





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


snake_case__					:							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 A_					(			nn.Module   ):




   def __init__(self	:List[Any]    ,   _UpperCamelCase	:Optional[int] )->					Tuple:
         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 _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Any )->					Optional[Any]:
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __A          =				self.pool(self.model(_UpperCamelCase ) )
         __A          =				torch.flatten(_UpperCamelCase    ,   start_dim=2 )
         __A          =				out.transpose(1    ,   2 ).contiguous()
         return out  # BxNx2048





class A_					(			_lowerCamelCase   ):




   def __init__(self	:Dict    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:str )->					List[str]:
         __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	:Any )->					Tuple:
         return len(self.data )




   def __getitem__(self	:Any    ,   _UpperCamelCase	:Tuple )->					int:
         __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 _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				Counter()
         for row in self.data:
               label_freqs.update(row['''label'''] )
         return label_freqs





def _a				(	lowerCamelCase:   Optional[Any] )	->						Any:







      '''simple docstring'''






      __A          =				[len(row['''sentence'''] ) for row in batch]
      __A   ,						__A          =				len(lowerCamelCase ), max(lowerCamelCase )

      __A          =				torch.zeros(lowerCamelCase					,				lowerCamelCase					,				dtype=torch.long )
      __A          =				torch.zeros(lowerCamelCase					,				lowerCamelCase					,				dtype=torch.long )

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase					,				lowerCamelCase ) ):
            __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 _a				(	)	->						List[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 _a				(	)	->						int:







      '''simple docstring'''






      return transforms.Compose(
          [
              transforms.Resize(2_56 ),
              transforms.CenterCrop(2_24 ),
              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]					,				),
          ] )












import requests

snake_case__					:							int													=     'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='



def _a				(	lowerCamelCase:   str )	->						None:







      '''simple docstring'''






      __A          =				requests.get(_NEWS_API + bbc_news_api_key ).json()
      # each article in the list is a dict
      for i, article in enumerate(bbc_news_page['''articles''']					,				1 ):
            print(F"""{i}.) {article['title']}""" )


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')





