): # calcualte the length of vector AB vector which is a vector between A and B points. return math.sqrt((xa-xb)*(xa-xb) + (ya-yb)*(ya-yb)) print('length', get_length())

26.190476

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3.967391

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0.059341

0.172727

550

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27.5

0.742857

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0.285714

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null

0

null

0

1

0

1

0

3

7ce3f2fa9ab64a2e056aae0886c6829b2b5285e6

7,722

py

Python

src/quart/local.py

Dunkledore/quart

803c8678b083895f4ece35fccb6aca56e189ee0a

[ "MIT" ]

3

2020-03-31T10:36:31.000Z

2020-04-23T12:01:10.000Z

venv/lib/python3.9/site-packages/quart/local.py

ryanwwest/kademlia

e1e5b84db0a7710cf372663325041850802d55f1

[ "MIT" ]

6

2020-09-05T01:40:23.000Z

2022-03-12T00:40:58.000Z

src/quart/local.py

ccns1/ccns11

d6edfac34fbee06fe974cda007d24a088d31ad30

[ "MIT" ]

1

2020-09-05T00:19:03.000Z

from __future__ import annotations import asyncio import copy from contextvars import ContextVar # noqa # contextvars not understood as stdlib from typing import Any # noqa # contextvars not understood as stdlib from typing import Callable, Dict, Optional class TaskLocal: """An object local to the current task.""" __slots__ = ("_storage",) def __init__(self) -> None: # Note as __setattr__ is overidden below, use the object __setattr__ object.__setattr__(self, "_storage", ContextVar("storage")) def __getattr__(self, name: str) -> Any: values = self._storage.get({}) try: return values[name] except KeyError: raise AttributeError(name) def __setattr__(self, name: str, value: Any) -> None: values = self._storage.get({}) values[name] = value self._storage.set(values) def __delattr__(self, name: str) -> None: values = self._storage.get({}) try: del values[name] self._storage.set(values) except KeyError: raise AttributeError(name) @staticmethod def _task_identity() -> int: loop = asyncio.get_event_loop() if loop.is_running(): task = asyncio.current_task() task_id = id(task) return task_id else: return 0 class LocalStack: def __init__(self) -> None: self._task_local = TaskLocal() def push(self, value: Any) -> None: stack = getattr(self._task_local, "stack", None) if stack is None: self._task_local.stack = stack = [] stack.append(value) def pop(self) -> Any: stack = getattr(self._task_local, "stack", None) if stack is None or stack == []: return None else: return stack.pop() @property def top(self) -> Any: try: return self._task_local.stack[-1] except (AttributeError, IndexError): return None class LocalProxy: """Proxy to a task local object.""" __slots__ = ("__dict__", "__local", "__wrapped__") def __init__(self, local: Callable, name: Optional[str] = None) -> None: # Note as __setattr__ is overidden below, use the object __setattr__ object.__setattr__(self, "__LocalProxy_local", local) object.__setattr__(self, "__wrapped__", local) object.__setattr__(self, "__name__", name) def _get_current_object(self) -> Any: return object.__getattribute__(self, "__LocalProxy_local")() @property def __dict__(self) -> Dict[str, Any]: # type: ignore try: return self._get_current_object().__dict__ except RuntimeError: raise AttributeError("__dict__") def __repr__(self) -> str: try: obj = self._get_current_object() except RuntimeError: return "<%s unbound>" % self.__class__.__name__ return repr(obj) def __bool__(self) -> bool: try: return bool(self._get_current_object()) except RuntimeError: return False def __dir__(self) -> Any: try: return dir(self._get_current_object()) except RuntimeError: return [] def __getattr__(self, name: Any) -> Any: if name == "__members__": return dir(self._get_current_object()) return getattr(self._get_current_object(), name) def __setitem__(self, key: Any, value: Any) -> Any: self._get_current_object()[key] = value def __delitem__(self, key: Any) -> Any: del self._get_current_object()[key] async def __aiter__(self) -> Any: async for x in self._get_current_object(): yield x __setattr__ = lambda x, n, v: setattr( # noqa: E731, E501 x._get_current_object(), n, v # type: ignore ) __delattr__ = lambda x, n: delattr(x._get_current_object(), n) # type: ignore # noqa: E731 __str__ = lambda x: str(x._get_current_object()) # type: ignore # noqa: E731 __lt__ = lambda x, o: x._get_current_object() < o # noqa: E731 __le__ = lambda x, o: x._get_current_object() <= o # noqa: E731 __eq__ = lambda x, o: x._get_current_object() == o # type: ignore # noqa: E731 __ne__ = lambda x, o: x._get_current_object() != o # type: ignore # noqa: E731 __gt__ = lambda x, o: x._get_current_object() > o # noqa: E731 __ge__ = lambda x, o: x._get_current_object() >= o # noqa: E731 __hash__ = lambda x: hash(x._get_current_object()) # type: ignore # noqa: E731 __call__ = lambda x, *a, **kw: x._get_current_object()(*a, **kw) # noqa: E731 __len__ = lambda x: len(x._get_current_object()) # noqa: E731 __getitem__ = lambda x, i: x._get_current_object()[i] # noqa: E731 __iter__ = lambda x: iter(x._get_current_object()) # noqa: E731 __contains__ = lambda x, i: i in x._get_current_object() # noqa: E731 __add__ = lambda x, o: x._get_current_object() + o # noqa: E731 __sub__ = lambda x, o: x._get_current_object() - o # noqa: E731 __mul__ = lambda x, o: x._get_current_object() * o # noqa: E731 __floordiv__ = lambda x, o: x._get_current_object() // o # noqa: E731 __mod__ = lambda x, o: x._get_current_object() % o # noqa: E731 __divmod__ = lambda x, o: x._get_current_object().__divmod__(o) # noqa: E731 __pow__ = lambda x, o: x._get_current_object() ** o # noqa: E731 __lshift__ = lambda x, o: x._get_current_object() << o # noqa: E731 __rshift__ = lambda x, o: x._get_current_object() >> o # noqa: E731 __and__ = lambda x, o: x._get_current_object() & o # noqa: E731 __xor__ = lambda x, o: x._get_current_object() ^ o # noqa: E731 __or__ = lambda x, o: x._get_current_object() | o # noqa: E731 __div__ = lambda x, o: x._get_current_object().__div__(o) # noqa: E731 __truediv__ = lambda x, o: x._get_current_object().__truediv__(o) # noqa: E731 __neg__ = lambda x: -(x._get_current_object()) # noqa: E731 __pos__ = lambda x: +(x._get_current_object()) # noqa: E731 __abs__ = lambda x: abs(x._get_current_object()) # noqa: E731 __invert__ = lambda x: ~(x._get_current_object()) # noqa: E731 __complex__ = lambda x: complex(x._get_current_object()) # noqa: E731 __int__ = lambda x: int(x._get_current_object()) # noqa: E731 __float__ = lambda x: float(x._get_current_object()) # noqa: E731 __oct__ = lambda x: oct(x._get_current_object()) # noqa: E731 __hex__ = lambda x: hex(x._get_current_object()) # noqa: E731 __index__ = lambda x: x._get_current_object().__index__() # noqa: E731 __coerce__ = lambda x, o: x._get_current_object().__coerce__(x, o) # noqa: E731 __enter__ = lambda x: x._get_current_object().__enter__() # noqa: E731 __exit__ = lambda x, *a, **kw: x._get_current_object().__exit__(*a, **kw) # noqa: E731 __radd__ = lambda x, o: o + x._get_current_object() # noqa: E731 __rsub__ = lambda x, o: o - x._get_current_object() # noqa: E731 __rmul__ = lambda x, o: o * x._get_current_object() # noqa: E731 __rdiv__ = lambda x, o: o / x._get_current_object() # noqa: E731 __rtruediv__ = __rdiv__ __rfloordiv__ = lambda x, o: o // x._get_current_object() # noqa: E731 __rmod__ = lambda x, o: o % x._get_current_object() # noqa: E731 __rdivmod__ = lambda x, o: x._get_current_object().__rdivmod__(o) # noqa: E731 __copy__ = lambda x: copy.copy(x._get_current_object()) # noqa: E731 __deepcopy__ = lambda x, memo: copy.deepcopy(x._get_current_object(), memo) # noqa: E731 __await__ = lambda x: x._get_current_object().__await__() # noqa: E731

41.740541

95

0.630536

1,009

7,722

4.22002

0.153617

0.145608

0.232973

0.207609

0.517849

0.463833

0.379286

0.318929

0.267262

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0

null

0

1

0

null

0

1

0

3

7cfc7ea83eddae1dd85543f912c4d06746387bfa

492

py

Python

30-39/35. final_class/final_class.py

dcragusa/PythonMorsels

5f75b51a68769036e4004e9ccdada6b220124ab6

[ "MIT" ]

1

2021-11-30T05:03:24.000Z

30-39/35. final_class/final_class.py

dcragusa/PythonMorsels

5f75b51a68769036e4004e9ccdada6b220124ab6

[ "MIT" ]

null

30-39/35. final_class/final_class.py

dcragusa/PythonMorsels

5f75b51a68769036e4004e9ccdada6b220124ab6

[ "MIT" ]

2

2021-04-18T05:26:43.000Z

2021-11-28T18:46:43.000Z

class Unsubclassable: def __init_subclass__(cls, **kwargs): raise TypeError('Unacceptable base type') def prevent_subclassing(): raise TypeError('Unacceptable base type') def final_class(cls): setattr(cls, '__init_subclass__', prevent_subclassing) return cls class UnsubclassableType(type): def __new__(cls, name, bases, dct): c = super().__new__(cls, name, bases, dct) setattr(c, '__init_subclass__', prevent_subclassing) return c

