

'''simple docstring'''
from __future__ import annotations

from collections import Counter
from random import random







class 							A__   :





  def __init__(		self				:     int			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ={}





  def 						A  (		self				:     Union[str, Any]							, _a				:     str			)     ->   None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ={}





  def 						A  (		self				:     Optional[Any]							, _a				:     str							, _a				:     str							, _a				:     float			)     ->   None:





       '''simple docstring'''






       if nodea not in self.connections:
            self.add_node(_a			)
       if nodea not in self.connections:
            self.add_node(_a			)
       _SCREAMING_SNAKE_CASE							   =probability





  def 						A  (		self				:     List[str]			)     ->   list[str]:





       '''simple docstring'''






       return list(self.connections			)





  def 						A  (		self				:     Union[str, Any]							, _a				:     str			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =0
       _SCREAMING_SNAKE_CASE							   =random()

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					list[tuple[str, str, float]]							,			_UpperCamelCase						:					int    )				->      dict[str, int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =MarkovChainGraphUndirectedUnweighted()

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

     _SCREAMING_SNAKE_CASE							   =Counter(graph.get_nodes()    )
     _SCREAMING_SNAKE_CASE							   =start

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

     return visited


if __name__ == "__main__":
     import doctest

     doctest.testmod()



'''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
