from ...processing_utils import ProcessorMixin





class  __snake_case  (			SCREAMING_SNAKE_CASE__	):
					__lowerCamelCase       					=   """SpeechT5FeatureExtractor"""
					__lowerCamelCase       					=   """SpeechT5Tokenizer"""




					def __init__(   self    ,       __UpperCamelCase    ,       __UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







									super().__init__(__UpperCamelCase    ,       __UpperCamelCase		)




					def __call__(   self    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)     ->  Tuple:



									'''simple docstring'''







									snake_case__					:	str												=      kwargs.pop('audio'    ,       __UpperCamelCase		)
									snake_case__					:	Optional[Any]												=      kwargs.pop('text'    ,       __UpperCamelCase		)
									snake_case__					:	str												=      kwargs.pop('text_target'    ,       __UpperCamelCase		)
									snake_case__					:	Any												=      kwargs.pop('audio_target'    ,       __UpperCamelCase		)
									snake_case__					:	Any												=      kwargs.pop('sampling_rate'    ,       __UpperCamelCase		)

									if audio is not None and text is not None:
													raise ValueError(
													    'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'		)
									if audio_target is not None and text_target is not None:
													raise ValueError(
													    'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'		)
									if audio is None and audio_target is None and text is None and text_target is None:
													raise ValueError(
													    'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'		)

									if audio is not None:
													snake_case__					:	Optional[int]												=      self.feature_extractor(__UpperCamelCase    ,       *__UpperCamelCase    ,       sampling_rate=__UpperCamelCase    ,       **__UpperCamelCase		)
									elif text is not None:
													snake_case__					:	int												=      self.tokenizer(__UpperCamelCase    ,       **__UpperCamelCase		)
									else:
													snake_case__					:	List[str]												=      None

									if audio_target is not None:
													snake_case__					:	str												=      self.feature_extractor(audio_target=__UpperCamelCase    ,       *__UpperCamelCase    ,       sampling_rate=__UpperCamelCase    ,       **__UpperCamelCase		)
													snake_case__					:	Tuple												=      targets['input_values']
									elif text_target is not None:
													snake_case__					:	Tuple												=      self.tokenizer(__UpperCamelCase    ,       **__UpperCamelCase		)
													snake_case__					:	List[Any]												=      targets['input_ids']
									else:
													snake_case__					:	Optional[int]												=      None

									if inputs is None:
													return targets

									if targets is not None:
													snake_case__					:	Tuple												=      labels

													snake_case__					:	Dict												=      targets.get('attention_mask'		)
													if decoder_attention_mask is not None:
																	snake_case__					:	Tuple												=      decoder_attention_mask

									return inputs




					def 			__a			(   self    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







									snake_case__					:	List[Any]												=      kwargs.pop('input_values'    ,       __UpperCamelCase		)
									snake_case__					:	str												=      kwargs.pop('input_ids'    ,       __UpperCamelCase		)
									snake_case__					:	Tuple												=      kwargs.pop('labels'    ,       __UpperCamelCase		)

									if input_values is not None and input_ids is not None:
													raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'		)
									if input_values is None and input_ids is None and labels is None:
													raise ValueError(
													    'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'		)

									if input_values is not None:
													snake_case__					:	str												=      self.feature_extractor.pad(__UpperCamelCase    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)
									elif input_ids is not None:
													snake_case__					:	Optional[int]												=      self.tokenizer.pad(__UpperCamelCase    ,       **__UpperCamelCase		)
									else:
													snake_case__					:	Optional[int]												=      None

									if labels is not None:
													if "input_ids" in labels or (isinstance(__UpperCamelCase    ,       __UpperCamelCase		) and "input_ids" in labels[0]):
																	snake_case__					:	Any												=      self.tokenizer.pad(__UpperCamelCase    ,       **__UpperCamelCase		)
																	snake_case__					:	List[Any]												=      targets['input_ids']
													else:
																	snake_case__					:	str												=      self.feature_extractor.feature_size
																	snake_case__					:	Dict												=      self.feature_extractor.num_mel_bins
																	snake_case__					:	Optional[Any]												=      self.feature_extractor.pad(__UpperCamelCase    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)
																	snake_case__					:	List[Any]												=      feature_size_hack
																	snake_case__					:	int												=      targets['input_values']
									else:
													snake_case__					:	List[Any]												=      None

									if inputs is None:
													return targets

									if targets is not None:
													snake_case__					:	Optional[Any]												=      labels

													snake_case__					:	Tuple												=      targets.get('attention_mask'		)
													if decoder_attention_mask is not None:
																	snake_case__					:	Optional[Any]												=      decoder_attention_mask

									return inputs




					def 			__a			(   self    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







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




					def 			__a			(   self    ,       *__UpperCamelCase    ,       **__UpperCamelCase		)     ->  Any:



									'''simple docstring'''







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



import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A   :     Tuple       =				DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A   :     Dict       =				"main"
# Default branch name
A   :     List[str]       =				"f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
A   :     Tuple       =				"aaaaaaa"
# This commit does not exist, so we should 404.

A   :     int       =				"d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
A   :     Tuple       =				"4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"


@contextlib.contextmanager
def    a__       (						):
      print("Welcome!" )
      yield
      print("Bye!" )


@contextlib.contextmanager
def    a__       (						):
      print("Bonjour!" )
      yield
      print("Au revoir!" )


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Any:
        # If the spec is missing, importlib would not be able to import the module dynamically.
        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("transformers"      ) is not None



class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Union[str, Any]      )     ->		Union[str, Any]:
        with ContextManagers([]      ):
              print("Transformers are awesome!"      )
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue()     ,   "Transformers are awesome!\n"      )







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Dict     ,   __magic_name__       :				Union[str, Any]      )     ->		int:
        with ContextManagers([context_en()]      ):
              print("Transformers are awesome!"      )
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()     ,   "Welcome!\nTransformers are awesome!\nBye!\n"      )







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				str      )     ->		Union[str, Any]:
        with ContextManagers([context_fr(), context_en()]      ):
              print("Transformers are awesome!"      )
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()     ,   "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"      )







  @require_torch
  def       __A   (							self       :				List[str]      )     ->		Union[str, Any]:
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels", "next_sentence_label"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["start_positions", "end_positions"]      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )







  @require_tf
  def       __A   (							self       :				List[str]      )     ->		Optional[Any]:
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels", "next_sentence_label"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["start_positions", "end_positions"]      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )







  @require_flax
  def       __A   (							self       :				int      )     ->		Tuple:
        # Flax models don't have labels
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   []      )


