

'''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 tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM







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





  @slow
  def 						A  (		self				:     Optional[Any]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'			)
       _SCREAMING_SNAKE_CASE							   =AutoTokenizer.from_pretrained('google/mt5-small'			)

       _SCREAMING_SNAKE_CASE							   =tokenizer('Hello there'							, return_tensors='tf'			).input_ids
       _SCREAMING_SNAKE_CASE							   =tokenizer('Hi I am'							, return_tensors='tf'			).input_ids

       _SCREAMING_SNAKE_CASE							   =model(_a							, labels=_a			).loss
       _SCREAMING_SNAKE_CASE							   =-tf.math.reduce_mean(_a			).numpy()

       _SCREAMING_SNAKE_CASE							   =-21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 2e-4			)



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 							A__   (	A__  ):
  A__					=    'MCTCTFeatureExtractor'
  A__					=    'AutoTokenizer'





  def __init__(		self				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       super().__init__(_a							, _a			)
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False





  def __call__(		self				:     Dict							, *_a				:     str							, **_a				:     Dict			)     ->   Dict:





       '''simple docstring'''






       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.'			)
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('raw_speech'			)
       else:
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('audio'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('sampling_rate'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('text'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =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:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor(_a							, *_a							, sampling_rate=_a							, **_a			)
       if text is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer(_a							, **_a			)

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





  def 						A  (		self				:     Any							, *_a				:     List[str]							, **_a				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							, **_a			)





  def 						A  (		self				:     Dict							, *_a				:     Tuple							, **_a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor.pad(*_a							, **_a			)

       _SCREAMING_SNAKE_CASE							   =kwargs.pop('input_features'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('labels'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if input_features is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor.pad(_a							, *_a							, **_a			)
       if labels is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer.pad(_a							, **_a			)

       if labels is None:
            return input_features
       elif input_features is None:
            return labels
       else:
            _SCREAMING_SNAKE_CASE							   =labels['input_ids']
            return input_features





  def 						A  (		self				:     Tuple							, *_a				:     Dict							, **_a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.decode(*_a							, **_a			)





  @contextmanager
  def 						A  (		self				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       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.'			)
       _SCREAMING_SNAKE_CASE							   =True
       _SCREAMING_SNAKE_CASE							   =self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False
