



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		int		=		['image_processor', 'tokenizer']
						UpperCAmelCase			:		str		=		'LayoutLMv3ImageProcessor'
						UpperCAmelCase			:		Optional[int]		=		('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self    :				str       ,	snake_case    :				int=None       ,	snake_case    :				List[Any]=None       ,	**snake_case    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'       ,	snake_case       ,	)
																SCREAMING_SNAKE_CASE							:     Tuple						=				kwargs.pop('feature_extractor'	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError('You need to specify an `image_processor`.'	)
											if tokenizer is None:
																raise ValueError('You need to specify a `tokenizer`.'	)

											super().__init__(snake_case       ,	snake_case	)







						def __call__(  self    :				int       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,	snake_case    :				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,	snake_case    :				Union[List[List[int]], List[List[List[int]]]] = None       ,	snake_case    :				Optional[Union[List[int], List[List[int]]]] = None       ,	snake_case    :				bool = True       ,	snake_case    :				Union[bool, str, PaddingStrategy] = False       ,	snake_case    :				Union[bool, str, TruncationStrategy] = None       ,	snake_case    :				Optional[int] = None       ,	snake_case    :				int = 0       ,	snake_case    :				Optional[int] = None       ,	snake_case    :				Optional[bool] = None       ,	snake_case    :				Optional[bool] = None       ,	snake_case    :				bool = False       ,	snake_case    :				bool = False       ,	snake_case    :				bool = False       ,	snake_case    :				bool = False       ,	snake_case    :				bool = True       ,	snake_case    :				Optional[Union[str, TensorType]] = None       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


											if self.image_processor.apply_ocr and (boxes is not None):
																raise ValueError(
																    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'	)

											if self.image_processor.apply_ocr and (word_labels is not None):
																raise ValueError(
																    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'	)

											# first, apply the image processor
											SCREAMING_SNAKE_CASE							:     str						=				self.image_processor(images=snake_case       ,	return_tensors=snake_case	)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																if isinstance(snake_case       ,	snake_case	):
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
																SCREAMING_SNAKE_CASE							:     Any						=				features['words']

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.tokenizer(
											    text=text if text is not None else features['words']       ,	text_pair=text_pair if text_pair is not None else None       ,	boxes=boxes if boxes is not None else features['boxes']       ,	word_labels=snake_case       ,	add_special_tokens=snake_case       ,	padding=snake_case       ,	truncation=snake_case       ,	max_length=snake_case       ,	stride=snake_case       ,	pad_to_multiple_of=snake_case       ,	return_token_type_ids=snake_case       ,	return_attention_mask=snake_case       ,	return_overflowing_tokens=snake_case       ,	return_special_tokens_mask=snake_case       ,	return_offsets_mapping=snake_case       ,	return_length=snake_case       ,	verbose=snake_case       ,	return_tensors=snake_case       ,	**snake_case       ,	)

											# add pixel values
											SCREAMING_SNAKE_CASE							:     Tuple						=				features.pop('pixel_values'	)
											if return_overflowing_tokens is True:
																SCREAMING_SNAKE_CASE							:     int						=				self.get_overflowing_images(snake_case       ,	encoded_inputs['overflow_to_sample_mapping']	)
											SCREAMING_SNAKE_CASE							:     Dict						=				images

											return encoded_inputs







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				List[str]       ,	snake_case    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[]
											for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]	)

											if len(snake_case	) != len(snake_case	):
																raise ValueError(
																    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
																    f''' {len(snake_case	)} and {len(snake_case	)}'''	)

											return images_with_overflow







						def       lowerCamelCase_				(  self    :				str       ,	*snake_case    :				List[Any]       ,	**snake_case    :				Optional[Any]	):



											'''simple docstring'''


											return self.tokenizer.batch_decode(*snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]       ,	*snake_case    :				List[Any]       ,	**snake_case    :				Tuple	):



											'''simple docstring'''


											return self.tokenizer.decode(*snake_case       ,	**snake_case	)







						@property
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,	snake_case       ,	)
											return self.image_processor_class







						@property
						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,	snake_case       ,	)
											return self.image_processor




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase    :		Dict		      =    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(os.path.join(self.data_dir       ,	self.data[index]['img']	)	).convert('RGB'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.transforms(snake_case	)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Any						=				torch.stack([row['image_end_token'] for row in batch]   )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        __a			(   )     ->     str:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]



def        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )