





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


_lowercase		:  Dict			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						int						    =					['input_values', 'padding_mask']



					def __init__(		self     :     List[str]       ,      lowerCamelCase     :     int = 1       ,      lowerCamelCase     :     int = 2_40_00       ,      lowerCamelCase     :     float = 0.0       ,      lowerCamelCase     :     float = None       ,      lowerCamelCase     :     float = None       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							super().__init__(feature_size=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      padding_value=lowerCamelCase       ,      **lowerCamelCase						)
							lowerCamelCase_	:     str 						=      chunk_length_s
							lowerCamelCase_	:     Union[str, Any] 						=      overlap



					@property
					def   __a     (		self     :     Union[str, Any]						):
							if self.chunk_length_s is None:
									return None
							else:
									return int(self.chunk_length_s * self.sampling_rate						)



					@property
					def   __a     (		self     :     Union[str, Any]						):
							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     :     List[Any]       ,      lowerCamelCase     :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,      lowerCamelCase     :     Optional[Union[bool, str, PaddingStrategy]] = None       ,      lowerCamelCase     :     Optional[bool] = False       ,      lowerCamelCase     :     Optional[int] = None       ,      lowerCamelCase     :     Optional[Union[str, TensorType]] = None       ,      lowerCamelCase     :     Optional[int] = 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
									lowerCamelCase_	:     Dict 						=      True

							lowerCamelCase_	:     Union[str, Any] 						=      bool(
							    isinstance(lowerCamelCase       ,      (list, tuple)						) and (isinstance(raw_audio[0]       ,      (np.ndarray, tuple, list)						))						)

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

							# always return batch
							if not is_batched:
									lowerCamelCase_	:     Tuple 						=      [np.asarray(lowerCamelCase						).T]

							# verify inputs are valid
							for idx, example in enumerate(lowerCamelCase						):
									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"						)

							lowerCamelCase_	:     List[Any] 						=      None
							lowerCamelCase_	:     Optional[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:
											lowerCamelCase_	:     int 						=      min(array.shape[0] for array in raw_audio						)
											lowerCamelCase_	:     str 						=      int(np.floor(max_length / self.chunk_stride						)						)
											lowerCamelCase_	:     Optional[Any] 						=      (nb_step - 1) * self.chunk_stride + self.chunk_length
									elif padding:
											lowerCamelCase_	:     Optional[int] 						=      max(array.shape[0] for array in raw_audio						)
											lowerCamelCase_	:     Union[str, Any] 						=      int(np.ceil(max_length / self.chunk_stride						)						)
											lowerCamelCase_	:     List[str] 						=      (nb_step - 1) * self.chunk_stride + self.chunk_length
											lowerCamelCase_	:     int 						=      'max_length'
									else:
											lowerCamelCase_	:     Optional[Any] 						=      input_values

        # normal padding on batch
							if padded_inputs is None:
									lowerCamelCase_	:     Tuple 						=      self.pad(
									    lowerCamelCase       ,      max_length=lowerCamelCase       ,      truncation=lowerCamelCase       ,      padding=lowerCamelCase       ,      return_attention_mask=lowerCamelCase       ,      )
									if padding:
											lowerCamelCase_	:     Optional[int] 						=      padded_inputs.pop('attention_mask'						)

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

							lowerCamelCase_	:     Tuple 						=      input_values
							if return_tensors is not None:
									lowerCamelCase_	:     Optional[Any] 						=      padded_inputs.convert_to_tensors(lowerCamelCase						)

							return padded_inputs







_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
