


from __future__ import annotations

from dataclasses import dataclass






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		float
    UpperCAmelCase__						:		TreeNode | None              =  None
    UpperCAmelCase__						:		TreeNode | None              =  None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : TreeNode | None    ):

     # Validation
     def is_valid_tree(SCREAMING_SNAKE_CASE__      : TreeNode | None    ) -> bool:
          if node is None:
               return True

          if not isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               return False

          try:
               float(node.data    )
          except (TypeError, ValueError):
               return False

          return is_valid_tree(node.left    ) and is_valid_tree(node.right    )

     if not is_valid_tree(SCREAMING_SNAKE_CASE__    ):
          raise ValueError(
              'Each node should be type of TreeNode and data should be float.'    )

     def is_binary_search_tree_recursive_check(
         SCREAMING_SNAKE_CASE__      : TreeNode | None      ,					SCREAMING_SNAKE_CASE__      : float      ,					SCREAMING_SNAKE_CASE__      : float    ) -> bool:

          if node is None:
               return True

          return (
              left_bound < node.data < right_bound
              and is_binary_search_tree_recursive_check(node.left      ,					SCREAMING_SNAKE_CASE__      ,					node.data    )
              and is_binary_search_tree_recursive_check(
                  node.right      ,					node.data      ,					SCREAMING_SNAKE_CASE__    )
          )

     return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE__      ,					-float('inf'    )      ,					float('inf'    )    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()









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			=						{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 ,       A_   )  ->	Optional[int]:
         super().__init__()
         __UpperCamelCase   							=torchvision.models.resnetaaa(pretrained=A_   )
         __UpperCamelCase   							=list(model.children()   )[:-2]
         __UpperCamelCase   							=nn.Sequential(*A_   )
         __UpperCamelCase   							=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






    def _a (      self ,       A_   )  ->	int:
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __UpperCamelCase   							=self.pool(self.model(A_   )   )
         __UpperCamelCase   							=torch.flatten(A_ ,       start_dim=2   )
         __UpperCamelCase   							=out.transpose(1 ,       2   ).contiguous()
         return out  # BxNx2048






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






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

         __UpperCamelCase   							=transforms






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






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=sentence[0], sentence[1:-1], sentence[-1]
         __UpperCamelCase   							=sentence[: self.max_seq_length]

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

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

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






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=Counter()
         for row in self.data:
              label_freqs.update(row['label']   )
         return label_freqs

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=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__    )    ):
          __UpperCamelCase   							=input_row['sentence']
          __UpperCamelCase   							=1

     __UpperCamelCase   							=torch.stack([row['image'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['label'] for row in batch]    )
     __UpperCamelCase   							=torch.stack([row['image_start_token'] for row in batch]    )
     __UpperCamelCase   							=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    (      ):
     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    (      ):
     return transforms.Compose(
         [
             transforms.Resize(2_56    ),
             transforms.CenterCrop(2_24    ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46777044, 0.44531429, 0.40661017]      ,					std=[0.12221994, 0.12145835, 0.14380469]      ,					),
         ]    )





