
"""simple docstring"""




import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
    import jax.numpy as jnp

    from transformers import FlaxXLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_flax
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @slow
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[Any] 							=						FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"  )
          UpperCAmelCase__							: Optional[Any] 							=						AutoTokenizer.from_pretrained("xlm-roberta-base"  )
          UpperCAmelCase__							: str 							=						"The dog is cute and lives in the garden house"
          UpperCAmelCase__							: str 							=						jnp.array([tokenizer.encode(__UpperCamelCase  )]  )

          UpperCAmelCase__							: Optional[Any] 							=						(1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
          UpperCAmelCase__							: Union[str, Any] 							=						jnp.array(
              [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]  )

          UpperCAmelCase__							: Tuple 							=						model(__UpperCamelCase  )["last_hidden_state"]
          self.assertEqual(output.shape ,			__UpperCamelCase  )
          # compare the actual values for a slice of last dim
          self.assertTrue(jnp.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



