




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


_lowerCamelCase			:	int							 =     {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   :			Union[str, Any]      ,				UpperCAmelCase__   :			int)		->int:





           '''simple docstring'''

           super().__init__()
           A__   				=					torchvision.models.resnetaaa(pretrained=UpperCAmelCase__)
           A__   				=					list(model.children())[:-2]
           A__   				=					nn.Sequential(*UpperCAmelCase__)
           A__   				=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])





    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.pool(self.model(UpperCAmelCase__))
           A__   				=					torch.flatten(UpperCAmelCase__      ,				start_dim=2)
           A__   				=					out.transpose(1      ,				2).contiguous()
           return out  # BxNx2048


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





           '''simple docstring'''

           A__   				=					[json.loads(UpperCAmelCase__) for l in open(UpperCAmelCase__)]
           A__   				=					os.path.dirname(UpperCAmelCase__)
           A__   				=					tokenizer
           A__   				=					labels
           A__   				=					len(UpperCAmelCase__)
           A__   				=					max_seq_length

           A__   				=					transforms


    def __len__(    self   :			List[Any])		->List[str]:





           '''simple docstring'''

           return len(self.data)


    def __getitem__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict)		->Dict:





           '''simple docstring'''

           A__   				=					torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']      ,				add_special_tokens=UpperCAmelCase__))
           A__				,							A__				,							A__   				=					sentence[0], sentence[1:-1], sentence[-1]
           A__   				=					sentence[: self.max_seq_length]

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

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

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





    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Union[str, Any]:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       A__   				=					[len(row['''sentence''']		) for row in batch]
       A__				,							A__   				=					len(lowercase_		), max(lowercase_		)

       A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)
       A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)

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

       A__   				=					torch.stack([row['''image'''] for row in batch]		)
       A__   				=					torch.stack([row['''label'''] for row in batch]		)
       A__   				=					torch.stack([row['''image_start_token'''] for row in batch]		)
       A__   				=					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       ( )     -> str:



       """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 SCREAMING_SNAKE_CASE       ( )     -> List[Any]:



       """simple docstring"""

       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]				,       ),
           ]		)








from __future__ import annotations

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       return np.maximum(0				,       lowercase_		)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