22.363636

60

0.693089

56

492

5.607143

0.428571

0.11465

0.165605

0.191083

0.579618

0.235669

0

0.203252

492

21

61

23.428571

0.80102

0

0.153846

0

0.159184

0

1

0.307692

false

0

0.615385

0

null

0

1

0

null

0

1

0

1

0

3

7cfd39821c7ad2ac471f6e189d3999d3560e833a

259

py

Python

users/views.py

AnvarKhan/django-python

bd54e44deb290f43ea5982c2ca9f37cd6c946879

[ "Apache-2.0" ]

1

2022-02-05T15:07:25.000Z

users/views.py

AnvarKhan/django-python

bd54e44deb290f43ea5982c2ca9f37cd6c946879

[ "Apache-2.0" ]

null

users/views.py

AnvarKhan/django-python

bd54e44deb290f43ea5982c2ca9f37cd6c946879

[ "Apache-2.0" ]

null

from django.views.generic import CreateView from django.urls import reverse_lazy from .forms import CustomUserCreationForm class SignUpView(CreateView): form_class = CustomUserCreationForm success_url = reverse_lazy('login') template_name = 'signup.html'

28.777778

43

0.830116

31

259

6.774194

0.677419

0.095238

0

0.104247

259

8

44

32.375

0.905172

0

0.061776

0

1

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false

0

0.428571

0

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null

0

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0

null

0

1

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1

0

3

6b0ff469900ccc9c854a18661fc7b7737ba3ac79

98

py

Python

pi_control/server_stats/apps.py

mhozza/pi-control

0dce821b4702519fedc3950270ee0091ed484ef6

[ "MIT" ]

null

pi_control/server_stats/apps.py

mhozza/pi-control

0dce821b4702519fedc3950270ee0091ed484ef6

[ "MIT" ]

10

2020-03-14T21:04:36.000Z

2022-03-03T21:51:07.000Z

pi_control/server_stats/apps.py

mhozza/pi-control

0dce821b4702519fedc3950270ee0091ed484ef6

[ "MIT" ]

null

from django.apps import AppConfig class ServerStatsConfig(AppConfig): name = "server_stats"

16.333333

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6.818182

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0.153061

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19.6

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null

0

1

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null

0

1

0

3

6b1e268c000917add1c1379d6ddcd9ab23f2b03b

245

py

Python

src/digibujogens/__main__.py

roaet/digibujogens

ab154edda69c091595902dd8b2e3fd273b2e7105

[ "MIT" ]

null

src/digibujogens/__main__.py

roaet/digibujogens

ab154edda69c091595902dd8b2e3fd273b2e7105

[ "MIT" ]

null

src/digibujogens/__main__.py

roaet/digibujogens

ab154edda69c091595902dd8b2e3fd273b2e7105

[ "MIT" ]

null

""" Main application entry point. python -m digibujogens ... """ def main(): """ Execute the application. """ raise NotImplementedError # Make the script executable. if __name__ == "__main__": raise SystemExit(main())

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null

0

null

0

1

0

3

6b3ef77f1a082e51763d4a446e010e19a72af147

101

py

Python

docs/source/tutorial/code/read_csv.py

HanSooLim/DIL-Project

069fa7e35a2e1edfff30dc2540d9b87f5db95dde

[ "MIT", "BSD-3-Clause" ]

2

2021-10-16T15:08:05.000Z

2021-10-16T15:59:57.000Z

docs/source/tutorial/code/read_csv.py

HanSooLim/DIL-Project

069fa7e35a2e1edfff30dc2540d9b87f5db95dde

[ "MIT", "BSD-3-Clause" ]

8

2021-10-21T04:48:12.000Z

2021-11-07T03:09:25.000Z

docs/source/tutorial/code/read_csv.py

HanSooLim/DIL-Project

069fa7e35a2e1edfff30dc2540d9b87f5db95dde

[ "MIT", "BSD-3-Clause" ]

3

2021-05-02T13:39:14.000Z

2021-05-31T14:05:56.000Z

import pandas datas = pandas.read_csv("../../Sample/example_dataset.csv", index_col=0) print(datas)

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0.8

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1

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null

0

1

0

3

866300a7100ec622abdb52b3c5fac82349d29555

60

py

Python

examples/web/handlers.py

nicoddemus/aioworkers

4ab85064844dc28141833d1348989d8c891f3d7d

[ "Apache-2.0" ]

45

2017-04-26T23:50:30.000Z

2021-12-29T03:21:06.000Z

examples/web/handlers.py

nicoddemus/aioworkers

4ab85064844dc28141833d1348989d8c891f3d7d

[ "Apache-2.0" ]

63

2017-08-01T10:35:45.000Z

2022-03-01T18:07:49.000Z

examples/web/handlers.py

nicoddemus/aioworkers

4ab85064844dc28141833d1348989d8c891f3d7d

[ "Apache-2.0" ]

6

2017-10-19T08:21:23.000Z

2021-12-29T03:25:32.000Z

async def handler(context): return await context.data

12

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60

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30

15

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1

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null

0

1

0

null

0

1

0

3

86789c8feaa8d10751a8b27ad6e7fc323ebc39ff

956

py

Python

redmine/__init__.py

hugoseabra/redmine-task-generator

b5ce1764f1c7588a7c82b25f7dd4bf07d1c105cf

[ "MIT" ]

null

redmine/__init__.py

hugoseabra/redmine-task-generator

b5ce1764f1c7588a7c82b25f7dd4bf07d1c105cf

[ "MIT" ]

4

2021-03-30T14:04:56.000Z

2021-06-10T19:40:52.000Z

redmine/__init__.py

hugoseabra/redmine-task-generator

b5ce1764f1c7588a7c82b25f7dd4bf07d1c105cf

[ "MIT" ]

null

from django.conf import settings from redminelib import Redmine as DefaultRedmine from .validator import RedmineInstanceValidator class Redmine(DefaultRedmine): def __init__(self, url=None, key=None): url = url or settings.REDMINE_BASE_URL key = key or settings.REDMINE_API_KEY super().__init__(url=url, key=key) self.validator = RedmineInstanceValidator(client=self) @property def score_field(self): return self.validator.score_field def instance_errors(self): errors = list() if self.validator.track_errors: errors += self.validator.track_errors if self.validator.score_field_errors: errors += self.validator.score_field_errors return errors def instance_valid(self) -> bool: return self.validator.instance_valid() def project_valid(self, project_id) -> bool: return self.validator.project_valid(project_id)

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0.090767

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956

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null

0

null

0

1

0

1

0

3

867af3eaf92e62e8468d18b191fba31f6c76639c

2,836

py

Python

utils/dsp.py

huchenxucs/WaveRNN

6d5805d54b8a3db99aa190083b550236f2c15d28

[ "MIT" ]

null

utils/dsp.py

huchenxucs/WaveRNN

6d5805d54b8a3db99aa190083b550236f2c15d28

[ "MIT" ]

null

utils/dsp.py

huchenxucs/WaveRNN

6d5805d54b8a3db99aa190083b550236f2c15d28

[ "MIT" ]

null

import math import numpy as np import librosa from utils import hparams as hp from scipy.signal import lfilter import soundfile as sf def label_2_float(x, bits): return 2 * x / (2**bits - 1.) - 1. def float_2_label(x, bits): assert abs(x).max() <= 1.0 x = (x + 1.) * (2**bits - 1) / 2 return x.clip(0, 2**bits - 1) def load_wav(path): return librosa.load(path, sr=hp.sample_rate)[0] def save_wav(x, path): # librosa.output.write_wav(path, x.astype(np.float32), sr=hp.sample_rate) sf.write(path, x.astype(np.float32), samplerate=hp.sample_rate) def split_signal(x): unsigned = x + 2**15 coarse = unsigned // 256 fine = unsigned % 256 return coarse, fine def combine_signal(coarse, fine): return coarse * 256 + fine - 2**15 def encode_16bits(x): return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16) def linear_to_mel(spectrogram): return librosa.feature.melspectrogram( S=spectrogram, sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin) ''' def build_mel_basis(): return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin) ''' def normalize(S): return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1) def denormalize(S): return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db def amp_to_db(x): return 20 * np.log10(np.maximum(1e-5, x)) def db_to_amp(x): return np.power(10.0, x * 0.05) def spectrogram(y): D = stft(y) S = amp_to_db(np.abs(D)) - hp.ref_level_db return normalize(S) def melspectrogram(y): D = stft(y) S = amp_to_db(linear_to_mel(np.abs(D))) return normalize(S) def stft(y): return librosa.stft( y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length) def pre_emphasis(x): return lfilter([1, -hp.preemphasis], [1], x) def de_emphasis(x): return lfilter([1], [1, -hp.preemphasis], x) def encode_mu_law(x, mu): mu = mu - 1 fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu) return np.floor((fx + 1) / 2 * mu + 0.5) def decode_mu_law(y, mu, from_labels=True): # TODO: get rid of log2 - makes no sense if from_labels: y = label_2_float(y, math.log2(mu)) mu = mu - 1 x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1) return x def reconstruct_waveform(mel, n_iter=32): """Uses Griffin-Lim phase reconstruction to convert from a normalized mel spectrogram back into a waveform.""" denormalized = denormalize(mel) amp_mel = db_to_amp(denormalized) S = librosa.feature.inverse.mel_to_stft( amp_mel, power=1, sr=hp.sample_rate, n_fft=hp.n_fft, fmin=hp.fmin) wav = librosa.core.griffinlim( S, n_iter=n_iter, hop_length=hp.hop_length, win_length=hp.win_length) return wav

