"""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


_a				:		str	   =       {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    ,					a__  ):
       super().__init__()
       _lowerCAmelCase						:	List[Any]        =					torchvision.models.resnetaaa(pretrained=a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					list(model.children()  )[:-2]
       _lowerCAmelCase						:	Any        =					nn.Sequential(*a__  )
       _lowerCAmelCase						:	Tuple        =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






     def    __A  (		self    ,					a__  ):
       # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
       _lowerCAmelCase						:	Any        =					self.pool(self.model(a__  )  )
       _lowerCAmelCase						:	List[Any]        =					torch.flatten(a__    ,					start_dim=2  )
       _lowerCAmelCase						:	Dict        =					out.transpose(1    ,					2  ).contiguous()
       return out  # BxNx2048







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[int]        =					[json.loads(a__  ) for l in open(a__  )]
       _lowerCAmelCase						:	Any        =					os.path.dirname(a__  )
       _lowerCAmelCase						:	Dict        =					tokenizer
       _lowerCAmelCase						:	Optional[Any]        =					labels
       _lowerCAmelCase						:	Optional[int]        =					len(a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					max_seq_length

       _lowerCAmelCase						:	Any        =					transforms






     def __len__(		self  ):
       return len(self.data  )






     def __getitem__(		self    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]    ,					add_special_tokens=a__  )  )
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					sentence[0], sentence[1:-1], sentence[-1]
       _lowerCAmelCase						:	Any        =					sentence[: self.max_seq_length]

       _lowerCAmelCase						:	Dict        =					torch.zeros(self.n_classes  )
       _lowerCAmelCase						:	Union[str, Any]        =					1

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

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Counter()
       for row in self.data:
         label_freqs.update(row["""label"""]  )
       return label_freqs







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					[len(row["""sentence"""]     ) for row in batch]
  _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[int]        =					len(_lowerCamelCase     ), max(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					torch.zeros(_lowerCamelCase      ,_lowerCamelCase      ,dtype=torch.long     )
  _lowerCAmelCase						:	Any        =					torch.zeros(_lowerCamelCase      ,_lowerCamelCase      ,dtype=torch.long     )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase      ,_lowerCamelCase     )     ):
    _lowerCAmelCase						:	Tuple        =					input_row["""sentence"""]
    _lowerCAmelCase						:	Optional[Any]        =					1

  _lowerCAmelCase						:	Optional[int]        =					torch.stack([row["""image"""] for row in batch]     )
  _lowerCAmelCase						:	List[Any]        =					torch.stack([row["""label"""] for row in batch]     )
  _lowerCAmelCase						:	Tuple        =					torch.stack([row["""image_start_token"""] for row in batch]     )
  _lowerCAmelCase						:	List[str]        =					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 							SCREAMING_SNAKE_CASE					(      )	->				List[Any]:
  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 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  return transforms.Compose(
      [
          transforms.Resize(256     ),
          transforms.CenterCrop(224     ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]      ,std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]      ,),
      ]     )





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int = 100     )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					n * (n + 1) * (2 * n + 1) / 6
  _lowerCAmelCase						:	Tuple        =					(n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares     )


if __name__ == "__main__":
     print(F"""{solution() = }""")




