
'''simple docstring'''





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   _SCREAMING_SNAKE_CASE					):
							lowercase_		:						Optional[int]   							=  ["input_features", "is_longer"]







							def __init__(						self  :  Any      ,				snake_case__  :  Optional[int]=64      ,				snake_case__  :  Any=48_000      ,				snake_case__  :  Union[str, Any]=480      ,				snake_case__  :  str=10      ,				snake_case__  :  Any=1_024      ,				snake_case__  :  Optional[int]=0.0      ,				snake_case__  :  Any=False      ,				snake_case__  :  List[Any] = 0      ,				snake_case__  :  List[Any] = 14_000      ,				snake_case__  :  Dict = None      ,				snake_case__  :  Optional[int] = "fusion"      ,				snake_case__  :  Dict = "repeatpad"      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







								super().__init__(
								    feature_size=A_      ,				sampling_rate=A_      ,				padding_value=A_      ,				return_attention_mask=A_      ,				**A_      ,				)
								__lowerCAmelCase         =							top_db
								__lowerCAmelCase         =							truncation
								__lowerCAmelCase         =							padding
								__lowerCAmelCase         =							fft_window_size
								__lowerCAmelCase         =							(fft_window_size >> 1) + 1
								__lowerCAmelCase         =							hop_length
								__lowerCAmelCase         =							max_length_s
								__lowerCAmelCase         =							max_length_s * sampling_rate
								__lowerCAmelCase         =							sampling_rate
								__lowerCAmelCase         =							frequency_min
								__lowerCAmelCase         =							frequency_max
								__lowerCAmelCase         =							mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins      ,				num_mel_filters=A_      ,				min_frequency=A_      ,				max_frequency=A_      ,				sampling_rate=A_      ,				norm=A_      ,				mel_scale="htk"      ,				)
								__lowerCAmelCase         =							mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins      ,				num_mel_filters=A_      ,				min_frequency=A_      ,				max_frequency=A_      ,				sampling_rate=A_      ,				norm="slaney"      ,				mel_scale="slaney"      ,				)







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							copy.deepcopy(self.__dict__ )
								__lowerCAmelCase         =							self.__class__.__name__
								if "mel_filters" in output:
									del output["mel_filters"]
								if "mel_filters_slaney" in output:
									del output["mel_filters_slaney"]
								return output







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Dict = None ):
								"""simple docstring"""







								__lowerCAmelCase         =							spectrogram(
								    A_      ,				window_function(self.fft_window_size      ,				"hann" )      ,				frame_length=self.fft_window_size      ,				hop_length=self.hop_length      ,				power=2.0      ,				mel_filters=A_      ,				log_mel="dB"      ,				)
								return log_mel_spectrogram.T







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Any      ,				snake_case__  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							np.array_split(list(range(0      ,				total_frames - chunk_frames + 1 ) )      ,				3 )
								if len(ranges[1] ) == 0:
									# if the audio is too short, we just use the first chunk
									__lowerCAmelCase         =							[0]
								if len(ranges[2] ) == 0:
									# if the audio is too short, we just use the first chunk
									__lowerCAmelCase         =							[0]
								# randomly choose index for each part
								__lowerCAmelCase         =							np.random.choice(ranges[0] )
								__lowerCAmelCase         =							np.random.choice(ranges[1] )
								__lowerCAmelCase         =							np.random.choice(ranges[2] )

								__lowerCAmelCase         =							mel[idx_front : idx_front + chunk_frames, :]
								__lowerCAmelCase         =							mel[idx_middle : idx_middle + chunk_frames, :]
								__lowerCAmelCase         =							mel[idx_back : idx_back + chunk_frames, :]

								__lowerCAmelCase         =							torch.tensor(mel[None, None, :] )
								__lowerCAmelCase         =							torch.nn.functional.interpolate(
								    A_      ,				size=[chunk_frames, 64]      ,				mode="bilinear"      ,				align_corners=A_ )
								__lowerCAmelCase         =							mel_shrink[0][0].numpy()
								__lowerCAmelCase         =							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,				axis=0 )
								return mel_fusion







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  int      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								if waveform.shape[0] > max_length:
									if truncation == "rand_trunc":
										__lowerCAmelCase         =							True
										# random crop to max_length (for compatibility) -> this should be handled by self.pad
										__lowerCAmelCase         =							len(A_ ) - max_length
										__lowerCAmelCase         =							np.random.randint(0      ,				overflow + 1 )
										__lowerCAmelCase         =							waveform[idx : idx + max_length]
										__lowerCAmelCase         =							self._np_extract_fbank_features(A_      ,				self.mel_filters_slaney )[None, :]
									elif truncation == "fusion":
										__lowerCAmelCase         =							self._np_extract_fbank_features(A_      ,				self.mel_filters )
										__lowerCAmelCase         =							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
										__lowerCAmelCase         =							mel.shape[0]
										if chunk_frames == total_frames:
											# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
											# In this case, we just use the whole audio.
											__lowerCAmelCase         =							np.stack([mel, mel, mel, mel]      ,				axis=0 )
											__lowerCAmelCase         =							False
										else:
											__lowerCAmelCase         =							self._random_mel_fusion(A_      ,				A_      ,				A_ )
											__lowerCAmelCase         =							True
									else:
										raise NotImplementedError(F"data_truncating {truncation} not implemented" )

