'''simple docstring'''







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_      	=	{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 _SCREAMING_SNAKE_CASE(							nn.Module					):




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       List[Any]      )     ->				str:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :int						    = torchvision.models.resnetaaa(pretrained=lowercase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = list(model.children()      )[:-2]
													SCREAMING_SNAKE_CASE__     :List[Any]						    = nn.Sequential(*lowercase_      )
													SCREAMING_SNAKE_CASE__     :str						    = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       Optional[Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.pool(self.model(lowercase_      )      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.flatten(lowercase_							,							start_dim=2      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = out.transpose(1							,							2      ).contiguous()
													return out  # BxNx2048




class _SCREAMING_SNAKE_CASE(							__a					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       List[Any]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = [json.loads(lowercase_      ) for l in open(lowercase_      )]
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = os.path.dirname(lowercase_      )
													SCREAMING_SNAKE_CASE__     :str						    = tokenizer
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = labels
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = len(lowercase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = max_seq_length

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = transforms




						def __len__(    self :       Union[str, Any]      )     ->				List[str]:
													return len(self.data      )




						def __getitem__(    self :       Tuple							,							UpperCamelCase_ :       List[str]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Dict						    = torch.LongTensor(self.tokenizer.encode(self.data[index]['text']							,							add_special_tokens=lowercase_      )      )
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[str]						    = sentence[0], sentence[1:-1], sentence[-1]
													SCREAMING_SNAKE_CASE__     :Tuple						    = sentence[: self.max_seq_length]

													SCREAMING_SNAKE_CASE__     :Any						    = torch.zeros(self.n_classes      )
													SCREAMING_SNAKE_CASE__     :Any						    = 1

													SCREAMING_SNAKE_CASE__     :List[str]						    = Image.open(os.path.join(self.data_dir							,							self.data[index]['img']      )      ).convert('RGB'      )
													SCREAMING_SNAKE_CASE__     :int						    = self.transforms(lowercase_      )

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




						def      __lowerCamelCase       (    self :       Dict      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = Counter()
													for row in self.data:
																				label_freqs.update(row['label']      )
													return label_freqs


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[int]		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = [len(row['sentence']		) for row in batch]
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = len(SCREAMING_SNAKE_CASE_		), max(SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :Dict						    = torch.zeros(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						dtype=torch.long		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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_		)		):
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = input_row['sentence']
														SCREAMING_SNAKE_CASE__     :Tuple						    = 1

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.stack([row['image'] for row in batch]		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.stack([row['label'] for row in batch]		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.stack([row['image_start_token'] for row in batch]		)
							SCREAMING_SNAKE_CASE__     :List[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       (			)			->	Dict:


							'''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    lowerCamelCase       (			)			->	Tuple:


							'''simple docstring'''






							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6		),
							        transforms.CenterCrop(2_2_4		),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]				,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]				,						),
							    ]		)







'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if num < 0:
    return False

  lowercase_     =       num
  lowercase_     =       0
  while num > 0:
    lowercase_     =       rev_num * 10 + (num % 10)
    num //= 10

  return num_copy == rev_num


if __name__ == "__main__":
       import doctest

       doctest.testmod()

