






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


__a      :       str				  =   logging.get_logger(__name__)


class       __lowercase      (	lowercase_  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				["input_features", "is_longer"]




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


									"""simple docstring"""



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




					def 		lowerCAmelCase_     (				self       :			Optional[int]				):


									"""simple docstring"""



									__A         =	copy.deepcopy(self.__dict__				)
									__A         =	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 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			np.array ,	UpperCamelCase_       :			Optional[np.array] = None				):


									"""simple docstring"""



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




					def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]				):


									"""simple docstring"""



									__A         =	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
													__A         =	[0]
									if len(ranges[2]				) == 0:
													# if the audio is too short, we just use the first chunk
													__A         =	[0]
									# randomly choose index for each part
									__A         =	np.random.choice(ranges[0]				)
									__A         =	np.random.choice(ranges[1]				)
									__A         =	np.random.choice(ranges[2]				)

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

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




					def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			np.array ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple				):


									"""simple docstring"""



									if waveform.shape[0] > max_length:
													if truncation == "rand_trunc":
																	__A         =	True
																	# random crop to max_length (for compatibility) -> this should be handled by self.pad
																	__A         =	len(UpperCamelCase_				) - max_length
																	__A         =	np.random.randint(0 ,	overflow + 1				)
																	__A         =	waveform[idx : idx + max_length]
																	__A         =	self._np_extract_fbank_features(UpperCamelCase_ ,	self.mel_filters_slaney				)[None, :]
													elif truncation == "fusion":
																	__A         =	self._np_extract_fbank_features(UpperCamelCase_ ,	self.mel_filters				)
																	__A         =	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																	__A         =	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.
																					__A         =	np.stack([mel, mel, mel, mel] ,	axis=0				)
																					__A         =	False
																	else:
																					__A         =	self._random_mel_fusion(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)
																					__A         =	True
													else:
																	raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

									else:
													__A         =	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":
																					__A         =	int(max_length / len(UpperCamelCase_				)				)
																					__A         =	np.stack(np.tile(UpperCamelCase_ ,	n_repeat + 1				)				)[:max_length]
																	if padding == "repeatpad":
																					__A         =	int(max_length / len(UpperCamelCase_				)				)
																					__A         =	np.stack(np.tile(UpperCamelCase_ ,	UpperCamelCase_				)				)
																	__A         =	np.pad(UpperCamelCase_ ,	(0, max_length - waveform.shape[0]) ,	mode="""constant""" ,	constant_values=0				)

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

									return input_mel, longer





					def __call__(				self       :			Optional[int] ,	UpperCamelCase_       :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,	UpperCamelCase_       :			str = None ,	UpperCamelCase_       :			Optional[str] = None ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			Optional[Union[str, TensorType]] = None ,	**UpperCamelCase_       :			Union[str, Any] ,	):


									"""simple docstring"""



									__A         =	truncation if truncation is not None else self.truncation
									__A         =	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."""				)

									__A         =	isinstance(UpperCamelCase_ ,	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}"				)
									__A         =	is_batched_numpy or (
									    isinstance(UpperCamelCase_ ,	(list, tuple)				) and (isinstance(raw_speech[0] ,	(np.ndarray, tuple, list)				))
									)

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

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

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

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

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

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

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

									__A         =	{"""input_features""": input_mel, """is_longer""": is_longer}
									__A         =	BatchFeature(UpperCamelCase_				)

									if return_tensors is not None:
													__A         =	input_features.convert_to_tensors(UpperCamelCase_				)

									return input_features












from collections.abc import Sequence



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    return sum(c * (x**i) for i, c in enumerate(__lowercase    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    __A         =	0.0
    for coeff in reversed(__lowercase    ):
        __A         =	result * x + coeff
    return result


if __name__ == "__main__":
  __a      :       Dict				  =   (0.0, 0.0, 5.0, 9.3, 7.0)
  __a      :       Optional[Any]				  =   1_0.0
  print(evaluate_poly(poly, x))
  print(horner(poly, x))




