




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


UpperCAmelCase_		:					Optional[int]          =  {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 		__A   (      nn.Module      ):

						def __init__(		self :List[str]     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Union[str, Any]										=torchvision.models.resnetaaa(pretrained=__lowerCamelCase			)
										__magic_name__						:						Union[str, Any]										=list(model.children()			)[:-2]
										__magic_name__						:						int										=nn.Sequential(*__lowerCamelCase			)
										__magic_name__						:						Tuple										=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]			)


						def      A__    (		self :List[Any]     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.pool(self.model(__lowerCamelCase			)			)
										__magic_name__						:						Union[str, Any]										=torch.flatten(__lowerCamelCase     ,			start_dim=2			)
										__magic_name__						:						Any										=out.transpose(1     ,			2			).contiguous()
										return out  # BxNx2048




class 		__A   (      UpperCAmelCase_      ):

						def __init__(		self :Tuple     ,			__snake_case :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=[json.loads(__lowerCamelCase			) for l in open(__lowerCamelCase			)]
										__magic_name__						:						str										=os.path.dirname(__lowerCamelCase			)
										__magic_name__						:						List[Any]										=tokenizer
										__magic_name__						:						Tuple										=labels
										__magic_name__						:						Union[str, Any]										=len(__lowerCamelCase			)
										__magic_name__						:						Tuple										=max_seq_length

										__magic_name__						:						Dict										=transforms

						def __len__(		self :Any			):
										'''simple docstring'''

										return len(self.data			)

						def __getitem__(		self :List[Any]     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

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

										__magic_name__						:						Optional[int]										=torch.zeros(self.n_classes			)
										__magic_name__						:						str										=1

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

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


						def      A__    (		self :str			):
										'''simple docstring'''

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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Dict										=[len(row["""sentence"""] ) for row in batch]
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=len(lowerCamelCase ), max(lowerCamelCase )

				__magic_name__						:						Optional[Any]										=torch.zeros(lowerCamelCase						,  lowerCamelCase						,  dtype=torch.long )
				__magic_name__						:						Tuple										=torch.zeros(lowerCamelCase						,  lowerCamelCase						,  dtype=torch.long )

				for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase						,  lowerCamelCase ) ):
								__magic_name__						:						str										=input_row["""sentence"""]
								__magic_name__						:						Optional[Any]										=1

				__magic_name__						:						str										=torch.stack([row["""image"""] for row in batch] )
				__magic_name__						:						Optional[int]										=torch.stack([row["""label"""] for row in batch] )
				__magic_name__						:						Any										=torch.stack([row["""image_start_token"""] for row in batch] )
				__magic_name__						:						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 		lowerCAmelCase_ (						):
				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 		lowerCAmelCase_ (						):
				return transforms.Compose(
				    [
				        transforms.Resize(256 ),
				        transforms.CenterCrop(224 ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]						,  std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]						,  ),
				    ] )











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




