


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 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		XLMRobertaModel.from_pretrained('''xlm-roberta-base'''      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		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

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		torch.Size((1, 12, 7_68)      )  # batch_size, sequence_length, embedding_vector_dim
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		torch.tensor(
               [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]      )
           #  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():
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		model(_lowercase      )['''last_hidden_state'''].detach()
           self.assertEqual(output.shape   ,   _lowercase      )
           # compare the actual values for a slice of last dim
           self.assertTrue(torch.allclose(output[:, :, -1]   ,   _lowercase   ,   atol=1E-3      )      )







     @slow
     def 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		XLMRobertaModel.from_pretrained('''xlm-roberta-large'''      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		torch.Size((1, 12, 10_24)      )  # batch_size, sequence_length, embedding_vector_dim
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.tensor(
               [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]      )
           #  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():
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		model(_lowercase      )['''last_hidden_state'''].detach()
           self.assertEqual(output.shape   ,   _lowercase      )
           # compare the actual values for a slice of last dim
           self.assertTrue(torch.allclose(output[:, :, -1]   ,   _lowercase   ,   atol=1E-3      )      )













'''simple docstring'''






from ..utils import DummyObject, requires_backends




class 					UpperCAmelCase	(     metaclass=snake_case_  ):
      _lowercase:     List[Any]			=  ['''torch''', '''scipy''']




      def __init__(     self     :					Tuple     ,     *__snake_case     :					Dict     ,     **__snake_case     :					List[str]    )							-> str:
            requires_backends(self     ,     ["""torch""", """scipy"""]    )




      @classmethod
      def 							lowercase__       (     cls     :					str     ,     *__snake_case     :					List[str]     ,     **__snake_case     :					str    )							-> Tuple:
            requires_backends(cls     ,     ["""torch""", """scipy"""]    )




      @classmethod
      def 							lowercase__       (     cls     :					Optional[int]     ,     *__snake_case     :					int     ,     **__snake_case     :					Union[str, Any]    )							-> Optional[int]:
            requires_backends(cls     ,     ["""torch""", """scipy"""]    )






