
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 					_SCREAMING_SNAKE_CASE  (	unittest.TestCase ):
      @slow
      def A_			(			self ):
          snake_case__						=				XLMRobertaModel.from_pretrained("xlm-roberta-base" )
          snake_case__						=				torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
          # The dog is cute and lives in the garden house

          snake_case__						=				torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
          snake_case__						=				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__						=				model(lowerCamelCase )["last_hidden_state"].detach()
          self.assertEqual(output.shape		,	lowerCamelCase )
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]		,	lowerCamelCase		,	atol=1e-3 ) )



      @slow
      def A_			(			self ):
          snake_case__						=				XLMRobertaModel.from_pretrained("xlm-roberta-large" )
          snake_case__						=				torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
          # The dog is cute and lives in the garden house

          snake_case__						=				torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
          snake_case__						=				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__						=				model(lowerCamelCase )["last_hidden_state"].detach()
          self.assertEqual(output.shape		,	lowerCamelCase )
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]		,	lowerCamelCase		,	atol=1e-3 ) )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F'''{price_plus_tax(100, 0.2_5) = }''')
     print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')



