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


__SCREAMING_SNAKE_CASE						:   List[Any]         =			logging.get_logger(__name__)



class        lowercase_   (					SCREAMING_SNAKE_CASE__		):
							_lowerCamelCase						=		['input_features', 'attention_mask']



							def __init__(						self				,    lowercase_=80				,    lowercase_=16_000				,    lowercase_=0.0				,    lowercase_=10				,    lowercase_=25				,    lowercase_="hamming_window"				,    lowercase_=32_768.0				,    lowercase_=0.97				,    lowercase_=1.0				,    lowercase_=True				,    lowercase_=True				,    lowercase_=False				,    **lowercase_				,    ):
													super().__init__(feature_size=_SCREAMING_SNAKE_CASE				,    sampling_rate=_SCREAMING_SNAKE_CASE				,    padding_value=_SCREAMING_SNAKE_CASE				,    **_SCREAMING_SNAKE_CASE    )

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

													_snake_case						:					List[Any]    =   win_length * sampling_rate // 1_000
													_snake_case						:					Union[str, Any]    =   hop_length * sampling_rate // 1_000

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



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													if self.win_function == "hamming_window":
																			_snake_case						:					Union[str, Any]    =   window_function(window_length=self.sample_size				,    name=self.win_function				,    periodic=_SCREAMING_SNAKE_CASE    )
													else:
																			_snake_case						:					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						:					str    =   spectrogram(
													    one_waveform * self.frame_signal_scale				,    window=_SCREAMING_SNAKE_CASE				,    frame_length=self.sample_size				,    hop_length=self.sample_stride				,    fft_length=self.n_fft				,    center=_SCREAMING_SNAKE_CASE				,    preemphasis=self.preemphasis_coeff				,    mel_filters=_SCREAMING_SNAKE_CASE				,    mel_floor=self.mel_floor				,    log_mel="log"				,    )
													return msfc_features.T



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													# make sure we normalize float32 arrays
													if self.normalize_means:
																			_snake_case						:					Optional[Any]    =   x[:input_length].mean(axis=0    )
																			_snake_case						:					Union[str, Any]    =   np.subtract(_SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE    )
													if self.normalize_vars:
																			_snake_case						:					Dict    =   x[:input_length].std(axis=0    )
																			_snake_case						:					Optional[Any]    =   np.divide(_SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE    )

													if input_length < x.shape[0]:
																			_snake_case						:					Optional[Any]    =   padding_value

													# make sure array is in float32
													_snake_case						:					Tuple    =   x.astype(np.floataa    )

													return x



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					Any    =   attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
													return [self._normalize_one(_SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE				,    self.padding_value    ) for x, n in zip(_SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE    )]



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

													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						:					Any    =   isinstance(_SCREAMING_SNAKE_CASE				,    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						:					int    =   is_batched_numpy or (
													    isinstance(_SCREAMING_SNAKE_CASE				,    (list, tuple)    ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)    ))
													)

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

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

													# extract fbank features
													_snake_case						:					str    =   [self._extract_mfsc_features(_SCREAMING_SNAKE_CASE    ) for one_waveform in raw_speech]

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

													_snake_case						:					str    =   self.pad(
													    _SCREAMING_SNAKE_CASE				,    padding=_SCREAMING_SNAKE_CASE				,    max_length=_SCREAMING_SNAKE_CASE				,    truncation=_SCREAMING_SNAKE_CASE				,    pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,    return_attention_mask=_SCREAMING_SNAKE_CASE				,    **_SCREAMING_SNAKE_CASE				,    )
													# make sure list is in array format
													_snake_case						:					Any    =   padded_inputs.get("input_features"    )
													if isinstance(input_features[0]				,    _SCREAMING_SNAKE_CASE    ):
																			_snake_case						:					str    =   [np.asarray(_SCREAMING_SNAKE_CASE				,    dtype=np.floataa    ) for feature in input_features]

													_snake_case						:					Optional[int]    =   padded_inputs.get("attention_mask"    )
													if attention_mask is not None:
																			_snake_case						:					Optional[Any]    =   [np.asarray(_SCREAMING_SNAKE_CASE				,    dtype=np.intaa    ) for array in attention_mask]

													if self.normalize_means or self.normalize_vars:
																			_snake_case						:					Optional[int]    =   (
																			    np.array(_SCREAMING_SNAKE_CASE				,    dtype=np.intaa    )
																			    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE				,    max_length=_SCREAMING_SNAKE_CASE    ) is not PaddingStrategy.DO_NOT_PAD
																			    and padding
																			    else None
																			)
																			_snake_case						:					int    =   self.normalize(
																			    padded_inputs["input_features"]				,    attention_mask=_SCREAMING_SNAKE_CASE    )

													if return_tensors is not None:
																			_snake_case						:					Optional[Any]    =   padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE    )

													return padded_inputs
def  snake_case						(__lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Tuple    =   ""
						for word_or_phrase in separated:
												if not isinstance(__lowercase ,      __lowercase						):
																		raise Exception("join() accepts only strings to be joined"						)
												joined += word_or_phrase + separator
						return joined.strip(__lowercase						)


if __name__ == "__main__":
				from doctest import testmod

				testmod()