





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					="""Speech2TextFeatureExtractor"""
  a_							:       Union[str, Any]      					="""Speech2TextTokenizer"""







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    super().__init__(UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	self.feature_extractor
    _snake_case   :				Tuple														=	False







  def __call__(      self      :					int      ,				*UpperCamelCase      :					Optional[int]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    if self._in_target_context_manager:
      return self.current_processor(*UpperCamelCase      ,				**UpperCamelCase							)

    if "raw_speech" in kwargs:
      warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
      _snake_case   :				int														=	kwargs.pop('raw_speech'							)
    else:
      _snake_case   :				List[str]														=	kwargs.pop('audio'      ,				UpperCamelCase							)
    _snake_case   :				str														=	kwargs.pop('sampling_rate'      ,				UpperCamelCase							)
    _snake_case   :				Tuple														=	kwargs.pop('text'      ,				UpperCamelCase							)
    if len(UpperCamelCase							) > 0:
      _snake_case   :				Optional[Any]														=	args[0]
      _snake_case   :				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:
      _snake_case   :				Optional[int]														=	self.feature_extractor(UpperCamelCase      ,				*UpperCamelCase      ,				sampling_rate=UpperCamelCase      ,				**UpperCamelCase							)
    if text is not None:
      _snake_case   :				Optional[Any]														=	self.tokenizer(UpperCamelCase      ,				**UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					str      ,				*UpperCamelCase      :					Tuple      ,				**UpperCamelCase      :					str							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





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







  @contextmanager
  def UpperCamelCase_ (      self      :					str							):


    '''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.'							)
    _snake_case   :				Optional[Any]														=	True
    _snake_case   :				List[str]														=	self.tokenizer
    yield
    _snake_case   :				List[Any]														=	self.feature_extractor
    _snake_case   :				List[str]														=	False











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