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0.083381

0

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2,836

118

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0

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0

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0.633803

0

null

0

null

0

1

0

1

0

3

867bbaa4b747400e8e0dce95ef2502b3a1d6e3df

188

py

Python

app/helpers/geocode.py

Soumya117/finnazureflaskapp

794f82596a329ff1a2e4dc23d49903a0ef474f95

[ "MIT" ]

null

app/helpers/geocode.py

Soumya117/finnazureflaskapp

794f82596a329ff1a2e4dc23d49903a0ef474f95

[ "MIT" ]

2

2021-03-31T20:43:02.000Z

2021-12-13T20:13:40.000Z

app/helpers/geocode.py

Soumya117/finnparser

e89ff6e1a0c08b57a1b2f971d5f7bb888c2f4a05

[ "MIT" ]

null

import googlemaps gmaps = googlemaps.Client(key='google_key') def get_markers(address): geocode_result = gmaps.geocode(address) return geocode_result[0]['geometry']['location']

20.888889

52

0.75

23

188

5.956522

0.695652

0.189781

0

0.006061

0.12234

188

8

53

23.5

0.824242

0

0.138298

0

1

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0

0.2

0

0.6

0

1

0

null

0

1

0

null

0

1

0

3

8687c290ea5275e332a8f9c623d8f42da0525b01

244

py

Python

mayan/apps/rest_api/exceptions.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

1

2021-02-24T15:03:23.000Z

mayan/apps/rest_api/exceptions.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

10

2021-03-20T00:01:17.000Z

2022-03-12T00:48:43.000Z

mayan/apps/rest_api/exceptions.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

1

2020-08-09T09:06:59.000Z

class APIError(Exception): """ Base exception for the API app """ pass class APIResourcePatternError(APIError): """ Raised when an app tries to override an existing URL regular expression pattern """ pass

16.266667

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244

14

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0

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0

1

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null

0

null

0

1

0

3

868c6ea160dd2c056e7da123714e1987646a86cf

9,215

py

Python

ravem/tests/util_test.py

bpedersen2/indico-plugins-cern

c4f06d11d981c316fc8de2892758484deb58e2f5

[ "MIT" ]

null

ravem/tests/util_test.py

bpedersen2/indico-plugins-cern

c4f06d11d981c316fc8de2892758484deb58e2f5

[ "MIT" ]

null

ravem/tests/util_test.py

bpedersen2/indico-plugins-cern

c4f06d11d981c316fc8de2892758484deb58e2f5

[ "MIT" ]

null

# This file is part of the CERN Indico plugins. # Copyright (C) 2014 - 2022 CERN # # The CERN Indico plugins are free software; you can redistribute # them and/or modify them under the terms of the MIT License; see # the LICENSE file for more details. from unittest.mock import MagicMock import pytest from requests.exceptions import HTTPError, Timeout from indico.testing.util import extract_logs from indico_ravem.plugin import RavemPlugin from indico_ravem.util import has_access, ravem_api_call @pytest.mark.usefixtures('db') @pytest.mark.parametrize('method', ('get', 'post')) def test_correct_http_method(mocker, method): request = mocker.patch('indico_ravem.util.requests.request') response = MagicMock() response.json.return_value = {'result': 'test'} response.raise_for_status.return_value = False request.return_value = response ravem_api_call('test_endpoint', method=method, param1='test1', param2='test2') assert request.call_count == 1 assert request.call_args[0][0] == method @pytest.mark.usefixtures('db') def test_correct_auth_method(mocker): request = mocker.patch('indico_ravem.util.requests.request') response = MagicMock() response.json.return_value = {'result': 'test'} response.raise_for_status.return_value = False request.return_value = response token = 'foo' RavemPlugin.settings.set('access_token', token) ravem_api_call('test_endpoint', param1='test1', param2='test2') assert request.call_count == 1 assert 'Authorization' in request.call_args[1]['headers'] assert request.call_args[1]['headers']['Authorization'] == 'Bearer %s' % token @pytest.mark.usefixtures('db') def test_accepts_json(mocker): request = mocker.patch('indico_ravem.util.requests.request') response = MagicMock() response.json.return_value = {'result': 'test'} response.raise_for_status.return_value = False request.return_value = response ravem_api_call('test_endpoint', param1='test1', param2='test2') assert request.call_count == 1 assert request.call_args[1]['headers']['Accept'] == 'application/json' @pytest.mark.usefixtures('db') @pytest.mark.parametrize(('root_endpoint', 'endpoint', 'expected_url'), ( ('https://ravem.test/', 'final_endpoint', 'https://ravem.test/final_endpoint'), ('https://ravem.test/api/', 'final_endpoint', 'https://ravem.test/api/final_endpoint'), ('https://ravem.test/api/v2/', 'final_endpoint', 'https://ravem.test/api/v2/final_endpoint'), ('https://ravem.test', './final_endpoint', 'https://ravem.test/final_endpoint'), ('https://ravem.test/api/', './final_endpoint', 'https://ravem.test/api/final_endpoint'), ('https://ravem.test/api/v2/', './final_endpoint', 'https://ravem.test/api/v2/final_endpoint'), ('https://ravem.test', 'sub/final_endpoint', 'https://ravem.test/sub/final_endpoint'), ('https://ravem.test/api/', 'sub/final_endpoint', 'https://ravem.test/api/sub/final_endpoint'), ('https://ravem.test/api/v2/', 'sub/final_endpoint', 'https://ravem.test/api/v2/sub/final_endpoint'), ('https://ravem.test', './sub/final_endpoint', 'https://ravem.test/sub/final_endpoint'), ('https://ravem.test/api/', './sub/final_endpoint', 'https://ravem.test/api/sub/final_endpoint'), ('https://ravem.test/api/v2/', './sub/final_endpoint', 'https://ravem.test/api/v2/sub/final_endpoint'), ('https://ravem.test/', '', 'https://ravem.test/'), ('https://ravem.test/api/', '', 'https://ravem.test/api/'), ('https://ravem.test/api/v2/', '', 'https://ravem.test/api/v2/'), )) def test_correct_api_endpoint(mocker, root_endpoint, endpoint, expected_url): request = mocker.patch('indico_ravem.util.requests.request') response = MagicMock() response.json.return_value = {'result': 'test'} response.raise_for_status.return_value = False request.return_value = response RavemPlugin.settings.set('api_endpoint', root_endpoint) ravem_api_call(endpoint, param1='test1', param2='test2') assert request.call_count == 1 assert request.call_args[0][1] == expected_url @pytest.mark.usefixtures('db') @pytest.mark.parametrize('params', ( {}, {'p1': '1stparam'}, {'p1': '1stparam', 'p2': '2ndparam'} )) def test_params_generated(mocker, params): request = mocker.patch('indico_ravem.util.requests.request') response = MagicMock() response.json.return_value = {'result': 'test'} response.raise_for_status.return_value = False request.return_value = response ravem_api_call('test_endpoint', params=params) assert request.call_count == 1 assert request.call_args[1]['params'] == params @pytest.mark.usefixtures('db') def test_raises_timeout(mocker): request = mocker.patch('indico_ravem.util.requests.request') request.side_effect = Timeout('Timeout test error message', request=request) with pytest.raises(Timeout) as excinfo: ravem_api_call('test_endpoint') assert str(excinfo.value) == "Timeout while contacting the room." assert request.call_count == 1 @pytest.mark.usefixtures('db') @pytest.mark.parametrize(('method', 'params'), ( ('get', {}), ('post', {}), ('get', {'p1': '1stparam'}), ('post', {'p1': '1stparam'}), ('get', {'p1': '1stparam', 'p2': '2ndparam'}), ('post', {'p1': '1stparam', 'p2': '2ndparam'}) )) def test_unexpected_exception_is_logged(mocker, caplog, method, params): request = mocker.patch('indico_ravem.util.requests.request') request.side_effect = IndexError('this is unexpected') with pytest.raises(IndexError) as excinfo: ravem_api_call('test_endpoint', method=method, **params) assert str(excinfo.value) == 'this is unexpected' log = extract_logs(caplog, one=True, name='indico.plugin.ravem') assert log.message == "failed call: {} {} with {}: {}".format(method.upper(), 'test_endpoint', params, 'this is unexpected') assert request.call_count == 1 @pytest.mark.usefixtures('db') @pytest.mark.parametrize(('method', 'params'), ( ('get', {}), ('post', {}), ('get', {'p1': '1stparam'}), ('post', {'p1': '1stparam'}), ('get', {'p1': '1stparam', 'p2': '2ndparam'}), ('post', {'p1': '1stparam', 'p2': '2ndparam'}) )) def test_http_error_is_logged(mocker, caplog, method, params): request = mocker.patch('indico_ravem.util.requests.request') request.method = method.upper() request.url = RavemPlugin.settings.get('api_endpoint') + 'test_endpoint' response = MagicMock() response.raise_for_status.side_effect = HTTPError('Well this is embarrassing') response.request = request response.url = response.request.url request.return_value = response with pytest.raises(HTTPError) as excinfo: ravem_api_call('test_endpoint', method=method, **params) assert str(excinfo.value) == 'Well this is embarrassing' log = extract_logs(caplog, one=True, name='indico.plugin.ravem') assert log.message == '{} {} failed with {}'.format( method.upper(), RavemPlugin.settings.get('api_endpoint') + 'test_endpoint', 'Well this is embarrassing') assert request.call_count == 1 @pytest.mark.usefixtures('db') def test_unlinked_event_vc_room_has_no_access(): event_vc_room = MagicMock() event_vc_room.link_object = None assert not has_access(event_vc_room) @pytest.mark.usefixtures('db', 'request_context') def test_unlinked_room_has_no_access(mocker): session = mocker.patch('indico_ravem.util.session') session.user = 'Guinea Pig' event_vc_room = MagicMock() event_vc_room.link_object.room = None assert not has_access(event_vc_room) @pytest.mark.usefixtures('db', 'request_context') def test_check_if_current_user_is_room_owner(mocker): session = mocker.patch('indico_ravem.util.session') session.user = 'Guinea Pig' request = mocker.patch('indico_ravem.util.request') request.remote_addr = '111.222.123.123' retrieve_principal = mocker.patch('indico_ravem.util._retrieve_principal') retrieve_principal.side_effect = lambda x: session.user event_vc_room = MagicMock() event_vc_room.link_object.room.has_equipment = MagicMock(return_value=True) event_vc_room.link_object.room.get_attribute_value.return_value = request.remote_addr event_vc_room.vc_room.data.get.return_value = 'User:123' event_vc_room.event.can_manage.return_value = False assert has_access(event_vc_room) @pytest.mark.usefixtures('db', 'request_context') def test_check_if_current_user_can_modify(mocker): request = mocker.patch('indico_ravem.util.request') request.remote_addr = '111.222.123.123' session = mocker.patch('indico_ravem.util.session') session.user = 'Guinea Pig' mocker.patch('indico_ravem.util._retrieve_principal') event_vc_room = MagicMock() event_vc_room.link_object.room.has_equipment = MagicMock(return_value=True) event_vc_room.link_object.room.get_attribute_value.return_value = request.remote_addr event_vc_room.event.can_manage.return_value = True assert has_access(event_vc_room) event_vc_room.event.can_manage.assert_called_once_with(session.user)

