


"""simple docstring"""
import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE :    Optional[int]									=				logging.get_logger(__name__)



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=['input_values', 'attention_mask']



		def __init__(self				,       a_ = 1				,       a_ = 1_60_00				,       a_ = 0.0				,       a_ = False				,       a_ = 80				,       a_ = 16				,       a_ = 64				,       a_ = "hann_window"				,       a_ = 1.0				,       a_ = 80				,       a_ = 76_00				,       a_ = 1E-10				,       a_ = 2				,       a_ = True				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(feature_size=a_				,       sampling_rate=a_				,       padding_value=a_				,       **a_					)
			__snake_case	:						Optional[Any]						=				do_normalize
			__snake_case	:						Optional[int]						=				return_attention_mask

			__snake_case	:						int						=				num_mel_bins
			__snake_case	:						List[str]						=				hop_length
			__snake_case	:						List[Any]						=				win_length
			__snake_case	:						Union[str, Any]						=				win_function
			__snake_case	:						List[Any]						=				frame_signal_scale
			__snake_case	:						List[Any]						=				fmin
			__snake_case	:						int						=				fmax
			__snake_case	:						Optional[int]						=				mel_floor
			__snake_case	:						List[str]						=				reduction_factor

			__snake_case	:						Union[str, Any]						=				win_length * sampling_rate // 10_00
			__snake_case	:						Dict						=				hop_length * sampling_rate // 10_00
			__snake_case	:						str						=				optimal_fft_length(self.sample_size					)
			__snake_case	:						Union[str, Any]						=				(self.n_fft // 2) + 1

			__snake_case	:						Optional[int]						=				window_function(window_length=self.sample_size				,       name=self.win_function				,       periodic=a_					)

			__snake_case	:						Dict						=				mel_filter_bank(
			    num_frequency_bins=self.n_freqs				,       num_mel_filters=self.num_mel_bins				,       min_frequency=self.fmin				,       max_frequency=self.fmax				,       sampling_rate=self.sampling_rate				,       norm='''slaney'''				,       mel_scale='''slaney'''				,       )

			if frame_signal_scale != 1.0:
				warnings.warn(
				    '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''				,       a_				,       )
			if reduction_factor != 2.0:
				warnings.warn(
				    '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''				,       a_				,       )



		@staticmethod
		# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
		def 				SCREAMING_SNAKE_CASE  (a_				,       a_				,       a_ = 0.0					):





			'''simple docstring'''

			if attention_mask is not None:
				__snake_case	:						Tuple						=				np.array(a_				,       np.intaa					)
				__snake_case	:						Dict						=				[]

				for vector, length in zip(a_				,       attention_mask.sum(-1					)					):
					__snake_case	:						str						=				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7					)
					if length < normed_slice.shape[0]:
						__snake_case	:						Union[str, Any]						=				padding_value

					normed_input_values.append(a_					)
			else:
				__snake_case	:						Optional[int]						=				[(x - x.mean()) / np.sqrt(x.var() + 1E-7					) for x in input_values]

			return normed_input_values



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				spectrogram(
			    a_				,       window=self.window				,       frame_length=self.sample_size				,       hop_length=self.sample_stride				,       fft_length=self.n_fft				,       mel_filters=self.mel_filters				,       mel_floor=self.mel_floor				,       log_mel='''log10'''				,       )
			return log_mel_spec.T



		def __call__(self				,       a_ = None				,       a_ = None				,       a_ = False				,       a_ = None				,       a_ = False				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			if audio is None and audio_target is None:
				raise ValueError('''You must provide either `audio` or `audio_target` values.'''					)

			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 audio 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.'''					)

			if audio is not None:
				__snake_case	:						int						=				self._process_audio(
				    a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       **a_				,       )
			else:
				__snake_case	:						Optional[int]						=				None

			if audio_target is not None:
				__snake_case	:						int						=				self._process_audio(
				    a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       **a_				,       )

				if inputs is None:
					return inputs_target
				else:
					__snake_case	:						Union[str, Any]						=				inputs_target['''input_values''']
					__snake_case	:						Union[str, Any]						=				inputs_target.get('''attention_mask'''					)
					if decoder_attention_mask is not None:
						__snake_case	:						Optional[Any]						=				decoder_attention_mask

