
"""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   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @slow
     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: List[Any] 							=						XLMRobertaModel.from_pretrained("xlm-roberta-base"  )
          UpperCAmelCase__							: 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

          UpperCAmelCase__							: Optional[Any] 							=						torch.Size((1, 12, 7_68)  )  # batch_size, sequence_length, embedding_vector_dim
          UpperCAmelCase__							: Dict 							=						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():
               UpperCAmelCase__							: Optional[int] 							=						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  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: int 							=						XLMRobertaModel.from_pretrained("xlm-roberta-large"  )
          UpperCAmelCase__							: Optional[Any] 							=						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

          UpperCAmelCase__							: List[str] 							=						torch.Size((1, 12, 10_24)  )  # batch_size, sequence_length, embedding_vector_dim
          UpperCAmelCase__							: List[Any] 							=						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():
               UpperCAmelCase__							: int 							=						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  )  )





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



