





"""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_					=     {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 						__snake_case   (						nn.Module	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




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

     __A       :		Dict							=	transforms

 def __len__(       self    ):



     '''simple docstring'''




     return len(self.data    )

 def __getitem__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		str							=	torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']   ,		add_special_tokens=__lowerCamelCase    )    )
     __A					,			__A					,			__A       :		List[Any]							=	sentence[0], sentence[1:-1], sentence[-1]
     __A       :		int							=	sentence[: self.max_seq_length]

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

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

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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	Counter()
     for row in self.data:
         label_freqs.update(row['''label''']    )
     return label_freqs







def 						__lowercase	(  snake_case_    :    str		)  ->Dict:



    '''simple docstring'''







    __A       :		Optional[Any]							=	[len(row['''sentence''']		) for row in batch]
    __A					,			__A       :		Tuple							=	len(snake_case_		), max(snake_case_		)

    __A       :		int							=	torch.zeros(snake_case_							,snake_case_							,dtype=torch.long		)
    __A       :		Optional[Any]							=	torch.zeros(snake_case_							,snake_case_							,dtype=torch.long		)

    for i_batch, (input_row, length) in enumerate(zip(snake_case_							,snake_case_		)		):
        __A       :		Tuple							=	input_row['''sentence''']
        __A       :		List[Any]							=	1

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



    '''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 						__lowercase	(  )  ->Optional[int]:



    '''simple docstring'''







    return transforms.Compose(
        [
            transforms.Resize(256		),
            transforms.CenterCrop(224		),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]							,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]							,),
        ]		)











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->float:



    '''simple docstring'''







    __A       :		Tuple							=	0
    while len(snake_case_		) > 1:
        __A       :		List[Any]							=	0
        # Consider two files with minimum cost to be merged
        for _ in range(2		):
            __A       :		Dict							=	files.index(min(snake_case_		)		)
            temp += files[min_index]
            files.pop(snake_case_		)
        files.append(snake_case_		)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
       import doctest

       doctest.testmod()




