

from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


a_						     =    logging.get_logger(__name__)






class _UpperCamelCase							(							__lowerCAmelCase   ):
			'''simple docstring'''







			lowerCamelCase__           =['''audio_values''', '''audio_mask''']
			def __init__(			self							: Optional[int]      ,      a							: Tuple=2048      ,      a							: Union[str, Any]=1      ,      a							: List[str]=[16, 16]      ,      a							: Union[str, Any]=128      ,      a							: Tuple=4_4100      ,      a							: Optional[int]=86      ,      a							: Optional[Any]=2048      ,      a							: Dict=0.0      ,      **a							: List[str]      ,      )							->       Tuple:

				"""simple docstring"""

				super().__init__(
				    feature_size=lowerCAmelCase_      ,      sampling_rate=lowerCAmelCase_      ,      padding_value=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       spectrogram_length
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       feature_size // self.patch_size[1]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_fft
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       sampling_rate // hop_length_to_sampling_rate
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sampling_rate
				SCREAMING_SNAKE_CASE   :      List[str]				     =       padding_value
				SCREAMING_SNAKE_CASE   :      Tuple				     =       mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2      ,      num_mel_filters=lowerCAmelCase_      ,      min_frequency=0.0      ,      max_frequency=2_2050.0      ,      sampling_rate=lowerCAmelCase_      ,      norm="slaney"      ,      mel_scale="slaney"      ,      ).T
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Optional[int]	)							->       np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       spectrogram(
				    lowerCAmelCase_      ,      window_function(self.n_fft      ,      "hann"	)      ,      frame_length=self.n_fft      ,      hop_length=self.hop_length      ,      power=2.0      ,      mel_filters=self.mel_filters.T      ,      log_mel="dB"      ,      db_range=80.0      ,      )
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       log_spec[:, :-1]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       log_spec - 20.0
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       np.clip(log_spec / 40.0      ,      -2.0      ,      0.0	) + 1.0
				return log_spec






			def __call__(			self							: str      ,      a							: Optional[Any]      ,      a							: List[Any] = None      ,      a							: Optional[int] = True      ,      a							: int = None      ,      a							: Union[str, Any] = False      ,      a							: str = False      ,      **a							: Optional[int]      ,      )							->       BatchFeature:

				"""simple docstring"""

				if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
						raise ValueError(
						    "This feature extractor is set to support sampling rate"
						    F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
						    F" with {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."	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       isinstance(lowerCAmelCase_      ,      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}"	)
				SCREAMING_SNAKE_CASE   :      Any				     =       is_batched_numpy or (
				    isinstance(lowerCAmelCase_      ,      (list, tuple)	) and (isinstance(raw_speech[0]      ,      (np.ndarray, tuple, list)	))
				)
				if is_batched:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       [np.asarray([speech]      ,      dtype=np.floataa	).T for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCAmelCase_      ,      np.ndarray	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       np.asarray(lowerCAmelCase_      ,      dtype=np.floataa	)
				elif isinstance(lowerCAmelCase_      ,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       raw_speech.astype(np.floataa	)
				# always return batch
				if not is_batched:
					SCREAMING_SNAKE_CASE   :      Dict				     =       [np.asarray([raw_speech]	).T]

				# Convert audio signals to log mel spectrograms, truncate by time axis
				SCREAMING_SNAKE_CASE   :      Dict				     =       [
				    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
				]
				if isinstance(audio_features[0]      ,      lowerCAmelCase_	):
					SCREAMING_SNAKE_CASE   :      str				     =       [np.asarray(lowerCAmelCase_      ,      dtype=np.floataa	) for feature in audio_features]

				# Create audio attention mask
				SCREAMING_SNAKE_CASE   :      Tuple				     =       max(
				    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
				if return_attention_mask:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       [
					    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
					    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
					    for feature in audio_features
					]
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.array(lowerCAmelCase_	).astype(np.floataa	)

				# convert into correct format for padding
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
				SCREAMING_SNAKE_CASE   :      str				     =       np.ones([len(lowerCAmelCase_	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       padded_audio_features * self.padding_value
				for i in range(len(lowerCAmelCase_	)	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       audio_features[i]
					SCREAMING_SNAKE_CASE   :      Dict				     =       feature

				# return as BatchFeature
				if return_attention_mask:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       {"audio_values": padded_audio_features, "audio_mask": audio_mask}
				else:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       {"audio_values": padded_audio_features}

				SCREAMING_SNAKE_CASE   :      str				     =       BatchFeature(data=lowerCAmelCase_      ,      tensor_type=lowerCAmelCase_	)
				return encoded_inputs



from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


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

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)