
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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @slow
  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       XLMRobertaModel.from_pretrained("""xlm-roberta-base"""    )
    __magic_name__				     =       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

    __magic_name__				     =       torch.Size((1, 12, 768)    )  # batch_size, sequence_length, embedding_vector_dim
    __magic_name__				     =       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():
      __magic_name__				     =       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 							_lowercase							( self			: Optional[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       XLMRobertaModel.from_pretrained("""xlm-roberta-large"""    )
    __magic_name__				     =       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

    __magic_name__				     =       torch.Size((1, 12, 1024)    )  # batch_size, sequence_length, embedding_vector_dim
    __magic_name__				     =       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():
      __magic_name__				     =       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    )    )


from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





