






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


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



class 			SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["input_features", "attention_mask"]



					def __init__( self					:  str							,		__A					:  Dict=8_0							,		__A					:  Tuple=1_6_0_0_0							,		__A					:  List[str]=0.0							,		__A					:  Optional[int]=1_0							,		__A					:  int=2_5							,		__A					:  List[str]="hamming_window"							,		__A					:  Any=3_2_7_6_8.0							,		__A					:  Union[str, Any]=0.9_7							,		__A					:  Any=1.0							,		__A					:  Union[str, Any]=True							,		__A					:  Any=True							,		__A					:  Optional[int]=False							,		**__A					:  Tuple							,		):
						super().__init__(feature_size=__A							,		sampling_rate=__A							,		padding_value=__A							,		**__A	)

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

						snake_case__			:				Any       =  win_length * sampling_rate // 1_0_0_0
						snake_case__			:				int       =  hop_length * sampling_rate // 1_0_0_0

						snake_case__			:				Optional[Any]       =  optimal_fft_length(self.sample_size	)
						snake_case__			:				Union[str, Any]       =  (self.n_fft // 2) + 1



					def 	_lowercase					( self					:  List[str]							,		__A					:  np.array	):
						if self.win_function == "hamming_window":
							snake_case__			:				str       =  window_function(window_length=self.sample_size							,		name=self.win_function							,		periodic=__A	)
						else:
							snake_case__			:				Optional[Any]       =  window_function(window_length=self.sample_size							,		name=self.win_function	)

						snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  Optional[int]							,		__A					:  List[str]							,		__A					:  List[Any]							,		__A					:  List[Any]	):
						# make sure we normalize float32 arrays
						if self.normalize_means:
							snake_case__			:				Optional[Any]       =  x[:input_length].mean(axis=0	)
							snake_case__			:				str       =  np.subtract(__A							,		__A	)
						if self.normalize_vars:
							snake_case__			:				Dict       =  x[:input_length].std(axis=0	)
							snake_case__			:				Optional[int]       =  np.divide(__A							,		__A	)

						if input_length < x.shape[0]:
							snake_case__			:				Dict       =  padding_value

						# make sure array is in float32
						snake_case__			:				Union[str, Any]       =  x.astype(np.floataa	)

						return x



					def 	_lowercase					( self					:  List[str]							,		__A					:  List[np.ndarray]							,		__A					:  Optional[np.ndarray] = None	):
						snake_case__			:				List[Any]       =  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					:  List[Any]							,		__A					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Optional[int] = None							,		__A					:  bool = False							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[str, TensorType]] = None							,		__A					:  Optional[int] = None							,		**__A					:  Dict							,		):

						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__			:				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}'''	)
						snake_case__			:				Optional[Any]       =  is_batched_numpy or (
						    isinstance(__A							,		(list, tuple)	) and (isinstance(raw_speech[0]							,		(np.ndarray, tuple, list)	))
						)

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

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

						# extract fbank features
						snake_case__			:				Optional[int]       =  [self._extract_mfsc_features(__A	) for one_waveform in raw_speech]

						# convert into correct format for padding
						snake_case__			:				List[str]       =  BatchFeature({"input_features": features}	)

						snake_case__			:				str       =  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[str]       =  padded_inputs.get("input_features"	)
						if isinstance(input_features[0]							,		__A	):
							snake_case__			:				List[Any]       =  [np.asarray(__A							,		dtype=np.floataa	) for feature in input_features]

						snake_case__			:				Any       =  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__			:				int       =  self.normalize(
							    padded_inputs["input_features"]							,		attention_mask=__A	)

						if return_tensors is not None:
							snake_case__			:				List[str]       =  padded_inputs.convert_to_tensors(__A	)

						return padded_inputs











from __future__ import annotations







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	if days_between_payments <= 0:
		raise ValueError("days_between_payments must be > 0"       )
	if daily_interest_rate < 0:
		raise ValueError("daily_interest_rate must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )
	return principal * daily_interest_rate * days_between_payments







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float					,  ):
	if number_of_compounding_periods <= 0:
		raise ValueError("number_of_compounding_periods must be > 0"       )
	if nominal_annual_interest_rate_percentage < 0:
		raise ValueError("nominal_annual_interest_rate_percentage must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )

	return principal * (
	    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
	    - 1
	)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float					,  ):
	if number_of_years <= 0:
		raise ValueError("number_of_years must be > 0"       )
	if nominal_annual_percentage_rate < 0:
		raise ValueError("nominal_annual_percentage_rate must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )

	return compound_interest(
	    snake_case_					,  nominal_annual_percentage_rate / 365					,  number_of_years * 365       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



