



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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( _UpperCAmelCase      ):
			UpperCamelCase					:Any     =			['''audio_values''', '''audio_mask''']





			def __init__(self							,    __magic_name__=2048							,    __magic_name__=1							,    __magic_name__=[16, 16]							,    __magic_name__=128							,    __magic_name__=44100							,    __magic_name__=86							,    __magic_name__=2048							,    __magic_name__=0.0							,    **__magic_name__							,    ):
						super().__init__(
						    feature_size=A_							,    sampling_rate=A_							,    padding_value=A_							,    **A_							,    )

						lowerCamelCase__ :      Union[str, Any]								=		spectrogram_length
						lowerCamelCase__ :      Optional[int]								=		num_channels
						lowerCamelCase__ :      Any								=		patch_size
						lowerCamelCase__ :      Tuple								=		feature_size // self.patch_size[1]
						lowerCamelCase__ :      Optional[int]								=		n_fft
						lowerCamelCase__ :      str								=		sampling_rate // hop_length_to_sampling_rate
						lowerCamelCase__ :      str								=		sampling_rate
						lowerCamelCase__ :      int								=		padding_value
						lowerCamelCase__ :      Tuple								=		mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2							,    num_mel_filters=A_							,    min_frequency=0.0							,    max_frequency=22050.0							,    sampling_rate=A_							,    norm="""slaney"""							,    mel_scale="""slaney"""							,    ).T





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      str								=		spectrogram(
						    A_							,    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							,    )
						lowerCamelCase__ :      List[Any]								=		log_spec[:, :-1]
						lowerCamelCase__ :      List[Any]								=		log_spec - 20.0
						lowerCamelCase__ :      str								=		np.clip(log_spec / 40.0							,    -2.0							,    0.0				) + 1.0
						return log_spec





			def __call__(self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = False							,    **__magic_name__							,    ):

						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."""				)

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

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

						# Create audio attention mask
						lowerCamelCase__ :      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:
									lowerCamelCase__ :      str								=		[
									    (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
									]
									lowerCamelCase__ :      int								=		np.array(A_				).astype(np.floataa				)

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

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

						lowerCamelCase__ :      int								=		BatchFeature(data=A_							,    tensor_type=A_				)
						return encoded_inputs








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



