

from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        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)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''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 :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







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






'''simple docstring'''






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 _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,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							)							)



