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import numpy as np
from PIL import Image
from typing import List, Optional, Union, Dict, Any
import torch
from torchvision import transforms as T
import albumentations as A
import cv2
import json
from transformers import ProcessorMixin, BaseImageProcessor, ImageProcessingMixin
from transformers.tokenization_utils_base import BatchEncoding
from transformers.image_utils import ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format
from transformers.utils import TensorType
class NemotronParseImageProcessor(BaseImageProcessor, ImageProcessingMixin):
"""
Image processor for NemotronParse model.
This processor inherits from BaseImageProcessor to be compatible with transformers AutoImageProcessor.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
final_size: tuple = (2048, 1648),
**kwargs,
):
clean_kwargs = {}
for k, v in kwargs.items():
if not k.startswith('_') and k not in ['transform', 'torch_transform']:
clean_kwargs[k] = v
if 'size' in clean_kwargs:
size_config = clean_kwargs.pop('size')
if isinstance(size_config, dict):
if 'longest_edge' in size_config:
longest_edge = size_config['longest_edge']
if isinstance(longest_edge, (list, tuple)):
final_size = tuple(int(x) for x in longest_edge)
else:
final_size = (int(longest_edge), int(longest_edge))
elif 'height' in size_config and 'width' in size_config:
final_size = (int(size_config['height']), int(size_config['width']))
super().__init__(**clean_kwargs)
if isinstance(final_size, (list, tuple)) and len(final_size) >= 2:
self.final_size = (int(final_size[0]), int(final_size[1]))
elif isinstance(final_size, (int, float)):
self.final_size = (int(final_size), int(final_size))
else:
self.final_size = (2048, 1648) # Default fallback
self._create_transforms()
def _create_transforms(self):
"""Create transform objects (not serialized to JSON)."""
if isinstance(self.final_size, (list, tuple)):
self.target_height, self.target_width = int(self.final_size[0]), int(self.final_size[1])
else:
self.target_height = self.target_width = int(self.final_size)
self.transform = A.Compose([
A.PadIfNeeded(
min_height=self.target_height,
min_width=self.target_width,
border_mode=cv2.BORDER_CONSTANT,
value=[255, 255, 255],
p=1.0
),
])
self.torch_transform = T.Compose([
T.ToTensor(),
# Note: Normalization is done within RADIO model
])
def to_dict(self):
"""Override to exclude non-serializable transforms."""
output = super().to_dict()
output.pop('transform', None)
output.pop('torch_transform', None)
return output
@classmethod
def from_dict(cls, config_dict: dict, **kwargs):
"""Override to recreate transforms after loading."""
config_dict = config_dict.copy()
config_dict.pop('transform', None)
config_dict.pop('torch_transform', None)
# Clean any problematic entries
for key in list(config_dict.keys()):
if key.startswith('_') or config_dict[key] is None:
config_dict.pop(key, None)
# Ensure numeric types are correct
if 'final_size' in config_dict:
final_size = config_dict['final_size']
if isinstance(final_size, (list, tuple)):
config_dict['final_size'] = tuple(int(x) for x in final_size)
try:
return cls(**config_dict, **kwargs)
except Exception as e:
print(f"Warning: Error in from_dict: {e}")
print("Using default parameters...")
return cls(**kwargs)
def save_pretrained(self, save_directory, **kwargs):
"""Save image processor configuration."""
import os
import json
os.makedirs(save_directory, exist_ok=True)
# Save preprocessor config in standard HuggingFace format
config = {
"feature_extractor_type": "NemotronParseImageProcessor",
"image_processor_type": "NemotronParseImageProcessor",
"processor_class": "NemotronParseImageProcessor",
"size": {
"height": self.final_size[0],
"width": self.final_size[1],
"longest_edge": self.final_size
},
"final_size": self.final_size,
}
config_path = os.path.join(save_directory, "preprocessor_config.json")
with open(config_path, 'w') as f:
json.dump(config, f, indent=2)
def _resize_with_aspect_ratio(self, image: np.ndarray) -> np.ndarray:
"""Resize image maintaining aspect ratio (exact replica of original LongestMaxSizeHW)."""
height, width = image.shape[:2]
max_size_height = self.target_height
max_size_width = self.target_width
# Original LongestMaxSizeHW algorithm from custom_augmentations.py
aspect_ratio = width / height
new_height = height
new_width = width
if height > max_size_height:
new_height = max_size_height
new_width = int(new_height * aspect_ratio)
if new_width > max_size_width:
new_width = max_size_width
new_height = int(new_width / aspect_ratio)
return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LINEAR)
def _pad_to_size(self, image: np.ndarray) -> np.ndarray:
"""Pad image to target size with white padding (matches A.PadIfNeeded behavior)."""
h, w = image.shape[:2]
min_height, min_width = self.target_height, self.target_width
pad_h = max(0, min_height - h)
pad_w = max(0, min_width - w)
if pad_h == 0 and pad_w == 0:
return image
if len(image.shape) == 3:
padded = np.pad(
image,
((0, pad_h), (0, pad_w), (0, 0)),
mode='constant',
constant_values=255
)
else:
padded = np.pad(
image,
((0, pad_h), (0, pad_w)),
mode='constant',
constant_values=255
)
return padded
def preprocess(
self,
images: ImageInput,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""
Preprocess an image or batch of images for the NemotronParse model.
