


from typing import Dict

from .base import GenericTensor, Pipeline







class     _lowercase				(							lowercase__):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str=None      ,   __lowerCamelCase							:    Optional[Any]=None      ,   __lowerCamelCase							:    Tuple=None      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             if tokenize_kwargs is None:
                   lowerCamelCase__	:       Optional[int]					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" )
                   lowerCamelCase__	:       List[str]					=						truncation

             lowerCamelCase__	:       Optional[Any]					=						tokenize_kwargs

             lowerCamelCase__	:       Optional[Any]					=						{}
             if return_tensors is not None:
                   lowerCamelCase__	:       str					=						return_tensors

             return preprocess_params, {}, postprocess_params





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.framework
             lowerCamelCase__	:       List[Any]					=						self.tokenizer(__lowerCamelCase      ,   return_tensors=__lowerCamelCase      ,   **__lowerCamelCase )
             return model_inputs





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.model(**__lowerCamelCase )
             return model_outputs





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]=False ):


             '''simple docstring'''



             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()





       def __call__(     self							:    Optional[int]      ,   *__lowerCamelCase							:    Union[str, Any]      ,   **__lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             return super().__call__(*__lowerCamelCase      ,   **__lowerCamelCase )






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel







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


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,   dtype=tf.intaa      ,   )  # J'aime le camembert !"

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   dtype=tf.floataa      ,   )
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1E-4 ) )


