





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_torch
class A_					(			unittest.TestCase   ):




   @slow
   def _lowerCAmelCase      (self	:str )->					List[Any]:
         __A          =				XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
         __A          =				torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
         # The dog is cute and lives in the garden house

         __A          =				torch.Size((1, 12, 768) )  # batch_size, sequence_length, embedding_vector_dim
         __A          =				torch.tensor(
             [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
               __A          =				model(_UpperCamelCase )['''last_hidden_state'''].detach()
         self.assertEqual(output.shape    ,   _UpperCamelCase )
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]    ,   _UpperCamelCase    ,   atol=1e-3 ) )






   @slow
   def _lowerCAmelCase      (self	:Optional[int] )->					Optional[int]:
         __A          =				XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
         __A          =				torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
         # The dog is cute and lives in the garden house

         __A          =				torch.Size((1, 12, 1024) )  # batch_size, sequence_length, embedding_vector_dim
         __A          =				torch.tensor(
             [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
               __A          =				model(_UpperCamelCase )['''last_hidden_state'''].detach()
         self.assertEqual(output.shape    ,   _UpperCamelCase )
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]    ,   _UpperCamelCase    ,   atol=1e-3 ) )












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>')