39.549356

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0

1

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1

0

null

0

3

868e31d3b6d09c73dfd001c290d85e56d3f9bb45

672

py

Python

app/http/middleware/LoadUserMiddleware.py

josephmancuso/masonite-forum

a91c7386f3e0b02b0ac71623eb295a7543cb60fd

[ "MIT" ]

11

2018-07-08T17:44:28.000Z

2020-03-02T10:45:37.000Z

app/http/middleware/LoadUserMiddleware.py

josephmancuso/masonite-forum

a91c7386f3e0b02b0ac71623eb295a7543cb60fd

[ "MIT" ]

2

2018-07-21T07:49:09.000Z

2019-05-29T14:34:42.000Z

app/http/middleware/LoadUserMiddleware.py

josephmancuso/masonite-forum

a91c7386f3e0b02b0ac71623eb295a7543cb60fd

[ "MIT" ]

5

2018-07-12T02:36:14.000Z

2020-04-05T21:10:30.000Z

''' Load User Middleware''' from masonite.facades.Auth import Auth class LoadUserMiddleware: ''' Middleware class which loads the current user into the request ''' def __init__(self, Request): ''' Inject Any Dependencies From The Service Container ''' self.request = Request def before(self): ''' Run This Middleware Before The Route Executes ''' self.load_user(self.request) return self.request def after(self): ''' Run This Middleware After The Route Executes ''' pass def load_user(self, request): ''' Load user into the request ''' request.set_user(Auth(request).user())

29.217391

74

0.644345

80

672

5.325

0.4125

0.129108

0.051643

0.084507

0

0.254464

672

22

75

30.545455

0.850299

0.38244

0

1

0.363636

false

0.090909

0

0.636364

0

null

0

null

0

1

0

1

0

1

0

3

869c4c6a792894e8eb7116f05f76e9950b851051

364

py

Python

dbclient/__init__.py

dmoore247/db-migration

cc75d491d7dd7e9e24b5a35dd3d1080317b25520

[ "Apache-2.0" ]

null

dbclient/__init__.py

dmoore247/db-migration

cc75d491d7dd7e9e24b5a35dd3d1080317b25520

[ "Apache-2.0" ]

null

dbclient/__init__.py

dmoore247/db-migration

cc75d491d7dd7e9e24b5a35dd3d1080317b25520

[ "Apache-2.0" ]

null

import json, requests, datetime from cron_descriptor import get_description from .dbclient import dbclient from .JobsClient import JobsClient from .ClustersClient import ClustersClient from .WorkspaceClient import WorkspaceClient from .ScimClient import ScimClient from .LibraryClient import LibraryClient from .HiveClient import HiveClient from .parser import *

30.333333

44

0.857143

41

364

7.560976

0.414634

0

0.112637

364

11

45

33.090909

0.959752

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

869e772414d99f560741ba4d5f3b4440b61ae41b

2,931

py

Python

Ifc/IfcBase.py

gsimon75/IFC_parser

f9fbe2afa48795bbb502530bc9ab5c4db842e10f

[ "BSD-2-Clause" ]

28

2019-12-02T11:41:14.000Z

2022-03-02T22:53:24.000Z

Ifc/IfcBase.py

gsimon75/IFC_parser

f9fbe2afa48795bbb502530bc9ab5c4db842e10f

[ "BSD-2-Clause" ]

4

2019-08-30T13:52:40.000Z

2022-02-02T02:31:31.000Z

Ifc/IfcBase.py

gsimon75/IFC_parser

f9fbe2afa48795bbb502530bc9ab5c4db842e10f

[ "BSD-2-Clause" ]

6

2020-07-11T22:35:07.000Z

2022-03-18T15:12:46.000Z

from Ifc.ClassRegistry import ifc_class, ifc_abstract_class, ifc_fallback_class @ifc_abstract_class class IfcEntity: """ Generic IFC entity, only for subclassing from it """ def __init__(self, rtype, args): """ rtype: Resource type args: Arguments in *reverse* order, so you can just args.pop() from it """ self.rtype = rtype def __str__(self): return self.rtype def __json__(self): return {'rtype': self.rtype} @ifc_fallback_class class IfcGenericEntity(IfcEntity): """ Generic IFC entity: type and args """ def __init__(self, rtype, args): IfcEntity.__init__(self, rtype, args) self.args = args self.args.reverse() def __str__(self): return "Gen<{sup}>{a}".format( sup=IfcEntity.__str__(self), a=self.args) @ifc_class class IfcScalarValue(IfcEntity): def __init__(self, rtype, args): IfcEntity.__init__(self, rtype, args) self.value = args.pop() def __str__(self): return str(self.value) @ifc_class class BOOLEAN(IfcScalarValue): pass @ifc_class class REAL(IfcScalarValue): pass @ifc_class class BINARY(IfcScalarValue): pass @ifc_class class INTEGER(IfcScalarValue): pass @ifc_class class NUMBER(IfcScalarValue): pass @ifc_class class STRING(IfcScalarValue): pass @ifc_class class LOGICAL(IfcScalarValue): pass class Omitted: """ Marked with '*' it states that some supertype had defined that attribute, but in the subtype it is a derived (calculated) value, so it no longer makes sense to explicitely assign value to it. """ # TODO: Haven't tried if it can be handled 'just as expected' def __init__(self): pass def __str__(self): return "<omitted>" def __json__(self): return None # class-level, enough to reference, no need to create multiple instances (doesn't hurt though) omitted = Omitted() class Reference: """ Refers to another entity by its index """ def __init__(self, index): self.index = index def __str__(self): return "<#{idx}>".format(idx=self.index) def __json__(self): return {'ref': self.index} class EnumValue: """ Item from some set of enumerated values. """ def __init__(self, value): self.value = value def __str__(self): return "<.{val}.>".format(val=self.value) def __json__(self): return self.value @ifc_class class STEPHeader(IfcEntity): def __init__(self): IfcEntity.__init__(self, "STEPHeader", []) self.fields = {} def add(self, e): self.fields[e.rtype] = e def __str__(self): return "STEPHeader({f})".format(f=", ".join(map(lambda f: "{n}: {v}".format(n=f[0], v=str(f[1])), self.fields.iteritems()))) # vim: set sw=4 ts=4 et:

21.23913

132

0.624701

369

2,931

4.674797

0.336043

0.063768

0.067826

0.064928

0.202899

0.057971

0

0.001842

0.258956

2,931

137

133

21.394161

0.792357

0.212214

0

0.448718

0

0.037341

0

0.007299

0

1

0.24359

false

0.102564

0.012821

0.141026

0.576923

0

null

0

null

0

1

0

1

0

1

0

3

86a6e60b85eb87efd2531834b58c525dde29390d

21,322

py

Python

school/views.py

pa-one-patel/college_managenment

be6f6dcac1f7e01f71d95f445e2118e8eec3fe3a

[ "MIT" ]

1

2021-04-11T12:05:44.000Z

school/views.py

aliffauzi/schoolmanagement

6a4477af01df148404d1ff2941f74accb5717b09

[ "MIT" ]

6

2021-03-19T04:10:49.000Z

2021-09-22T19:06:14.000Z

school/views.py

aliffauzi/schoolmanagement

6a4477af01df148404d1ff2941f74accb5717b09

[ "MIT" ]

