


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   """AutoFeatureExtractor"""
      __snake_case					       =   """Speech2Text2Tokenizer"""




      def __init__(				self:       Dict						,      a:       Tuple						,      a:       Optional[int]				):
       super().__init__(a						,      a				)
       __lowerCamelCase		:							str							=						self.feature_extractor
       __lowerCamelCase		:							str							=						False




      def __call__(				self:       Dict						,      *a:       str						,      **a:       List[Any]				):
       # For backward compatibility
       if self._in_target_context_manager:
        return self.current_processor(*a						,      **a				)

       if "raw_speech" in kwargs:
        warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
        __lowerCamelCase		:							Dict							=						kwargs.pop('raw_speech'				)
       else:
        __lowerCamelCase		:							int							=						kwargs.pop('audio'						,      a				)
       __lowerCamelCase		:							Dict							=						kwargs.pop('sampling_rate'						,      a				)
       __lowerCamelCase		:							Union[str, Any]							=						kwargs.pop('text'						,      a				)
       if len(a				) > 0:
        __lowerCamelCase		:							str							=						args[0]
        __lowerCamelCase		:							Optional[Any]							=						args[1:]

       if audio is None and text is None:
        raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

       if audio is not None:
        __lowerCamelCase		:							Dict							=						self.feature_extractor(a						,      *a						,      sampling_rate=a						,      **a				)
       if text is not None:
        __lowerCamelCase		:							Union[str, Any]							=						self.tokenizer(a						,      **a				)

       if text is None:
        return inputs
       elif audio is None:
        return encodings
       else:
        __lowerCamelCase		:							Tuple							=						encodings['input_ids']
        return inputs




      def      _snake_case	(				self:       List[Any]						,      *a:       List[Any]						,      **a:       List[str]				):
       return self.tokenizer.batch_decode(*a						,      **a				)




      def      _snake_case	(				self:       Union[str, Any]						,      *a:       List[str]						,      **a:       Tuple				):
       return self.tokenizer.decode(*a						,      **a				)




      @contextmanager
      def      _snake_case	(				self:       Any				):
       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your audio inputs, or in a separate call.'				)
       __lowerCamelCase		:							str							=						True
       __lowerCamelCase		:							Union[str, Any]							=						self.tokenizer
       yield
       __lowerCamelCase		:							List[Any]							=						self.feature_extractor
       __lowerCamelCase		:							int							=						False





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.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]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