			return inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = False				,       a_ = False				,       a_ = None				,       a_ = False				,       a_ = None				,       a_ = None				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				isinstance(a_				,       np.ndarray					) and len(speech.shape					) > 1
			if is_batched_numpy and len(speech.shape					) > 2:
				raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""					)
			__snake_case	:						int						=				is_batched_numpy or (
			    isinstance(a_				,       (list, tuple)					) and (isinstance(speech[0]				,       (np.ndarray, tuple, list)					))
			)

			if is_batched:
				__snake_case	:						Any						=				[np.asarray(a_				,       dtype=np.floataa					) for speech in speech]
			elif not is_batched and not isinstance(a_				,       np.ndarray					):
				__snake_case	:						Optional[int]						=				np.asarray(a_				,       dtype=np.floataa					)
			elif isinstance(a_				,       np.ndarray					) and speech.dtype is np.dtype(np.floataa					):
				__snake_case	:						Union[str, Any]						=				speech.astype(np.floataa					)

			# always return batch
			if not is_batched:
				__snake_case	:						List[Any]						=				[speech]

			# needed to make pad() work on spectrogram inputs
			__snake_case	:						List[str]						=				self.feature_size

			# convert into correct format for padding
			if is_target:
				__snake_case	:						List[str]						=				[self._extract_mel_features(a_					) for waveform in speech]
				__snake_case	:						List[str]						=				BatchFeature({'''input_values''': features}					)
				__snake_case	:						Dict						=				self.num_mel_bins
			else:
				__snake_case	:						Dict						=				BatchFeature({'''input_values''': speech}					)

			__snake_case	:						Dict						=				self.pad(
			    a_				,       padding=a_				,       max_length=a_				,       truncation=a_				,       pad_to_multiple_of=a_				,       return_attention_mask=a_				,       **a_				,       )

			__snake_case	:						List[Any]						=				feature_size_hack

			# convert input values to correct format
			__snake_case	:						Dict						=				padded_inputs['''input_values''']
			if not isinstance(input_values[0]				,       np.ndarray					):
				__snake_case	:						Union[str, Any]						=				[np.asarray(a_				,       dtype=np.floataa					) for array in input_values]
			elif (
			    not isinstance(a_				,       np.ndarray					)
			    and isinstance(input_values[0]				,       np.ndarray					)
			    and input_values[0].dtype is np.dtype(np.floataa					)
			):
				__snake_case	:						Union[str, Any]						=				[array.astype(np.floataa					) for array in input_values]
			elif isinstance(a_				,       np.ndarray					) and input_values.dtype is np.dtype(np.floataa					):
				__snake_case	:						Optional[Any]						=				input_values.astype(np.floataa					)

			# convert attention_mask to correct format
			__snake_case	:						Dict						=				padded_inputs.get('''attention_mask'''					)
			if attention_mask is not None:
				__snake_case	:						List[str]						=				[np.asarray(a_				,       dtype=np.intaa					) for array in attention_mask]

			# zero-mean and unit-variance normalization
			if not is_target and self.do_normalize:
				__snake_case	:						Optional[int]						=				(
				    attention_mask
				    if self._get_padding_strategies(a_				,       max_length=a_					) is not PaddingStrategy.DO_NOT_PAD
				    else None
				)
				__snake_case	:						Dict						=				self.zero_mean_unit_var_norm(
				    padded_inputs['''input_values''']				,       attention_mask=a_				,       padding_value=self.padding_value					)

			if return_tensors is not None:
				__snake_case	:						Tuple						=				padded_inputs.convert_to_tensors(a_					)

			return padded_inputs



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				super().to_dict()

			# Don't serialize these as they are derived from the other properties.
			__snake_case	:						Any						=				['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
			for name in names:
				if name in output:
					del output[name]

			return output









"""simple docstring"""
from math import factorial, radians






def 		lowercase							(     _snake_case						:      float     ,   _snake_case						:      int = 18     ,   _snake_case						:      int = 10     )     ->float:
	"""simple docstring"""





	__snake_case	:						Any						=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	__snake_case	:						int						=				radians(_snake_case     )

	__snake_case	:						str						=				angle_in_radians
	__snake_case	:						Optional[int]						=				3
	__snake_case	:						List[Any]						=				-1

	for _ in range(_snake_case     ):
		result += (b * (angle_in_radians**a)) / factorial(_snake_case     )

		__snake_case	:						int						=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(_snake_case     ,   _snake_case     )


if __name__ == "__main__":
			__import__("""doctest""").testmod()