1

2021-04-11T12:07:08.000Z

from django.shortcuts import render,redirect,reverse from . import forms,models from django.db.models import Sum from django.contrib.auth.models import Group from django.http import HttpResponseRedirect from django.contrib.auth.decorators import login_required,user_passes_test def home_view(request): if request.user.is_authenticated: return HttpResponseRedirect('afterlogin') return render(request,'school/index.html') #for showing signup/login button for teacher(by sumit) def adminclick_view(request): if request.user.is_authenticated: return HttpResponseRedirect('afterlogin') return render(request,'school/adminclick.html') #for showing signup/login button for teacher(by sumit) def teacherclick_view(request): if request.user.is_authenticated: return HttpResponseRedirect('afterlogin') return render(request,'school/teacherclick.html') #for showing signup/login button for student(by sumit) def studentclick_view(request): if request.user.is_authenticated: return HttpResponseRedirect('afterlogin') return render(request,'school/studentclick.html') def admin_signup_view(request): form=forms.AdminSigupForm() if request.method=='POST': form=forms.AdminSigupForm(request.POST) if form.is_valid(): user=form.save() user.set_password(user.password) user.save() my_admin_group = Group.objects.get_or_create(name='ADMIN') my_admin_group[0].user_set.add(user) return HttpResponseRedirect('adminlogin') return render(request,'school/adminsignup.html',{'form':form}) def student_signup_view(request): form1=forms.StudentUserForm() form2=forms.StudentExtraForm() mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.StudentUserForm(request.POST) form2=forms.StudentExtraForm(request.POST) if form1.is_valid() and form2.is_valid(): user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.user=user user2=f2.save() my_student_group = Group.objects.get_or_create(name='STUDENT') my_student_group[0].user_set.add(user) return HttpResponseRedirect('studentlogin') return render(request,'school/studentsignup.html',context=mydict) def teacher_signup_view(request): form1=forms.TeacherUserForm() form2=forms.TeacherExtraForm() mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.TeacherUserForm(request.POST) form2=forms.TeacherExtraForm(request.POST) if form1.is_valid() and form2.is_valid(): user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.user=user user2=f2.save() my_teacher_group = Group.objects.get_or_create(name='TEACHER') my_teacher_group[0].user_set.add(user) return HttpResponseRedirect('teacherlogin') return render(request,'school/teachersignup.html',context=mydict) #for checking user is techer , student or admin(by sumit) def is_admin(user): return user.groups.filter(name='ADMIN').exists() def is_teacher(user): return user.groups.filter(name='TEACHER').exists() def is_student(user): return user.groups.filter(name='STUDENT').exists() def afterlogin_view(request): if is_admin(request.user): return redirect('admin-dashboard') elif is_teacher(request.user): accountapproval=models.TeacherExtra.objects.all().filter(user_id=request.user.id,status=True) if accountapproval: return redirect('teacher-dashboard') else: return render(request,'school/teacher_wait_for_approval.html') elif is_student(request.user): accountapproval=models.StudentExtra.objects.all().filter(user_id=request.user.id,status=True) if accountapproval: return redirect('student-dashboard') else: return render(request,'school/student_wait_for_approval.html') #for dashboard of adminnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_dashboard_view(request): teachercount=models.TeacherExtra.objects.all().filter(status=True).count() pendingteachercount=models.TeacherExtra.objects.all().filter(status=False).count() studentcount=models.StudentExtra.objects.all().filter(status=True).count() pendingstudentcount=models.StudentExtra.objects.all().filter(status=False).count() teachersalary=models.TeacherExtra.objects.filter(status=True).aggregate(Sum('salary')) pendingteachersalary=models.TeacherExtra.objects.filter(status=False).aggregate(Sum('salary')) studentfee=models.StudentExtra.objects.filter(status=True).aggregate(Sum('fee',default=0)) pendingstudentfee=models.StudentExtra.objects.filter(status=False).aggregate(Sum('fee')) notice=models.Notice.objects.all() #aggregate function return dictionary so fetch data from dictionay(by sumit) mydict={ 'teachercount':teachercount, 'pendingteachercount':pendingteachercount, 'studentcount':studentcount, 'pendingstudentcount':pendingstudentcount, 'teachersalary':teachersalary['salary__sum'], 'pendingteachersalary':pendingteachersalary['salary__sum'], 'studentfee':studentfee['fee__sum'], 'pendingstudentfee':pendingstudentfee['fee__sum'], 'notice':notice } return render(request,'school/admin_dashboard.html',context=mydict) #for teacher sectionnnnnnnn by adminnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_teacher_view(request): return render(request,'school/admin_teacher.html') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_add_teacher_view(request): form1=forms.TeacherUserForm() form2=forms.TeacherExtraForm() mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.TeacherUserForm(request.POST) form2=forms.TeacherExtraForm(request.POST) if form1.is_valid() and form2.is_valid(): user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.user=user f2.status=True f2.save() my_teacher_group = Group.objects.get_or_create(name='TEACHER') my_teacher_group[0].user_set.add(user) return HttpResponseRedirect('admin-teacher') return render(request,'school/admin_add_teacher.html',context=mydict) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_teacher_view(request): teachers=models.TeacherExtra.objects.all().filter(status=True) return render(request,'school/admin_view_teacher.html',{'teachers':teachers}) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_approve_teacher_view(request): teachers=models.TeacherExtra.objects.all().filter(status=False) return render(request,'school/admin_approve_teacher.html',{'teachers':teachers}) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def approve_teacher_view(request,pk): teacher=models.TeacherExtra.objects.get(id=pk) teacher.status=True teacher.save() return redirect(reverse('admin-approve-teacher')) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def delete_teacher_view(request,pk): teacher=models.TeacherExtra.objects.get(id=pk) user=models.User.objects.get(id=teacher.user_id) user.delete() teacher.delete() return redirect('admin-approve-teacher') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def delete_teacher_from_school_view(request,pk): teacher=models.TeacherExtra.objects.get(id=pk) user=models.User.objects.get(id=teacher.user_id) user.delete() teacher.delete() return redirect('admin-view-teacher') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def update_teacher_view(request,pk): teacher=models.TeacherExtra.objects.get(id=pk) user=models.User.objects.get(id=teacher.user_id) form1=forms.TeacherUserForm(instance=user) form2=forms.TeacherExtraForm(instance=teacher) mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.TeacherUserForm(request.POST,instance=user) form2=forms.TeacherExtraForm(request.POST,instance=teacher) print(form1) if form1.is_valid() and form2.is_valid(): user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.status=True f2.save() return redirect('admin-view-teacher') return render(request,'school/admin_update_teacher.html',context=mydict) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_teacher_salary_view(request): teachers=models.TeacherExtra.objects.all() return render(request,'school/admin_view_teacher_salary.html',{'teachers':teachers}) #for student by adminnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_student_view(request): return render(request,'school/admin_student.html') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_add_student_view(request): form1=forms.StudentUserForm() form2=forms.StudentExtraForm() mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.StudentUserForm(request.POST) form2=forms.StudentExtraForm(request.POST) if form1.is_valid() and form2.is_valid(): print("form is valid") user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.user=user f2.status=True f2.save() my_student_group = Group.objects.get_or_create(name='STUDENT') my_student_group[0].user_set.add(user) else: print("form is invalid") return HttpResponseRedirect('admin-student') return render(request,'school/admin_add_student.html',context=mydict) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_student_view(request): students=models.StudentExtra.objects.all().filter(status=True) return render(request,'school/admin_view_student.html',{'students':students}) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def delete_student_from_school_view(request,pk): student=models.StudentExtra.objects.get(id=pk) user=models.User.objects.get(id=student.user_id) user.delete() student.delete() return redirect('admin-view-student') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def delete_student_view(request,pk): student=models.StudentExtra.objects.get(id=pk) user=models.User.objects.get(id=student.user_id) user.delete() student.delete() return redirect('admin-approve-student') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def update_student_view(request,pk): student=models.StudentExtra.objects.get(id=pk) user=models.User.objects.get(id=student.user_id) form1=forms.StudentUserForm(instance=user) form2=forms.StudentExtraForm(instance=student) mydict={'form1':form1,'form2':form2} if request.method=='POST': form1=forms.StudentUserForm(request.POST,instance=user) form2=forms.StudentExtraForm(request.POST,instance=student) print(form1) if form1.is_valid() and form2.is_valid(): user=form1.save() user.set_password(user.password) user.save() f2=form2.save(commit=False) f2.status=True f2.save() return redirect('admin-view-student') return render(request,'school/admin_update_student.html',context=mydict) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_approve_student_view(request): students=models.StudentExtra.objects.all().filter(status=False) return render(request,'school/admin_approve_student.html',{'students':students}) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def approve_student_view(request,pk): students=models.StudentExtra.objects.get(id=pk) students.status=True students.save() return redirect(reverse('admin-approve-student')) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_student_fee_view(request): students=models.StudentExtra.objects.all() return render(request,'school/admin_view_student_fee.html',{'students':students}) #attendance related viewwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_attendance_view(request): return render(request,'school/admin_attendance.html') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_take_attendance_view(request,cl): students=models.StudentExtra.objects.all().filter(cl=cl) print(students) aform=forms.AttendanceForm() if request.method=='POST': form=forms.AttendanceForm(request.POST) if form.is_valid(): Attendances=request.POST.getlist('present_status') date=form.cleaned_data['date'] for i in range(len(Attendances)): AttendanceModel=models.Attendance() AttendanceModel.cl=cl AttendanceModel.date=date AttendanceModel.present_status=Attendances[i] AttendanceModel.roll=students[i].roll AttendanceModel.save() return redirect('admin-attendance') else: print('form invalid') return render(request,'school/admin_take_attendance.html',{'students':students,'aform':aform}) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_attendance_view(request,cl): form=forms.AskDateForm() if request.method=='POST': form=forms.AskDateForm(request.POST) if form.is_valid(): date=form.cleaned_data['date'] attendancedata=models.Attendance.objects.all().filter(date=date,cl=cl) studentdata=models.StudentExtra.objects.all().filter(cl=cl) mylist=zip(attendancedata,studentdata) return render(request,'school/admin_view_attendance_page.html',{'cl':cl,'mylist':mylist,'date':date}) else: print('form invalid') return render(request,'school/admin_view_attendance_ask_date.html',{'cl':cl,'form':form}) #fee related view by adminnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_fee_view(request): return render(request,'school/admin_fee.html') @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_view_fee_view(request,cl): feedetails=models.StudentExtra.objects.all().filter(cl=cl) return render(request,'school/admin_view_fee.html',{'feedetails':feedetails,'cl':cl}) #notice related viewsssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss(by sumit) @login_required(login_url='adminlogin') @user_passes_test(is_admin) def admin_notice_view(request): form=forms.NoticeForm() if request.method=='POST': form=forms.NoticeForm(request.POST) if form.is_valid(): form=form.save(commit=False) form.by=request.user.first_name form.save() return redirect('admin-dashboard') return render(request,'school/admin_notice.html',{'form':form}) #for TEACHER LOGIN SECTIONNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN(by sumit) @login_required(login_url='teacherlogin') @user_passes_test(is_teacher) def teacher_dashboard_view(request): teacherdata=models.TeacherExtra.objects.all().filter(status=True,user_id=request.user.id) notice=models.Notice.objects.all() mydict={ 'salary':teacherdata[0].salary, 'mobile':teacherdata[0].mobile, 'date':teacherdata[0].joindate, 'notice':notice } return render(request,'school/teacher_dashboard.html',context=mydict) @login_required(login_url='teacherlogin') @user_passes_test(is_teacher) def teacher_attendance_view(request): return render(request,'school/teacher_attendance.html') @login_required(login_url='teacherlogin') @user_passes_test(is_teacher) def teacher_take_attendance_view(request,cl): students=models.StudentExtra.objects.all().filter(cl=cl) aform=forms.AttendanceForm() if request.method=='POST': form=forms.AttendanceForm(request.POST) if form.is_valid(): Attendances=request.POST.getlist('present_status') date=form.cleaned_data['date'] for i in range(len(Attendances)): AttendanceModel=models.Attendance() AttendanceModel.cl=cl AttendanceModel.date=date AttendanceModel.present_status=Attendances[i] AttendanceModel.roll=students[i].roll AttendanceModel.save() return redirect('teacher-attendance') else: print('form invalid') return render(request,'school/teacher_take_attendance.html',{'students':students,'aform':aform}) @login_required(login_url='teacherlogin') @user_passes_test(is_teacher) def teacher_view_attendance_view(request,cl): form=forms.AskDateForm() if request.method=='POST': form=forms.AskDateForm(request.POST) if form.is_valid(): date=form.cleaned_data['date'] attendancedata=models.Attendance.objects.all().filter(date=date,cl=cl) studentdata=models.StudentExtra.objects.all().filter(cl=cl) mylist=zip(attendancedata,studentdata) return render(request,'school/teacher_view_attendance_page.html',{'cl':cl,'mylist':mylist,'date':date}) else: print('form invalid') return render(request,'school/teacher_view_attendance_ask_date.html',{'cl':cl,'form':form}) @login_required(login_url='teacherlogin') @user_passes_test(is_teacher) def teacher_notice_view(request): form=forms.NoticeForm() if request.method=='POST': form=forms.NoticeForm(request.POST) if form.is_valid(): form=form.save(commit=False) form.by=request.user.first_name form.save() return redirect('teacher-dashboard') else: print('form invalid') return render(request,'school/teacher_notice.html',{'form':form}) #FOR STUDENT AFTER THEIR Loginnnnnnnnnnnnnnnnnnnnn(by sumit) @login_required(login_url='studentlogin') @user_passes_test(is_student) def student_dashboard_view(request): studentdata=models.StudentExtra.objects.all().filter(status=True,user_id=request.user.id) notice=models.Notice.objects.all() mydict={ 'roll':studentdata[0].roll, 'mobile':studentdata[0].mobile, 'fee':studentdata[0].fee, 'notice':notice } return render(request,'school/student_dashboard.html',context=mydict) @login_required(login_url='studentlogin') @user_passes_test(is_student) def student_attendance_view(request): form=forms.AskDateForm() if request.method=='POST': form=forms.AskDateForm(request.POST) if form.is_valid(): date=form.cleaned_data['date'] studentdata=models.StudentExtra.objects.all().filter(user_id=request.user.id,status=True) attendancedata=models.Attendance.objects.all().filter(date=date,cl=studentdata[0].cl,roll=studentdata[0].roll) mylist=zip(attendancedata,studentdata) return render(request,'school/student_view_attendance_page.html',{'mylist':mylist,'date':date}) else: print('form invalid') return render(request,'school/student_view_attendance_ask_date.html',{'form':form}) # for aboutus and contact ussssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss (by sumit) def aboutus_view(request): return render(request,'school/aboutus.html') def contactus_view(request): sub = forms.ContactusForm() if request.method == 'POST': sub = forms.ContactusForm(request.POST) if sub.is_valid(): email = sub.cleaned_data['Email'] name=sub.cleaned_data['Name'] message = sub.cleaned_data['Message'] send_mail(str(name)+' || '+str(email),message, EMAIL_HOST_USER, ['[email protected]'], fail_silently = False) return render(request, 'school/contactussuccess.html') return render(request, 'school/contactus.html', {'form':sub})

