





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class     SCREAMING_SNAKE_CASE__ (						lowerCamelCase_      ):
 __SCREAMING_SNAKE_CASE							       =	'''Wav2Vec2FeatureExtractor'''
 __SCREAMING_SNAKE_CASE							       =	'''AutoTokenizer'''



 def __init__(							self,__lowerCamelCase,__lowerCamelCase ):
        super().__init__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
        A__											=						self.feature_extractor
        A__											=						False



 @classmethod
 def 				UpperCamelCase			(							cls,__lowerCamelCase,**__lowerCamelCase ):





        try:
               return super().from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
        except OSError:
               warnings.warn(
                   f"Loading a tokenizer inside {cls.__name__} from a config that does not"
                   ''' include a `tokenizer_class` attribute is deprecated and will be '''
                   '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
                   ''' attribute to either your `config.json` or `tokenizer_config.json` '''
                   '''file to suppress this warning: ''',__SCREAMING_SNAKE_CASE,)

               A__											=						WavaVecaFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
               A__											=						WavaVecaCTCTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )

               return cls(feature_extractor=__SCREAMING_SNAKE_CASE,tokenizer=__SCREAMING_SNAKE_CASE )



 def __call__(							self,*__lowerCamelCase,**__lowerCamelCase ):





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

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

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



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):





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

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

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

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



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



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




 @contextmanager
 def 				UpperCamelCase			(							self ):
        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.''' )
        A__											=						True
        A__											=						self.tokenizer
        yield
        A__											=						self.feature_extractor
        A__											=						False







'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

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







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

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

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

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

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