								else:
									__lowerCAmelCase         =							False
									# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
									if waveform.shape[0] < max_length:
										if padding == "repeat":
											__lowerCAmelCase         =							int(max_length / len(A_ ) )
											__lowerCAmelCase         =							np.stack(np.tile(A_      ,				n_repeat + 1 ) )[:max_length]
										if padding == "repeatpad":
											__lowerCAmelCase         =							int(max_length / len(A_ ) )
											__lowerCAmelCase         =							np.stack(np.tile(A_      ,				A_ ) )
										__lowerCAmelCase         =							np.pad(A_      ,				(0, max_length - waveform.shape[0])      ,				mode="constant"      ,				constant_values=0 )

									if truncation == "fusion":
										__lowerCAmelCase         =							self._np_extract_fbank_features(A_      ,				self.mel_filters )
										__lowerCAmelCase         =							np.stack([input_mel, input_mel, input_mel, input_mel]      ,				axis=0 )
									else:
										__lowerCAmelCase         =							self._np_extract_fbank_features(A_      ,				self.mel_filters_slaney )[None, :]

								return input_mel, longer







							def __call__(						self  :  int      ,				snake_case__  :  Any      ,				snake_case__  :  Dict = None      ,				snake_case__  :  Dict = None      ,				snake_case__  :  str = None      ,				snake_case__  :  Dict = None      ,				snake_case__  :  Dict = None      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							truncation if truncation is not None else self.truncation
								__lowerCAmelCase         =							padding if padding else self.padding

								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
										    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
										    F" was sampled with {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." )

								__lowerCAmelCase         =							isinstance(A_      ,				np.ndarray ) and len(raw_speech.shape ) > 1
								if is_batched_numpy and len(raw_speech.shape ) > 2:
									raise ValueError(F"Only mono-channel audio is supported for input to {self}" )
								__lowerCAmelCase         =							is_batched_numpy or (
								    isinstance(A_      ,				(list, tuple) ) and (isinstance(raw_speech[0]      ,				(np.ndarray, tuple, list) ))
								)

								if is_batched:
									__lowerCAmelCase         =							[np.asarray(A_      ,				dtype=np.floataa ) for speech in raw_speech]
								elif not is_batched and not isinstance(A_      ,				np.ndarray ):
									__lowerCAmelCase         =							np.asarray(A_      ,				dtype=np.floataa )
								elif isinstance(A_      ,				np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
									__lowerCAmelCase         =							raw_speech.astype(np.floataa )

								# always return batch
								if not is_batched:
									__lowerCAmelCase         =							[np.asarray(A_ )]

								# convert to mel spectrogram, truncate and pad if needed.
								__lowerCAmelCase         =							[
								    self._get_input_mel(A_      ,				max_length if max_length else self.nb_max_samples      ,				A_      ,				A_ )
								    for waveform in raw_speech
								]

								__lowerCAmelCase         =							[]
								__lowerCAmelCase         =							[]
								for mel, longer in padded_inputs:
									input_mel.append(A_ )
									is_longer.append(A_ )

								if truncation == "fusion" and sum(A_ ) == 0:
									# if no audio is longer than 10s, then randomly select one audio to be longer
									__lowerCAmelCase         =							np.random.randint(0      ,				len(A_ ) )
									__lowerCAmelCase         =							True

								if isinstance(input_mel[0]      ,				A_ ):
									__lowerCAmelCase         =							[np.asarray(A_      ,				dtype=np.floataa ) for feature in input_mel]

								# is_longer is a list of bool
								__lowerCAmelCase         =							[[longer] for longer in is_longer]

								__lowerCAmelCase         =							{"input_features": input_mel, "is_longer": is_longer}
								__lowerCAmelCase         =							BatchFeature(A_ )

								if return_tensors is not None:
									__lowerCAmelCase         =							input_features.convert_to_tensors(A_ )

								return input_features








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							data
								__lowerCAmelCase         =							None

class       a		:







							def __init__(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							None







							def __iter__(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.head
								while self.head:
									yield node.data
									__lowerCAmelCase         =							node.next
									if node == self.head:
										break







							def __len__(						self  :  Dict ):
								"""simple docstring"""







								return sum(1 for _ in self )







							def __repr__(						self  :  str ):
								"""simple docstring"""







								return "->".join(str(snake_case__ ) for item in iter(self ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(len(self )      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(0      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Any ):
								"""simple docstring"""







								if index < 0 or index > len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							Node(snake_case__ )
								if self.head is None:
									__lowerCAmelCase         =							new_node  # first node points itself
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								elif index == 0:  # insert at head
									__lowerCAmelCase         =							self.head
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							new_node
									if index == len(self ) - 1:  # insert at tail
										__lowerCAmelCase         =							new_node







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								return self.delete_nth(0 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.delete_nth(len(self ) - 1 )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int = 0 ):
								"""simple docstring"""







								if not 0 <= index < len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							self.head
								if self.head == self.tail:  # just one node
									__lowerCAmelCase         =							__lowerCAmelCase         =							None
								elif index == 0:  # delete head node
									__lowerCAmelCase         =							self.tail.next.next
									__lowerCAmelCase         =							self.head.next
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next.next
									if index == len(self ) - 1:  # delete at tail
										__lowerCAmelCase         =							temp
								return delete_node.data







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return len(self ) == 0






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase			,							i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0			,							7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2			,							3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()