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1

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null

0

1

0

3

86bcd2890d4f11513d628469a8efe8d1af2d7195

65

py

Python

src/cicd_sim/artifact/__init__.py

Software-Natives-OSS/cicd_sim

19452a5b06a6c6d99322c9b6777c501025e954f1

[ "MIT" ]

null

src/cicd_sim/artifact/__init__.py

Software-Natives-OSS/cicd_sim

19452a5b06a6c6d99322c9b6777c501025e954f1

[ "MIT" ]

8

2020-03-12T05:51:56.000Z

2020-03-15T17:31:12.000Z

src/cicd_sim/artifact/__init__.py

Software-Natives-OSS/cicd_sim

19452a5b06a6c6d99322c9b6777c501025e954f1

[ "MIT" ]

null

from . artifactory import Artifactory __all__ = ['Artifactory']

16.25

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null

0

1

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null

0

1

0

3

86c445a03cb1fedcfaa9af4175640a3d81afd9b9

8,505

py

Python

reco_utils/recommender/deeprec/io/iterator.py

yutian-zhao/recommenders

17b9c1280a79019dd91f50b3a7e66f25cb5004b1

[ "MIT" ]

null

reco_utils/recommender/deeprec/io/iterator.py

yutian-zhao/recommenders

17b9c1280a79019dd91f50b3a7e66f25cb5004b1

[ "MIT" ]