Args:
images: Input image(s)
return_tensors: Type of tensors to return
"""
# Ensure images is a list
if not isinstance(images, list):
images = [images]
# Convert PIL images to numpy arrays if needed
processed_images = []
for image in images:
if isinstance(image, Image.Image):
image = np.asarray(image)
processed_images.append(image)
# Apply NemotronParse-specific transforms
pixel_values = []
for image in processed_images:
processed_image = self._resize_with_aspect_ratio(image)
if self.transform is not None:
transformed = self.transform(image=processed_image)
processed_image = transformed["image"]
else:
# Fallback: just pad to target size
processed_image = self._pad_to_size(processed_image)
pixel_values_tensor = self.torch_transform(processed_image)
if pixel_values_tensor.shape[0] == 1:
pixel_values_tensor = pixel_values_tensor.expand(3, -1, -1)
pixel_values.append(pixel_values_tensor)
pixel_values = torch.stack(pixel_values)
data = {"pixel_values": pixel_values}
if return_tensors is not None:
data = self._convert_output_format(data, return_tensors)
return data
def _convert_output_format(self, data: Dict[str, torch.Tensor], return_tensors: Union[str, TensorType]) -> Dict:
"""Convert output format based on return_tensors parameter."""
if return_tensors == "pt" or return_tensors == TensorType.PYTORCH:
return data
elif return_tensors == "np" or return_tensors == TensorType.NUMPY:
return {k: v.numpy() for k, v in data.items()}
else:
return data
def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> Dict[str, torch.Tensor]:
"""Process images for the model (backward compatibility)."""
return self.preprocess(images, **kwargs)
class NemotronParseProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = ("PreTrainedTokenizer", "PreTrainedTokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
if image_processor is None:
image_processor = NemotronParseImageProcessor(**kwargs)
super().__init__(image_processor, tokenizer)
def __call__(
self,
images: Union[Image.Image, List[Image.Image]] = None,
text: Union[str, List[str]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_token_type_ids: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, "TensorType"]] = None,
**kwargs
) -> BatchEncoding:
"""
Main method to prepare for the model one or several text(s) and image(s).
"""
# Process images
if images is not None:
image_inputs = self.image_processor(images, **kwargs)
else:
image_inputs = {}
# Process text
if text is not None:
text_inputs = self.tokenizer(
text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
else:
text_inputs = {}
# Combine inputs
return BatchEncoding({**image_inputs, **text_inputs})
def decode(self, *args, **kwargs):
"""Decode token ids to strings."""
return self.tokenizer.decode(*args, **kwargs)
def batch_decode(self, *args, **kwargs):
"""Batch decode token ids to strings."""
return self.tokenizer.batch_decode(*args, **kwargs)
def post_process_generation(self, sequences, fix_markdown=False):
"""Post-process generated sequences."""
if hasattr(self.tokenizer, 'post_process_generation'):
return self.tokenizer.post_process_generation(sequences, fix_markdown=fix_markdown)
else:
# Fallback processing
if isinstance(sequences, str):
sequences = [sequences]
processed = []
for seq in sequences:
# Basic cleaning
seq = seq.replace('<s>', '').replace('</s>', '').strip()
processed.append(seq)
return processed[0] if len(processed) == 1 else processed
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
"""
Load processor from pretrained model.
This method is compatible with AutoProcessor.from_pretrained().
"""
# Explicitly load subcomponents via Auto* to ensure remote auto_map is honored.
from transformers import AutoImageProcessor, AutoTokenizer
trust_remote_code = kwargs.get("trust_remote_code", None)
revision = kwargs.get("revision", None)
token = kwargs.get("token", None)
image_processor = AutoImageProcessor.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
revision=revision,
token=token,
)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
revision=revision,
token=token,
)
return cls(image_processor=image_processor, tokenizer=tokenizer)
def save_pretrained(self, save_directory, **kwargs):
"""
Save processor to directory.
This method is compatible with AutoProcessor/AutoImageProcessor loading.
"""
import os
os.makedirs(save_directory, exist_ok=True)
# Save tokenizer with proper configuration for AutoTokenizer
print("Saving tokenizer for AutoTokenizer compatibility...")
self.tokenizer.save_pretrained(save_directory, **kwargs)
# Save image processor
print("Saving image processor...")
self.image_processor.save_pretrained(save_directory, **kwargs)
# Use the parent class's save_pretrained method for processor config
super().save_pretrained(save_directory, **kwargs)
print(f"NemotronParseProcessor saved to {save_directory}")
print(f"AutoTokenizer.from_pretrained('{save_directory}') should now work!")
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