
"""simple docstring"""







from ...processing_utils import ProcessorMixin




class 				A__ (			_lowerCamelCase):
  A_		: Optional[Any]            =			'SpeechT5FeatureExtractor'
  A_		: Dict            =			'SpeechT5Tokenizer'

  def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
   super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

  def __call__(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   __lowerCAmelCase    :		Tuple		    =   kwargs.pop('audio' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		Dict		    =   kwargs.pop('text' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		str		    =   kwargs.pop('text_target' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		int		    =   kwargs.pop('audio_target' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		List[Any]		    =   kwargs.pop('sampling_rate' ,	_SCREAMING_SNAKE_CASE				)

   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:
    __lowerCAmelCase    :		List[Any]		    =   self.feature_extractor(_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE ,	sampling_rate=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
   elif text is not None:
    __lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
   else:
    __lowerCAmelCase    :		Tuple		    =   None

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

   if inputs is None:
    return targets

   if targets is not None:
    __lowerCAmelCase    :		List[Any]		    =   labels

    __lowerCAmelCase    :		Dict		    =   targets.get('attention_mask'				)
    if decoder_attention_mask is not None:
     __lowerCAmelCase    :		Any		    =   decoder_attention_mask

   return inputs

  def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   __lowerCAmelCase    :		Dict		    =   kwargs.pop('input_values' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		List[str]		    =   kwargs.pop('input_ids' ,	_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		str		    =   kwargs.pop('labels' ,	_SCREAMING_SNAKE_CASE				)

   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:
    __lowerCAmelCase    :		Optional[int]		    =   self.feature_extractor.pad(_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
   elif input_ids is not None:
    __lowerCAmelCase    :		Optional[Any]		    =   self.tokenizer.pad(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
   else:
    __lowerCAmelCase    :		List[str]		    =   None

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

   if inputs is None:
    return targets

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

    __lowerCAmelCase    :		str		    =   targets.get('attention_mask'				)
    if decoder_attention_mask is not None:
     __lowerCAmelCase    :		List[Any]		    =   decoder_attention_mask

   return inputs

  def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







  def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
   return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()