null

reco_utils/recommender/deeprec/io/iterator.py

yutian-zhao/recommenders

17b9c1280a79019dd91f50b3a7e66f25cb5004b1

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import numpy as np # import tensorflow as tf import abc class BaseIterator(object): @abc.abstractmethod def parser_one_line(self, line): pass @abc.abstractmethod def load_data_from_file(self, infile): pass @abc.abstractmethod def _convert_data(self, labels, features): pass @abc.abstractmethod def gen_feed_dict(self, data_dict): pass # class FFMTextIterator(BaseIterator): # """Data loader for FFM format based models, such as xDeepFM. # Iterator will not load the whole data into memory. Instead, it loads data into memory # per mini-batch, so that large files can be used as input data. # """ # def __init__(self, hparams, graph, col_spliter=" ", ID_spliter="%"): # """Initialize an iterator. Create necessary placeholders for the model. # Args: # hparams (obj): Global hyper-parameters. Some key settings such as #_feature and #_field are there. # graph (obj): the running graph. All created placeholder will be added to this graph. # col_spliter (str): column splitter in one line. # ID_spliter (str): ID splitter in one line. # """ # self.feature_cnt = hparams.FEATURE_COUNT # self.field_cnt = hparams.FIELD_COUNT # self.col_spliter = col_spliter # self.ID_spliter = ID_spliter # self.batch_size = hparams.batch_size # self.graph = graph # with self.graph.as_default(): # self.labels = tf.placeholder(tf.float32, [None, 1], name="label") # self.fm_feat_indices = tf.placeholder( # tf.int64, [None, 2], name="fm_feat_indices" # ) # self.fm_feat_values = tf.placeholder( # tf.float32, [None], name="fm_feat_values" # ) # self.fm_feat_shape = tf.placeholder(tf.int64, [None], name="fm_feat_shape") # self.dnn_feat_indices = tf.placeholder( # tf.int64, [None, 2], name="dnn_feat_indices" # ) # self.dnn_feat_values = tf.placeholder( # tf.int64, [None], name="dnn_feat_values" # ) # self.dnn_feat_weights = tf.placeholder( # tf.float32, [None], name="dnn_feat_weights" # ) # self.dnn_feat_shape = tf.placeholder( # tf.int64, [None], name="dnn_feat_shape" # ) # def parser_one_line(self, line): # """Parse one string line into feature values. # Args: # line (str): a string indicating one instance # Returns: # list: Parsed results,including label, features and impression_id # """ # impression_id = 0 # words = line.strip().split(self.ID_spliter) # if len(words) == 2: # impression_id = words[1].strip() # cols = words[0].strip().split(self.col_spliter) # label = float(cols[0]) # features = [] # for word in cols[1:]: # if not word.strip(): # continue # tokens = word.split(":") # features.append([int(tokens[0]) - 1, int(tokens[1]) - 1, float(tokens[2])]) # return label, features, impression_id # def load_data_from_file(self, infile): # """Read and parse data from a file. # Args: # infile (str): text input file. Each line in this file is an instance. # Returns: # obj: An iterator that will yields parsed results, in the format of graph feed_dict. # """ # label_list = [] # features_list = [] # impression_id_list = [] # cnt = 0 # with tf.gfile.GFile(infile, "r") as rd: # for line in rd: # label, features, impression_id = self.parser_one_line(line) # features_list.append(features) # label_list.append(label) # impression_id_list.append(impression_id) # cnt += 1 # if cnt == self.batch_size: # res = self._convert_data(label_list, features_list) # yield self.gen_feed_dict(res), impression_id_list, self.batch_size # label_list = [] # features_list = [] # impression_id_list = [] # cnt = 0 # if cnt > 0: # res = self._convert_data(label_list, features_list) # yield self.gen_feed_dict(res), impression_id_list, cnt # def _convert_data(self, labels, features): # """Convert data into numpy arrays that are good for further operation. # Args: # labels (list): a list of ground-truth labels. # features (list): a 3-dimensional list, carrying a list (batch_size) of feature array, # where each feature array is a list of [field_idx, feature_idx, feature_value] tuple. # Returns: # dict: A dictionary, contains multiple numpy arrays that are convenient for further operation. # """ # dim = self.feature_cnt # FIELD_COUNT = self.field_cnt # instance_cnt = len(labels) # fm_feat_indices = [] # fm_feat_values = [] # fm_feat_shape = [instance_cnt, dim] # dnn_feat_indices = [] # dnn_feat_values = [] # dnn_feat_weights = [] # dnn_feat_shape = [instance_cnt * FIELD_COUNT, -1] # for i in range(instance_cnt): # m = len(features[i]) # dnn_feat_dic = {} # for j in range(m): # fm_feat_indices.append([i, features[i][j][1]]) # fm_feat_values.append(features[i][j][2]) # if features[i][j][0] not in dnn_feat_dic: # dnn_feat_dic[features[i][j][0]] = 0 # else: # dnn_feat_dic[features[i][j][0]] += 1 # dnn_feat_indices.append( # [ # i * FIELD_COUNT + features[i][j][0], # dnn_feat_dic[features[i][j][0]], # ] # ) # dnn_feat_values.append(features[i][j][1]) # dnn_feat_weights.append(features[i][j][2]) # if dnn_feat_shape[1] < dnn_feat_dic[features[i][j][0]]: # dnn_feat_shape[1] = dnn_feat_dic[features[i][j][0]] # dnn_feat_shape[1] += 1 # sorted_index = sorted( # range(len(dnn_feat_indices)), # key=lambda k: (dnn_feat_indices[k][0], dnn_feat_indices[k][1]), # ) # res = {} # res["fm_feat_indices"] = np.asarray(fm_feat_indices, dtype=np.int64) # res["fm_feat_values"] = np.asarray(fm_feat_values, dtype=np.float32) # res["fm_feat_shape"] = np.asarray(fm_feat_shape, dtype=np.int64) # res["labels"] = np.asarray([[label] for label in labels], dtype=np.float32) # res["dnn_feat_indices"] = np.asarray(dnn_feat_indices, dtype=np.int64)[ # sorted_index # ] # res["dnn_feat_values"] = np.asarray(dnn_feat_values, dtype=np.int64)[ # sorted_index # ] # res["dnn_feat_weights"] = np.asarray(dnn_feat_weights, dtype=np.float32)[ # sorted_index # ] # res["dnn_feat_shape"] = np.asarray(dnn_feat_shape, dtype=np.int64) # return res # def gen_feed_dict(self, data_dict): # """Construct a dictionary that maps graph elements to values. # Args: # data_dict (dict): a dictionary that maps string name to numpy arrays. # Returns: # dict: a dictionary that maps graph elements to numpy arrays. # """ # feed_dict = { # self.labels: data_dict["labels"], # self.fm_feat_indices: data_dict["fm_feat_indices"], # self.fm_feat_values: data_dict["fm_feat_values"], # self.fm_feat_shape: data_dict["fm_feat_shape"], # self.dnn_feat_indices: data_dict["dnn_feat_indices"], # self.dnn_feat_values: data_dict["dnn_feat_values"], # self.dnn_feat_weights: data_dict["dnn_feat_weights"], # self.dnn_feat_shape: data_dict["dnn_feat_shape"], # } # return feed_dict

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0

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null

0

1

0

1

0

3

86f2a90daa96d1b00a32e75d50fd040ca01ed705

172

py

Python

pyconde/context_processors.py

EuroPython/djep

afcccbdda483e5f6962ac97f0dc4c4c5ea67fd21

[ "BSD-3-Clause" ]

5

2015-01-02T14:33:14.000Z

2021-08-03T10:19:07.000Z

pyconde/context_processors.py

EuroPython/djep

afcccbdda483e5f6962ac97f0dc4c4c5ea67fd21

[ "BSD-3-Clause" ]

null

pyconde/context_processors.py

EuroPython/djep

afcccbdda483e5f6962ac97f0dc4c4c5ea67fd21

[ "BSD-3-Clause" ]

3

2015-08-30T09:45:03.000Z

2017-04-08T12:15:22.000Z

from django.conf import settings def less_settings(request): return { 'use_dynamic_less_in_debug': getattr(settings, 'LESS_USE_DYNAMIC_IN_DEBUG', True) }

21.5

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0.732558

23

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0.652174

0.17094

0

0.180233

172

7

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null

0

1

0

null

0

1

0

3

8109517cd2448992084aac4cf51be9ed93b5e56f

467

py

Python

greendoge/types/condition_with_args.py

grayfallstown/greendoge-blockchain

31e325913374d694dc0859140d006a642e7f95ac

[ "Apache-2.0" ]

44

2021-07-06T10:09:06.000Z

2022-02-09T04:30:14.000Z

greendoge/types/condition_with_args.py

grayfallstown/greendoge-blockchain

31e325913374d694dc0859140d006a642e7f95ac

[ "Apache-2.0" ]

67

2021-07-06T11:57:18.000Z

2022-02-02T16:14:15.000Z

greendoge/types/condition_with_args.py

grayfallstown/greendoge-blockchain

31e325913374d694dc0859140d006a642e7f95ac

[ "Apache-2.0" ]

16

2021-07-06T10:36:37.000Z

2022-03-15T08:35:16.000Z

from dataclasses import dataclass from typing import List from greendoge.types.condition_opcodes import ConditionOpcode from greendoge.util.streamable import Streamable, streamable @dataclass(frozen=True) @streamable class ConditionWithArgs(Streamable): """ This structure is used to store parsed CLVM conditions Conditions in CLVM have either format of (opcode, var1) or (opcode, var1, var2) """ opcode: ConditionOpcode vars: List[bytes]

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0.660714

0.072423

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467

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0.912821

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0

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true

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0.444444

0

0.777778

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null

0

null

0

1

0

1

0

1

0

3

810dcd1a1c119a6c004be66c020243fbafedf1ee

5,229

py

Python

boto3_type_annotations/boto3_type_annotations/guardduty/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

119

2018-12-01T18:20:57.000Z

2022-02-02T10:31:29.000Z

boto3_type_annotations/boto3_type_annotations/guardduty/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

15

2018-11-16T00:16:44.000Z

2021-11-13T03:44:18.000Z

boto3_type_annotations/boto3_type_annotations/guardduty/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

