






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      _lowercase (     unittest.TestCase						):


      @slow
      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=XLMRobertaModel.from_pretrained('''xlm-roberta-base'''						)
            __snake_case      :    int  			=torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
            # The dog is cute and lives in the garden house

            __snake_case      :    int  			=torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
            __snake_case      :    List[Any]  			=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():
                  __snake_case      :    Any  			=model(a						)['''last_hidden_state'''].detach()
            self.assertEqual(output.shape							,   a						)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]							,   a							,   atol=1e-3						)						)







      @slow
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=XLMRobertaModel.from_pretrained('''xlm-roberta-large'''						)
            __snake_case      :    Union[str, Any]  			=torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
            # The dog is cute and lives in the garden house

            __snake_case      :    Optional[int]  			=torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
            __snake_case      :    str  			=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():
                  __snake_case      :    Dict  			=model(a						)['''last_hidden_state'''].detach()
            self.assertEqual(output.shape							,   a						)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]							,   a							,   atol=1e-3						)						)










"""simple docstring"""





def     __lowercase					(    a :				str       ,		a :				str )					->					str:
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __snake_case      :    list  			=[]
      for char_count in range(a ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count] )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count] )
      return "".join(a )


if __name__ == "__main__":
    print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)


