


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase__       :	Any     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		["""input_values""", """attention_mask"""]






							def __init__(	self       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=1_6_0_0_0       ,		lowerCAmelCase__=1_2_8       ,		lowerCAmelCase__=1_0_2_4       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=-4.2_677_393       ,		lowerCAmelCase__=4.5_689_974       ,		lowerCAmelCase__=False       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										super().__init__(feature_size=lowerCAmelCase__       ,		sampling_rate=lowerCAmelCase__       ,		padding_value=lowerCAmelCase__       ,		**lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_mel_bins
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= max_length
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= do_normalize
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= mean
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= std
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= return_attention_mask






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= torch.from_numpy(lowerCAmelCase__    ).unsqueeze(0    )
										SCREAMING_SNAKE_CASE_						:			Any       						= ta_kaldi.fbank(
										    lowerCAmelCase__       ,		htk_compat=lowerCAmelCase__       ,		sample_frequency=self.sampling_rate       ,		use_energy=lowerCAmelCase__       ,		window_type='hanning'       ,		num_mel_bins=self.num_mel_bins       ,		dither=0.0       ,		frame_shift=1_0       ,		)

										SCREAMING_SNAKE_CASE_						:			Tuple       						= fbank.shape[0]
										SCREAMING_SNAKE_CASE_						:			Tuple       						= max_length - n_frames

										# pad or truncate, depending on difference
										if difference > 0:
													SCREAMING_SNAKE_CASE_						:			str       						= torch.nn.ZeroPadad((0, 0, 0, difference)    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= pad_module(lowerCAmelCase__    )
										elif difference < 0:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= fbank[0:max_length, :]

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= fbank.numpy()

										return fbank






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return (input_values - (self.mean)) / (self.std * 2)






							def __call__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										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.'    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= isinstance(lowerCAmelCase__       ,		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}'''    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= is_batched_numpy or (
										    isinstance(lowerCAmelCase__       ,		(list, tuple)    ) and (isinstance(raw_speech[0]       ,		(np.ndarray, tuple, list)    ))
										)

										if is_batched:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= [np.asarray(lowerCAmelCase__       ,		dtype=np.floataa    ) for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase__       ,		np.ndarray    ):
													SCREAMING_SNAKE_CASE_						:			str       						= np.asarray(lowerCAmelCase__       ,		dtype=np.floataa    )
										elif isinstance(lowerCAmelCase__       ,		np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= raw_speech.astype(np.floataa    )

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

										# extract fbank features and pad/truncate to max_length
										SCREAMING_SNAKE_CASE_						:			int       						= [self._extract_fbank_features(lowerCAmelCase__       ,		max_length=self.max_length    ) for waveform in raw_speech]

										# convert into BatchFeature
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= BatchFeature({'input_values': features}    )

										# make sure list is in array format
										SCREAMING_SNAKE_CASE_						:			List[str]       						= padded_inputs.get('input_values'    )
										if isinstance(input_values[0]       ,		lowerCAmelCase__    ):
													SCREAMING_SNAKE_CASE_						:			int       						= [np.asarray(lowerCAmelCase__       ,		dtype=np.floataa    ) for feature in input_values]

										# normalization
										if self.do_normalize:
													SCREAMING_SNAKE_CASE_						:			str       						= [self.normalize(lowerCAmelCase__    ) for feature in input_values]

										if return_tensors is not None:
													SCREAMING_SNAKE_CASE_						:			str       						= padded_inputs.convert_to_tensors(lowerCAmelCase__    )

										return padded_inputs







'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


