

from math import ceil
from typing import List, Optional, Union

import numpy as np

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


A_ :				List[str]						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= ['''audio_values''', '''audio_mask''']






					def __init__(				self					, A__=2048					, A__=1					, A__=[16, 16]					, A__=128					, A__=4_4100					, A__=86					, A__=2048					, A__=0.0					, **A__					, ):
						super().__init__(
						    feature_size=A__					, sampling_rate=A__					, padding_value=A__					, **A__					, )

						A__							:					int       =	spectrogram_length
						A__							:					List[str]       =	num_channels
						A__							:					Tuple       =	patch_size
						A__							:					Any       =	feature_size // self.patch_size[1]
						A__							:					Tuple       =	n_fft
						A__							:					Any       =	sampling_rate // hop_length_to_sampling_rate
						A__							:					Dict       =	sampling_rate
						A__							:					Dict       =	padding_value
						A__							:					Dict       =	mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2					, num_mel_filters=A__					, min_frequency=0.0					, max_frequency=2_2_0_5_0.0					, sampling_rate=A__					, norm="""slaney"""					, mel_scale="""slaney"""					, ).T






					def 						__A    (				self					, A__    ):
						A__							:					List[Any]       =	spectrogram(
						    A__					, window_function(self.n_fft					, """hann"""    )					, frame_length=self.n_fft					, hop_length=self.hop_length					, power=2.0					, mel_filters=self.mel_filters.T					, log_mel="""dB"""					, db_range=8_0.0					, )
						A__							:					List[Any]       =	log_spec[:, :-1]
						A__							:					Dict       =	log_spec - 2_0.0
						A__							:					List[str]       =	np.clip(log_spec / 4_0.0					, -2.0					, 0.0    ) + 1.0
						return log_spec






					def __call__(				self					, A__					, A__ = None					, A__ = True					, A__ = None					, A__ = False					, A__ = False					, **A__					, ):

						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    """This feature extractor is set to support sampling rate"""
								    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
								    F""" 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__							:					Optional[int]       =	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}"""    )
						A__							:					Optional[int]       =	is_batched_numpy or (
						    isinstance(A__					, (list, tuple)    ) and (isinstance(raw_speech[0]					, (np.ndarray, tuple, list)    ))
						)
						if is_batched:
							A__							:					int       =	[np.asarray([speech]					, dtype=np.floataa    ).T for speech in raw_speech]
						elif not is_batched and not isinstance(A__					, np.ndarray    ):
							A__							:					List[Any]       =	np.asarray(A__					, dtype=np.floataa    )
						elif isinstance(A__					, np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
							A__							:					int       =	raw_speech.astype(np.floataa    )
						# always return batch
						if not is_batched:
							A__							:					Dict       =	[np.asarray([raw_speech]    ).T]

						# Convert audio signals to log mel spectrograms, truncate by time axis
						A__							:					List[str]       =	[
						    self._np_extract_fbank_features(waveform.squeeze()    ).T[: self.spectrogram_length] for waveform in raw_speech
						]
						if isinstance(audio_features[0]					, A__    ):
							A__							:					Dict       =	[np.asarray(A__					, dtype=np.floataa    ) for feature in audio_features]

						# Create audio attention mask
						A__							:					Optional[int]       =	max(
						    [ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len for feature in audio_features]    )  # The maximum number of audio patches in a batch
						if return_attention_mask:
							A__							:					str       =	[
							    (ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len) * [1]
							    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len) * [0]
							    for feature in audio_features
							]
							A__							:					Tuple       =	np.array(A__    ).astype(np.floataa    )

						# convert into correct format for padding
						A__							:					Tuple       =	max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
						A__							:					str       =	np.ones([len(A__    ), 1, max_time_len, self.feature_size]    ).astype(np.floataa    )
						A__							:					Tuple       =	padded_audio_features * self.padding_value
						for i in range(len(A__    )    ):
							A__							:					List[Any]       =	audio_features[i]
							A__							:					List[str]       =	feature

						# return as BatchFeature
						if return_attention_mask:
							A__							:					Union[str, Any]       =	{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
						else:
							A__							:					Optional[int]       =	{"""audio_values""": padded_audio_features}

						A__							:					List[Any]       =	BatchFeature(data=A__					, tensor_type=A__    )
						return encoded_inputs






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	0
			for cola in range(col + 1					,   size + 1      ):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   size + 1      ):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]					,   10      )] for row in range(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

	def interpolated_func(lowercase_:				int      ) -> int:
		return sum(
		    round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase_      )      )

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


if __name__ == "__main__":
	print(f'''{solution() = }''')