11

2019-05-06T05:26:51.000Z

2021-09-28T15:27:59.000Z

from typing import Optional from botocore.client import BaseClient from typing import Dict from typing import Union from botocore.paginate import Paginator from botocore.waiter import Waiter from typing import List class Client(BaseClient): def accept_invitation(self, DetectorId: str, InvitationId: str, MasterId: str) -> Dict: pass def archive_findings(self, DetectorId: str, FindingIds: List) -> Dict: pass def can_paginate(self, operation_name: str = None): pass def create_detector(self, Enable: bool, ClientToken: str = None, FindingPublishingFrequency: str = None) -> Dict: pass def create_filter(self, DetectorId: str, FindingCriteria: Dict, Name: str, Action: str = None, ClientToken: str = None, Description: str = None, Rank: int = None) -> Dict: pass def create_ip_set(self, Activate: bool, DetectorId: str, Format: str, Location: str, Name: str, ClientToken: str = None) -> Dict: pass def create_members(self, AccountDetails: List, DetectorId: str) -> Dict: pass def create_sample_findings(self, DetectorId: str, FindingTypes: List = None) -> Dict: pass def create_threat_intel_set(self, Activate: bool, DetectorId: str, Format: str, Location: str, Name: str, ClientToken: str = None) -> Dict: pass def decline_invitations(self, AccountIds: List) -> Dict: pass def delete_detector(self, DetectorId: str) -> Dict: pass def delete_filter(self, DetectorId: str, FilterName: str) -> Dict: pass def delete_invitations(self, AccountIds: List) -> Dict: pass def delete_ip_set(self, DetectorId: str, IpSetId: str) -> Dict: pass def delete_members(self, AccountIds: List, DetectorId: str) -> Dict: pass def delete_threat_intel_set(self, DetectorId: str, ThreatIntelSetId: str) -> Dict: pass def disassociate_from_master_account(self, DetectorId: str) -> Dict: pass def disassociate_members(self, AccountIds: List, DetectorId: str) -> Dict: pass def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None): pass def get_detector(self, DetectorId: str) -> Dict: pass def get_filter(self, DetectorId: str, FilterName: str) -> Dict: pass def get_findings(self, DetectorId: str, FindingIds: List, SortCriteria: Dict = None) -> Dict: pass def get_findings_statistics(self, DetectorId: str, FindingStatisticTypes: List, FindingCriteria: Dict = None) -> Dict: pass def get_invitations_count(self) -> Dict: pass def get_ip_set(self, DetectorId: str, IpSetId: str) -> Dict: pass def get_master_account(self, DetectorId: str) -> Dict: pass def get_members(self, AccountIds: List, DetectorId: str) -> Dict: pass def get_paginator(self, operation_name: str = None) -> Paginator: pass def get_threat_intel_set(self, DetectorId: str, ThreatIntelSetId: str) -> Dict: pass def get_waiter(self, waiter_name: str = None) -> Waiter: pass def invite_members(self, AccountIds: List, DetectorId: str, DisableEmailNotification: bool = None, Message: str = None) -> Dict: pass def list_detectors(self, MaxResults: int = None, NextToken: str = None) -> Dict: pass def list_filters(self, DetectorId: str, MaxResults: int = None, NextToken: str = None) -> Dict: pass def list_findings(self, DetectorId: str, FindingCriteria: Dict = None, MaxResults: int = None, NextToken: str = None, SortCriteria: Dict = None) -> Dict: pass def list_invitations(self, MaxResults: int = None, NextToken: str = None) -> Dict: pass def list_ip_sets(self, DetectorId: str, MaxResults: int = None, NextToken: str = None) -> Dict: pass def list_members(self, DetectorId: str, MaxResults: int = None, NextToken: str = None, OnlyAssociated: str = None) -> Dict: pass def list_threat_intel_sets(self, DetectorId: str, MaxResults: int = None, NextToken: str = None) -> Dict: pass def start_monitoring_members(self, AccountIds: List, DetectorId: str) -> Dict: pass def stop_monitoring_members(self, AccountIds: List, DetectorId: str) -> Dict: pass def unarchive_findings(self, DetectorId: str, FindingIds: List) -> Dict: pass def update_detector(self, DetectorId: str, Enable: bool = None, FindingPublishingFrequency: str = None) -> Dict: pass def update_filter(self, DetectorId: str, FilterName: str, Action: str = None, Description: str = None, FindingCriteria: Dict = None, Rank: int = None) -> Dict: pass def update_findings_feedback(self, DetectorId: str, Feedback: str, FindingIds: List, Comments: str = None) -> Dict: pass def update_ip_set(self, DetectorId: str, IpSetId: str, Activate: bool = None, Location: str = None, Name: str = None) -> Dict: pass def update_threat_intel_set(self, DetectorId: str, ThreatIntelSetId: str, Activate: bool = None, Location: str = None, Name: str = None) -> Dict: pass

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0

null

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1

0

1

0

1

0

3

810f24ca6e713fb7958aa28861ebd60291bab8c3

2,089

bzl

Python

google/cloud/google_cloud_cpp_common_unit_tests.bzl

joezqren/google-cloud-cpp

325d312b0a21569f3c57515aec7d91f3540d3b48

[ "Apache-2.0" ]

null

google/cloud/google_cloud_cpp_common_unit_tests.bzl

joezqren/google-cloud-cpp

325d312b0a21569f3c57515aec7d91f3540d3b48

[ "Apache-2.0" ]

null

google/cloud/google_cloud_cpp_common_unit_tests.bzl

joezqren/google-cloud-cpp

325d312b0a21569f3c57515aec7d91f3540d3b48

[ "Apache-2.0" ]

null

# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # DO NOT EDIT -- GENERATED BY CMake -- Change the CMakeLists.txt file if needed """Automatically generated unit tests list - DO NOT EDIT.""" google_cloud_cpp_common_unit_tests = [ "common_options_test.cc", "future_generic_test.cc", "future_generic_then_test.cc", "future_void_test.cc", "future_void_then_test.cc", "iam_bindings_test.cc", "internal/algorithm_test.cc", "internal/api_client_header_test.cc", "internal/backoff_policy_test.cc", "internal/base64_transforms_test.cc", "internal/big_endian_test.cc", "internal/compiler_info_test.cc", "internal/credentials_impl_test.cc", "internal/env_test.cc", "internal/filesystem_test.cc", "internal/format_time_point_test.cc", "internal/future_impl_test.cc", "internal/invoke_result_test.cc", "internal/log_impl_test.cc", "internal/pagination_range_test.cc", "internal/parse_rfc3339_test.cc", "internal/random_test.cc", "internal/retry_policy_test.cc", "internal/status_payload_keys_test.cc", "internal/strerror_test.cc", "internal/throw_delegate_test.cc", "internal/tuple_test.cc", "internal/type_list_test.cc", "internal/user_agent_prefix_test.cc", "internal/utility_test.cc", "kms_key_name_test.cc", "log_test.cc", "options_test.cc", "polling_policy_test.cc", "project_test.cc", "status_or_test.cc", "status_test.cc", "stream_range_test.cc", "terminate_handler_test.cc", "tracing_options_test.cc", ]

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811134f08b2c67534a9093ee9d1a20f045af6b48

865

py

Python

socialdistribution/app/templatetags/filters.py

CMPUT404-Project-Group/CMPUT404-Group-Project

e541cc609f260d7221fe0be8975c5b2444d74af0

[ "W3C-20150513" ]

null

socialdistribution/app/templatetags/filters.py

CMPUT404-Project-Group/CMPUT404-Group-Project

e541cc609f260d7221fe0be8975c5b2444d74af0

[ "W3C-20150513" ]

44

2021-10-14T15:44:46.000Z

2021-12-05T00:57:23.000Z

socialdistribution/app/templatetags/filters.py

CMPUT404-Project-Group/Social-Distribution-CMPUT404-Group-Project

e541cc609f260d7221fe0be8975c5b2444d74af0

[ "W3C-20150513" ]

1

2021-12-07T01:14:14.000Z

from django import template from django.template.defaultfilters import stringfilter from django.utils.safestring import SafeString import markdown import urllib register = template.Library() @register.filter def strip_space(value): return value.replace(' ', '') @register.filter @stringfilter def commonmark(value): return markdown.Markdown().convert(value) @register.filter(name="getID") def get_ID(value): if not type(value) is str: return value return value.split('/')[-1] @register.filter(name="getNav") def get_nav(value): return value.split('/')[-2] @register.filter(name="encode_url") def encode_url(value): return urllib.parse.quote(value) @register.filter def get_post_id(url): """ gets the post id from the comment page url """ return urllib.parse.urlparse(url.get_full_path()).path.rsplit('/', 1)[0]

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865

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811e73ee0c3fc584081650f0224040703f26ea00

386

py

Python

tabular/__init__.py

yamins81/tabular

1caf091c8c395960a9ad7078f95158b533cc52dd

[ "MIT" ]

6

2015-05-24T20:59:31.000Z

2021-05-31T14:34:18.000Z

tabular/__init__.py

yamins81/tabular

1caf091c8c395960a9ad7078f95158b533cc52dd

[ "MIT" ]

3

2016-06-17T20:02:27.000Z

2020-02-13T19:20:40.000Z

tabular/__init__.py

yamins81/tabular

1caf091c8c395960a9ad7078f95158b533cc52dd

[ "MIT" ]

8

2015-08-22T17:09:40.000Z

2022-02-10T14:47:40.000Z

import io import fast import spreadsheet import tab import utils import web from io import * from fast import * from spreadsheet import * from tab import * from utils import * from web import * __all__ = [] __all__.extend(io.__all__) __all__.extend(fast.__all__) __all__.extend(spreadsheet.__all__) __all__.extend(tab.__all__) __all__.extend(utils.__all__) __all__.extend(web.__all__)

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3

812594dced1920626bd6e5484a03e5c3aa5dda9e

1,943

py

Python

ServerSide/models.py

Coullence/DRF_Percels-Couriers_API_V.0.0.2

906786115861b316f8ecf023c8af82f2dacff68e

[ "MIT" ]

null

ServerSide/models.py

Coullence/DRF_Percels-Couriers_API_V.0.0.2

906786115861b316f8ecf023c8af82f2dacff68e

[ "MIT" ]

null

ServerSide/models.py

Coullence/DRF_Percels-Couriers_API_V.0.0.2

906786115861b316f8ecf023c8af82f2dacff68e

[ "MIT" ]

null

from django.db import models # Create your models here. # Station class Stations(models.Model): stationName = models.CharField(max_length=100) stationLocation = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.stationName # Customers class Customers(models.Model): customerName = models.CharField(max_length=100) customerPhone = models.CharField(max_length=100) customerId = models.CharField(max_length=100) customerStartLoc = models.CharField(max_length=100) customerDestinationLoc = models.CharField(max_length=100) stationStaffId = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str_(self): return self.customerName # Items class Items(models.Model): itemName = models.CharField(max_length=100) itemType = models.CharField(max_length=100) Quantity = models.CharField(max_length=100) originStation = models.CharField(max_length=100) originCounty = models.CharField(max_length=100) receiverName = models.CharField(max_length=100) receiverPhone = models.CharField(max_length=100) destinationAddress = models.CharField(max_length=100) destinationCounty = models.CharField(max_length=100) dateSend= models.CharField(max_length=100) dateExpected = models.CharField(max_length=100) def __str__(self): return self.itemName # Payments class Payments(models.Model): customerPhone = models.CharField(max_length=100) paymentAmount = models.CharField(max_length=100) paymentMeans = models.EmailField(max_length=100) code = models.CharField(max_length=100) date = models.DateTimeField(auto_now_add=True) def __str__(self): return self.customerPhone

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