

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


_SCREAMING_SNAKE_CASE					:  Union[str, 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 	UpperCAmelCase__		(	nn.Module	):






  """simple docstring"""


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







  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		str )		->							List[Any]:
       # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
       SCREAMING_SNAKE_CASE__   =   self.pool(self.model(__lowerCamelCase ) )
       SCREAMING_SNAKE_CASE__   =   torch.flatten(__lowerCamelCase     ,       start_dim=2 )
       SCREAMING_SNAKE_CASE__   =   out.transpose(1     ,       2 ).contiguous()
       return out  # BxNx2048






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


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

       SCREAMING_SNAKE_CASE__   =   transforms






  def __len__(							self						:		Dict )		->							str:
       return len(self.data )






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

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

       SCREAMING_SNAKE_CASE__   =   Image.open(os.path.join(self.data_dir     ,       self.data[index]['''img'''] ) ).convert('''RGB''' )
       SCREAMING_SNAKE_CASE__   =   self.transforms(__lowerCamelCase )

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







  def 				lowercase_		(							self						:		Any )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   Counter()
       for row in self.data:
            label_freqs.update(row['''label'''] )
       return label_freqs



def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [len(row['''sentence''']    ) for row in batch]
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   len(_A    ), max(_A    )

     SCREAMING_SNAKE_CASE__   =   torch.zeros(_A				,		_A				,		dtype=torch.long    )
     SCREAMING_SNAKE_CASE__   =   torch.zeros(_A				,		_A				,		dtype=torch.long    )

     for i_batch, (input_row, length) in enumerate(zip(_A				,		_A    )    ):
          SCREAMING_SNAKE_CASE__   =   input_row['''sentence''']
          SCREAMING_SNAKE_CASE__   =   1

     SCREAMING_SNAKE_CASE__   =   torch.stack([row['''image'''] for row in batch]    )
     SCREAMING_SNAKE_CASE__   =   torch.stack([row['''label'''] for row in batch]    )
     SCREAMING_SNAKE_CASE__   =   torch.stack([row['''image_start_token'''] for row in batch]    )
     SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						):





     '''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     UpperCAmelCase_       (						):





     '''simple docstring'''




     return transforms.Compose(
         [
             transforms.Resize(2_56    ),
             transforms.CenterCrop(2_24    ),
             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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''multiplicative_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   1
          for i in range(0				,		len(_A    )    ):
               total *= numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''additive_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   0
          for i in range(0				,		len(_A    )    ):
               total += numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps


if __name__ == "__main__":
       import doctest

       doctest.testmod()


