


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							:   Dict						=						logging.get_logger(__name__)






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['input_features', 'attention_mask']

				def __init__(    self					, a__=80					, a__=1_6000					, a__=0.0					, a__=10					, a__=25					, a__="hamming_window"					, a__=3_27_68.0					, a__=0.97					, a__=1.0					, a__=True					, a__=True					, a__=False					, **a__					, )						->					str:
					super().__init__(feature_size=a__					, sampling_rate=a__					, padding_value=a__					, **a__ )

					A             =					feature_size
					A             =					sampling_rate
					A             =					padding_value
					A             =					hop_length
					A             =					win_length
					A             =					frame_signal_scale
					A             =					preemphasis_coeff
					A             =					mel_floor
					A             =					normalize_means
					A             =					normalize_vars
					A             =					win_function
					A             =					return_attention_mask

					A             =					win_length * sampling_rate // 1000
					A             =					hop_length * sampling_rate // 1000

					A             =					optimal_fft_length(self.sample_size )
					A             =					(self.n_fft // 2) + 1

				def   _UpperCAmelCase       (    self					, a__ )						->					np.ndarray:
					if self.win_function == "hamming_window":
						A             =					window_function(window_length=self.sample_size					, name=self.win_function					, periodic=a__ )
					else:
						A             =					window_function(window_length=self.sample_size					, name=self.win_function )

					A             =					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					, )

					A             =					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   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Optional[int]:
					# make sure we normalize float32 arrays
					if self.normalize_means:
						A             =					x[:input_length].mean(axis=0 )
						A             =					np.subtract(a__					, a__ )
					if self.normalize_vars:
						A             =					x[:input_length].std(axis=0 )
						A             =					np.divide(a__					, a__ )

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

					# make sure array is in float32
					A             =					x.astype(np.floataa )

					return x

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[np.ndarray]:
					A             =					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					, a__					, a__ = False					, a__ = None					, a__ = False					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, **a__					, )						->					BatchFeature:

					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.""" )

					A             =					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}' )
					A             =					is_batched_numpy or (
					    isinstance(a__					, (list, tuple) ) and (isinstance(raw_speech[0]					, (np.ndarray, tuple, list) ))
					)

					if is_batched:
						A             =					[np.asarray(a__					, dtype=np.floataa ) for speech in raw_speech]
					elif not is_batched and not isinstance(a__					, np.ndarray ):
						A             =					np.asarray(a__					, dtype=np.floataa )
					elif isinstance(a__					, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
						A             =					raw_speech.astype(np.floataa )

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

					# extract fbank features
					A             =					[self._extract_mfsc_features(a__ ) for one_waveform in raw_speech]

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

					A             =					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
					A             =					padded_inputs.get("""input_features""" )
					if isinstance(input_features[0]					, a__ ):
						A             =					[np.asarray(a__					, dtype=np.floataa ) for feature in input_features]

					A             =					padded_inputs.get("""attention_mask""" )
					if attention_mask is not None:
						A             =					[np.asarray(a__					, dtype=np.intaa ) for array in attention_mask]

					if self.normalize_means or self.normalize_vars:
						A             =					(
						    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
						)
						A             =					self.normalize(
						    padded_inputs["""input_features"""]					, attention_mask=a__ )

					if return_tensors is not None:
						A             =					padded_inputs.convert_to_tensors(a__ )

					return padded_inputs







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



