






'''simple docstring'''

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 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      XLMRobertaModel.from_pretrained("""xlm-roberta-base"""   )
        UpperCamelCase       						=      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
        # The dog is cute and lives in the garden house

        UpperCamelCase       						=      torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        UpperCamelCase       						=      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():
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )["""last_hidden_state"""].detach()
        self.assertEqual(output.shape    ,	_SCREAMING_SNAKE_CASE   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-3   )   )


  @slow
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      XLMRobertaModel.from_pretrained("""xlm-roberta-large"""   )
        UpperCamelCase       						=      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
        # The dog is cute and lives in the garden house

        UpperCamelCase       						=      torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
        UpperCamelCase       						=      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():
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )["""last_hidden_state"""].detach()
        self.assertEqual(output.shape    ,	_SCREAMING_SNAKE_CASE   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-3   )   )







'''simple docstring'''

SCREAMING_SNAKE_CASE__											=     8.31_44_62  # Unit - J mol-1 K-1







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				float:
      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""					)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()
