

"""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_	:				Optional[Any]										= {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						):


      def __init__(    self				:	Tuple							,   a				:	Any						):





            """simple docstring"""







            super().__init__()
            __snake_case      :    Optional[Any]  			=torchvision.models.resnetaaa(pretrained=__A						)
            __snake_case      :    str  			=list(model.children()						)[:-2]
            __snake_case      :    Dict  			=nn.Sequential(*__A						)
            __snake_case      :    Union[str, Any]  			=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)







      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	str						):





            """simple docstring"""







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



class      _lowercase (     __lowercase						):


      def __init__(    self				:	Dict							,   a				:	Optional[Any]							,   a				:	List[str]							,   a				:	Any							,   a				:	Any							,   a				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=[json.loads(__A						) for l in open(__A						)]
            __snake_case      :    str  			=os.path.dirname(__A						)
            __snake_case      :    str  			=tokenizer
            __snake_case      :    List[str]  			=labels
            __snake_case      :    Optional[Any]  			=len(__A						)
            __snake_case      :    Any  			=max_seq_length

            __snake_case      :    Optional[Any]  			=transforms


      def __len__(    self				:	Tuple						):





            """simple docstring"""







            return len(self.data						)


      def __getitem__(    self				:	Tuple							,   a				:	Dict						):





            """simple docstring"""







            __snake_case      :    Tuple  			=torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']							,   add_special_tokens=__A						)						)
            __snake_case			,				__snake_case			,				__snake_case      :    Union[str, Any]  			=sentence[0], sentence[1:-1], sentence[-1]
            __snake_case      :    Dict  			=sentence[: self.max_seq_length]

            __snake_case      :    List[str]  			=torch.zeros(self.n_classes						)
            __snake_case      :    List[Any]  			=1

            __snake_case      :    int  			=Image.open(os.path.join(self.data_dir							,   self.data[index]['''img''']						)						).convert('''RGB'''						)
            __snake_case      :    int  			=self.transforms(__A						)

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







      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







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



def     __lowercase					(    a :				int )					->					List[Any]:
      __snake_case      :    List[Any]  			=[len(row['''sentence'''] ) for row in batch]
      __snake_case			,				__snake_case      :    str  			=len(a__ ), max(a__ )

      __snake_case      :    Optional[int]  			=torch.zeros(a__       ,		a__       ,		dtype=torch.long )
      __snake_case      :    Union[str, Any]  			=torch.zeros(a__       ,		a__       ,		dtype=torch.long )

      for i_batch, (input_row, length) in enumerate(zip(a__       ,		a__ ) ):
            __snake_case      :    Tuple  			=input_row['''sentence''']
            __snake_case      :    Dict  			=1

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










"""simple docstring"""





def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return x if y == 0 else greatest_common_divisor(a       ,		x % y )



def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return (x * y) // greatest_common_divisor(a       ,		a )



def     __lowercase					(    a :				int = 20 )					->					int:
      __snake_case      :    Optional[Any]  			=1
      for i in range(1       ,		n + 1 ):
            __snake_case      :    Optional[int]  			=lcm(a       ,		a )
      return g


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


