


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


__snake_case            =   {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    UpperCAmelCase					(       nn.Module    ):




			def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







						super().__init__()
						UpperCamelCase         =					torchvision.models.resnetaaa(pretrained=__magic_name__						)
						UpperCamelCase         =					list(model.children()						)[:-2]
						UpperCamelCase         =					nn.Sequential(*__magic_name__						)
						UpperCamelCase         =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)





			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.pool(self.model(__magic_name__						)						)
						UpperCamelCase         =					torch.flatten(__magic_name__       ,       start_dim=2						)
						UpperCamelCase         =					out.transpose(1       ,       2						).contiguous()
						return out  # BxNx2048




class    UpperCAmelCase					(       __snake_case    ):




			def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					[json.loads(__magic_name__						) for l in open(__magic_name__						)]
						UpperCamelCase         =					os.path.dirname(__magic_name__						)
						UpperCamelCase         =					tokenizer
						UpperCamelCase         =					labels
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					max_seq_length

						UpperCamelCase         =					transforms




			def __len__(						self							:		Optional[int]						):







						"""simple docstring"""







						return len(self.data						)




			def __getitem__(						self							:		List[str]       ,       __magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]       ,       add_special_tokens=__magic_name__						)						)
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					sentence[0], sentence[1:-1], sentence[-1]
						UpperCamelCase         =					sentence[: self.max_seq_length]

						UpperCamelCase         =					torch.zeros(self.n_classes						)
						UpperCamelCase         =					1

						UpperCamelCase         =					Image.open(os.path.join(self.data_dir       ,       self.data[index]["""img"""]						)						).convert("""RGB"""						)
						UpperCamelCase         =					self.transforms(__magic_name__						)

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





			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					Counter()
						for row in self.data:
									label_freqs.update(row["""label"""]						)
						return label_freqs






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					[len(row["""sentence"""]	) for row in batch]
			UpperCamelCase  ,  UpperCamelCase         =					len(SCREAMING_SNAKE_CASE_	), max(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					torch.zeros(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  dtype=torch.long	)
			UpperCamelCase         =					torch.zeros(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  dtype=torch.long	)

			for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					input_row["""sentence"""]
						UpperCamelCase         =					1

			UpperCamelCase         =					torch.stack([row["""image"""] for row in batch]	)
			UpperCamelCase         =					torch.stack([row["""label"""] for row in batch]	)
			UpperCamelCase         =					torch.stack([row["""image_start_token"""] for row in batch]	)
			UpperCamelCase         =					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 							_lowercase    (		):





			"""simple docstring"""




			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 							_lowercase    (		):





			"""simple docstring"""




			return transforms.Compose(
			    [
			        transforms.Resize(256	),
			        transforms.CenterCrop(224	),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,  std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,  ),
			    ]	)








import warnings
from typing import List, Optional, Union

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




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					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__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




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







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







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




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







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




