






'''simple docstring'''





from typing import List, Optional, Union

import numpy as np

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


lowercase  :							Optional[Any]			  =    logging.get_logger(__name__)




class 		UpperCAmelCase_						(	__SCREAMING_SNAKE_CASE					):


						'''simple docstring'''




						A     :   Any						=					['input_features', 'attention_mask']






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=80		,				_SCREAMING_SNAKE_CASE=1_6000		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=25		,				_SCREAMING_SNAKE_CASE="hamming_window"		,				_SCREAMING_SNAKE_CASE=3_2768.0		,				_SCREAMING_SNAKE_CASE=0.97		,				_SCREAMING_SNAKE_CASE=1.0		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				Any:
								super().__init__(feature_size=_a		,				sampling_rate=_a		,				padding_value=_a		,				**_a				)

								snake_case_				:							Optional[int]						 =       feature_size
								snake_case_				:							str						 =       sampling_rate
								snake_case_				:							int						 =       padding_value
								snake_case_				:							int						 =       hop_length
								snake_case_				:							Union[str, Any]						 =       win_length
								snake_case_				:							Optional[Any]						 =       frame_signal_scale
								snake_case_				:							Dict						 =       preemphasis_coeff
								snake_case_				:							List[Any]						 =       mel_floor
								snake_case_				:							Dict						 =       normalize_means
								snake_case_				:							int						 =       normalize_vars
								snake_case_				:							int						 =       win_function
								snake_case_				:							List[str]						 =       return_attention_mask

								snake_case_				:							Union[str, Any]						 =       win_length * sampling_rate // 1000
								snake_case_				:							str						 =       hop_length * sampling_rate // 1000

								snake_case_				:							List[Any]						 =       optimal_fft_length(self.sample_size				)
								snake_case_				:							Optional[Any]						 =       (self.n_fft // 2) + 1






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								if self.win_function == "hamming_window":
										snake_case_				:							Optional[Any]						 =       window_function(window_length=self.sample_size		,				name=self.win_function		,				periodic=_a				)
								else:
										snake_case_				:							List[Any]						 =       window_function(window_length=self.sample_size		,				name=self.win_function				)

								snake_case_				:							str						 =       mel_filter_bank(
								    num_frequency_bins=self.n_freqs		,				num_mel_filters=self.feature_size		,				min_frequency=0.0		,				max_frequency=self.sampling_rate / 2.0		,				sampling_rate=self.sampling_rate		,				)

								snake_case_				:							int						 =       spectrogram(
								    one_waveform * self.frame_signal_scale		,				window=_a		,				frame_length=self.sample_size		,				hop_length=self.sample_stride		,				fft_length=self.n_fft		,				center=_a		,				preemphasis=self.preemphasis_coeff		,				mel_filters=_a		,				mel_floor=self.mel_floor		,				log_mel="log"		,				)
								return msfc_features.T






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								# make sure we normalize float32 arrays
								if self.normalize_means:
										snake_case_				:							List[Any]						 =       x[:input_length].mean(axis=0				)
										snake_case_				:							Any						 =       np.subtract(_a		,				_a				)
								if self.normalize_vars:
										snake_case_				:							Tuple						 =       x[:input_length].std(axis=0				)
										snake_case_				:							Dict						 =       np.divide(_a		,				_a				)

								if input_length < x.shape[0]:
										snake_case_				:							str						 =       padding_value

								# make sure array is in float32
								snake_case_				:							Dict						 =       x.astype(np.floataa				)

								return x






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				int:
								snake_case_				:							Tuple						 =       attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
								return [self._normalize_one(_a		,				_a		,				self.padding_value				) for x, n in zip(_a		,				_a				)]






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:

								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 `raw_speech` 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."				)

								snake_case_				:							Dict						 =       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}'''				)
								snake_case_				:							str						 =       is_batched_numpy or (
								    isinstance(_a		,				(list, tuple)				) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list)				))
								)

								if is_batched:
										snake_case_				:							Dict						 =       [np.asarray(_a		,				dtype=np.floataa				) for speech in raw_speech]
								elif not is_batched and not isinstance(_a		,				np.ndarray				):
										snake_case_				:							int						 =       np.asarray(_a		,				dtype=np.floataa				)
								elif isinstance(_a		,				np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
										snake_case_				:							List[str]						 =       raw_speech.astype(np.floataa				)

								# always return batch
								if not is_batched:
										snake_case_				:							List[Any]						 =       [raw_speech]

								# extract fbank features
								snake_case_				:							Optional[Any]						 =       [self._extract_mfsc_features(_a				) for one_waveform in raw_speech]

								# convert into correct format for padding
								snake_case_				:							Tuple						 =       BatchFeature({"input_features": features}				)

								snake_case_				:							Optional[int]						 =       self.pad(
								    _a		,				padding=_a		,				max_length=_a		,				truncation=_a		,				pad_to_multiple_of=_a		,				return_attention_mask=_a		,				**_a		,				)
								# make sure list is in array format
								snake_case_				:							List[Any]						 =       padded_inputs.get("input_features"				)
								if isinstance(input_features[0]		,				_a				):
										snake_case_				:							Tuple						 =       [np.asarray(_a		,				dtype=np.floataa				) for feature in input_features]

								snake_case_				:							Optional[int]						 =       padded_inputs.get("attention_mask"				)
								if attention_mask is not None:
										snake_case_				:							Optional[Any]						 =       [np.asarray(_a		,				dtype=np.intaa				) for array in attention_mask]

								if self.normalize_means or self.normalize_vars:
										snake_case_				:							List[str]						 =       (
										    np.array(_a		,				dtype=np.intaa				)
										    if self._get_padding_strategies(_a		,				max_length=_a				) is not PaddingStrategy.DO_NOT_PAD
										    and padding
										    else None
										)
										snake_case_				:							Any						 =       self.normalize(
										    padded_inputs["input_features"]		,				attention_mask=_a				)

								if return_tensors is not None:
										snake_case_				:							Optional[int]						 =       padded_inputs.convert_to_tensors(_a				)

								return padded_inputs








def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		# "extended trapezoidal rule"
		# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
		snake_case_				:							Optional[Any]						 =       (boundary[1] - boundary[0]) / steps
		snake_case_				:							str						 =       boundary[0]
		snake_case_				:							Dict						 =       boundary[1]
		snake_case_				:							str						 =       make_points(_a  ,      _a  ,      _a    )
		snake_case_				:							str						 =       0.0
		y += (h / 2.0) * f(_a    )
		for i in x_i:
				# print(i)
				y += h * f(_a    )
		y += (h / 2.0) * f(_a    )
		return y

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[Any]  ,      _a       :			Tuple    ):
		snake_case_				:							Tuple						 =       a + h
		while x < (b - h):
				yield x
				snake_case_				:							List[str]						 =       x + h

def      lowerCAmelCase__	(    _a       :			Tuple    ):  # enter your function here
		snake_case_				:							Tuple						 =       (x - 0) * (x - 0)
		return y

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       0.0  # Lower bound of integration
		snake_case_				:							List[Any]						 =       1.0  # Upper bound of integration
		snake_case_				:							Tuple						 =       10.0  # define number of steps or resolution
		snake_case_				:							List[Any]						 =       [a, b]  # define boundary of integration
		snake_case_				:							Any						 =       method_a(_a  ,      _a    )
		print(F'''y = {y}'''    )


if __name__ == "__main__":
							main()
