





from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase						:   str			  =     logging.get_logger(__name__)





class 						_A(					_lowerCamelCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Union[str, Any]					    =	['''input_values''', '''padding_mask''']



				def __init__(			self     ,					_A = 1     ,					_A = 24000     ,					_A = 0.0     ,					_A = None     ,					_A = None     ,					**_A     ,					):
									super().__init__(feature_size=_UpperCamelCase     ,					sampling_rate=_UpperCamelCase     ,					padding_value=_UpperCamelCase     ,					**_UpperCamelCase     )
									__A	:						str       =				chunk_length_s
									__A	:						Tuple       =				overlap



				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.chunk_length_s is None:
														return None
									else:
														return int(self.chunk_length_s * self.sampling_rate     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.chunk_length_s is None or self.overlap is None:
														return None
									else:
														return max(1     ,					int((1.0 - self.overlap) * self.chunk_length     )     )





				def __call__(			self     ,					_A     ,					_A = None     ,					_A = False     ,					_A = None     ,					_A = None     ,					_A = None     ,					):
									if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																			    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
																			    F""" {self.sampling_rate} and not {sampling_rate}."""     )
									else:
														logger.warning(
														    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
														    'Failing to do so can result in silent errors that might be hard to debug.'     )

									if padding and truncation:
														raise ValueError('Both padding and truncation were set. Make sure you only set one.'     )
									elif padding is None:
														# by default let's pad the inputs
														__A	:						Optional[Any]       =				True

									__A	:						Optional[int]       =				bool(
									    isinstance(_UpperCamelCase     ,					(list, tuple)     ) and (isinstance(raw_audio[0]     ,					(np.ndarray, tuple, list)     ))     )

									if is_batched:
														__A	:						Optional[int]       =				[np.asarray(_UpperCamelCase     ,					dtype=np.floataa     ).T for audio in raw_audio]
									elif not is_batched and not isinstance(_UpperCamelCase     ,					np.ndarray     ):
														__A	:						str       =				np.asarray(_UpperCamelCase     ,					dtype=np.floataa     )
									elif isinstance(_UpperCamelCase     ,					np.ndarray     ) and raw_audio.dtype is np.dtype(np.floataa     ):
														__A	:						List[str]       =				raw_audio.astype(np.floataa     )

									# always return batch
									if not is_batched:
														__A	:						Dict       =				[np.asarray(_UpperCamelCase     ).T]

									# verify inputs are valid
									for idx, example in enumerate(_UpperCamelCase     ):
														if example.ndim > 2:
																			raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""     )
														if self.feature_size == 1 and example.ndim != 1:
																			raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""     )
														if self.feature_size == 2 and example.shape[-1] != 2:
																			raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""     )

									__A	:						Tuple       =				None
									__A	:						int       =				BatchFeature({'input_values': raw_audio}     )
									if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
														if truncation:
																			__A	:						Dict       =				min(array.shape[0] for array in raw_audio     )
																			__A	:						str       =				int(np.floor(max_length / self.chunk_stride     )     )
																			__A	:						Dict       =				(nb_step - 1) * self.chunk_stride + self.chunk_length
														elif padding:
																			__A	:						Any       =				max(array.shape[0] for array in raw_audio     )
																			__A	:						Tuple       =				int(np.ceil(max_length / self.chunk_stride     )     )
																			__A	:						Dict       =				(nb_step - 1) * self.chunk_stride + self.chunk_length
																			__A	:						str       =				'max_length'
														else:
																			__A	:						int       =				input_values

        # normal padding on batch
									if padded_inputs is None:
														__A	:						Dict       =				self.pad(
														    _UpperCamelCase     ,					max_length=_UpperCamelCase     ,					truncation=_UpperCamelCase     ,					padding=_UpperCamelCase     ,					return_attention_mask=_UpperCamelCase     ,					)
														if padding:
																			__A	:						Any       =				padded_inputs.pop('attention_mask'     )

									__A	:						Any       =				[]
									for example in padded_inputs.pop('input_values'     ):
														if self.feature_size == 1:
																			__A	:						Optional[Any]       =				example[..., None]
														input_values.append(example.T     )

									__A	:						Optional[int]       =				input_values
									if return_tensors is not None:
														__A	:						int       =				padded_inputs.convert_to_tensors(_UpperCamelCase     )

									return padded_inputs











def _a				(	lowerCamelCase:   dict )	->						bool:







      '''simple docstring'''






      __A          =				set()
      # To detect a back edge, keep track of vertices currently in the recursion stack
      __A          =				set()
      return any(
          node not in visited and depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
          for node in graph )



def _a				(	lowerCamelCase:   dict					,				lowerCamelCase:   int					,				lowerCamelCase:   set					,				lowerCamelCase:   set )	->						bool:







      '''simple docstring'''






      visited.add(lowerCamelCase )
      rec_stk.add(lowerCamelCase )

      for node in graph[vertex]:
            if node not in visited:
                  if depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ):
                        return True
            elif node in rec_stk:
                  return True

    # The node needs to be removed from recursion stack before function ends
      rec_stk.remove(lowerCamelCase )
      return False


if __name__ == "__main__":
       from doctest import testmod

       testmod()





