

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


A_ :				Dict						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= ['''input_values''', '''attention_mask''']






					def __init__(				self					, A__ = 1					, A__ = 1_6000					, A__ = 0.0					, A__ = False					, A__ = 80					, A__ = 16					, A__ = 64					, A__ = "hann_window"					, A__ = 1.0					, A__ = 80					, A__ = 7600					, A__ = 1e-10					, A__ = 2					, A__ = True					, **A__					, ):
						super().__init__(feature_size=A__					, sampling_rate=A__					, padding_value=A__					, **A__    )
						A__							:					Dict       =	do_normalize
						A__							:					int       =	return_attention_mask

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

						A__							:					Optional[int]       =	win_length * sampling_rate // 1000
						A__							:					Union[str, Any]       =	hop_length * sampling_rate // 1000
						A__							:					Tuple       =	optimal_fft_length(self.sample_size    )
						A__							:					Any       =	(self.n_fft // 2) + 1

						A__							:					Union[str, Any]       =	window_function(window_length=self.sample_size					, name=self.win_function					, periodic=A__    )

						A__							:					Union[str, Any]       =	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 						__A    (				A__					, A__					, A__ = 0.0    ):
						if attention_mask is not None:
							A__							:					Tuple       =	np.array(A__					, np.intaa    )
							A__							:					List[Any]       =	[]

							for vector, length in zip(A__					, attention_mask.sum(-1    )    ):
								A__							:					Tuple       =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
								if length < normed_slice.shape[0]:
									A__							:					Optional[int]       =	padding_value

								normed_input_values.append(A__    )
						else:
							A__							:					Union[str, Any]       =	[(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

						return normed_input_values






					def 						__A    (				self					, A__					, ):
						A__							:					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__					, ):
						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:
							A__							:					str       =	self._process_audio(
							    A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, **A__					, )
						else:
							A__							:					Dict       =	None

						if audio_target is not None:
							A__							:					Tuple       =	self._process_audio(
							    A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, **A__					, )

							if inputs is None:
								return inputs_target
							else:
								A__							:					Optional[Any]       =	inputs_target["""input_values"""]
								A__							:					Dict       =	inputs_target.get("""attention_mask"""    )
								if decoder_attention_mask is not None:
									A__							:					str       =	decoder_attention_mask

						return inputs






					def 						__A    (				self					, A__					, A__ = False					, A__ = False					, A__ = None					, A__ = False					, A__ = None					, A__ = None					, A__ = None					, **A__					, ):
						A__							:					str       =	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}"""    )
						A__							:					List[str]       =	is_batched_numpy or (
						    isinstance(A__					, (list, tuple)    ) and (isinstance(speech[0]					, (np.ndarray, tuple, list)    ))
						)

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

						# always return batch
						if not is_batched:
							A__							:					Optional[Any]       =	[speech]

						# needed to make pad() work on spectrogram inputs
						A__							:					Dict       =	self.feature_size

						# convert into correct format for padding
						if is_target:
							A__							:					Any       =	[self._extract_mel_features(A__    ) for waveform in speech]
							A__							:					Union[str, Any]       =	BatchFeature({"""input_values""": features}    )
							A__							:					List[Any]       =	self.num_mel_bins
						else:
							A__							:					int       =	BatchFeature({"""input_values""": speech}    )

						A__							:					List[str]       =	self.pad(
						    A__					, padding=A__					, max_length=A__					, truncation=A__					, pad_to_multiple_of=A__					, return_attention_mask=A__					, **A__					, )

						A__							:					str       =	feature_size_hack

						# convert input values to correct format
						A__							:					Optional[Any]       =	padded_inputs["""input_values"""]
						if not isinstance(input_values[0]					, np.ndarray    ):
							A__							:					Optional[int]       =	[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    )
						):
							A__							:					Tuple       =	[array.astype(np.floataa    ) for array in input_values]
						elif isinstance(A__					, np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
							A__							:					int       =	input_values.astype(np.floataa    )

						# convert attention_mask to correct format
						A__							:					Any       =	padded_inputs.get("""attention_mask"""    )
						if attention_mask is not None:
							A__							:					Optional[Any]       =	[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:
							A__							:					Any       =	(
							    attention_mask
							    if self._get_padding_strategies(A__					, max_length=A__    ) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							A__							:					int       =	self.zero_mean_unit_var_norm(
							    padded_inputs["""input_values"""]					, attention_mask=A__					, padding_value=self.padding_value    )

						if return_tensors is not None:
							A__							:					Union[str, Any]       =	padded_inputs.convert_to_tensors(A__    )

						return padded_inputs






					def 						__A    (				self    ):
						A__							:					str       =	super().to_dict()

						# Don't serialize these as they are derived from the other properties.
						A__							:					List[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






from functools import lru_cache

@lru_cache
def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if num < 0:
		raise ValueError("""Number should not be negative."""      )

	return 1 if num in (0, 1) else num * factorial(num - 1      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



