






"""simple docstring"""





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



def   a    (     __UpperCAmelCase      :    Iterable[str]      ,   __UpperCAmelCase      :    int  )    ->							Generator[tuple[str, ...], None, None]:
						__magic_name__:	Optional[Any]							=       iter(__UpperCAmelCase  )
						while True:
												__magic_name__:	Tuple							=       tuple(itertools.islice(__UpperCAmelCase      ,   __UpperCAmelCase  )  )
												if not chunk:
																		return
												yield chunk



def   a    (     __UpperCAmelCase      :    str  )    ->							str:
						__magic_name__:	str							=       """""".join([c.upper() for c in dirty if c in string.ascii_letters]  )
						__magic_name__:	Optional[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]:
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						__magic_name__:	Dict							=       """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
						__magic_name__:	List[str]							=       []

						# 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:
						__magic_name__:	Optional[Any]							=       generate_table(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       prepare_input(__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       """"""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(__UpperCAmelCase      ,   2  ):
												__magic_name__,			__magic_name__:	List[Any]							=       divmod(table.index(__UpperCAmelCase  )      ,   5  )
												__magic_name__,			__magic_name__:	Tuple							=       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:
						__magic_name__:	int							=       generate_table(__UpperCAmelCase  )
						__magic_name__:	Tuple							=       """"""

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




"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['input_features', 'is_longer']





			def __init__(			self   ,      snake_case_=64   ,      snake_case_=48_000   ,      snake_case_=480   ,      snake_case_=10   ,      snake_case_=1_024   ,      snake_case_=0.0   ,      snake_case_=False   ,      snake_case_ = 0   ,      snake_case_ = 14_000   ,      snake_case_ = None   ,      snake_case_ = "fusion"   ,      snake_case_ = "repeatpad"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										A__       :     List[str]								=			top_db
										A__       :     Optional[int]								=			truncation
										A__       :     str								=			padding
										A__       :     str								=			fft_window_size
										A__       :     Any								=			(fft_window_size >> 1) + 1
										A__       :     Optional[Any]								=			hop_length
										A__       :     Any								=			max_length_s
										A__       :     int								=			max_length_s * sampling_rate
										A__       :     Any								=			sampling_rate
										A__       :     List[str]								=			frequency_min
										A__       :     int								=			frequency_max
										A__       :     Tuple								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm=snake_case_   ,      mel_scale="""htk"""   ,      )
										A__       :     List[Any]								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm="""slaney"""   ,      mel_scale="""slaney"""   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			copy.deepcopy(self.__dict__							)
										A__       :     int								=			self.__class__.__name__
										if "mel_filters" in output:
																	del output["mel_filters"]
										if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
										return output





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			spectrogram(
										    snake_case_   ,      window_function(self.fft_window_size   ,      """hann"""							)   ,      frame_length=self.fft_window_size   ,      hop_length=self.hop_length   ,      power=2.0   ,      mel_filters=snake_case_   ,      log_mel="""dB"""   ,      )
										return log_mel_spectrogram.T





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			np.array_split(list(range(0   ,      total_frames - chunk_frames + 1							)							)   ,      3							)
										if len(ranges[1]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     Optional[Any]								=			[0]
										if len(ranges[2]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     str								=			[0]
										# randomly choose index for each part
										A__       :     int								=			np.random.choice(ranges[0]							)
										A__       :     Dict								=			np.random.choice(ranges[1]							)
										A__       :     Dict								=			np.random.choice(ranges[2]							)

										A__       :     Any								=			mel[idx_front : idx_front + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_middle : idx_middle + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_back : idx_back + chunk_frames, :]

										A__       :     Dict								=			torch.tensor(mel[None, None, :]							)
										A__       :     Dict								=			torch.nn.functional.interpolate(
										    snake_case_   ,      size=[chunk_frames, 64]   ,      mode="""bilinear"""   ,      align_corners=snake_case_							)
										A__       :     List[Any]								=			mel_shrink[0][0].numpy()
										A__       :     Dict								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,      axis=0							)
										return mel_fusion





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																								A__       :     int								=			True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								A__       :     Tuple								=			len(snake_case_							) - max_length
																								A__       :     Any								=			np.random.randint(0   ,      overflow + 1							)
																								A__       :     int								=			waveform[idx : idx + max_length]
																								A__       :     List[str]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]
																	elif truncation == "fusion":
																								A__       :     Optional[int]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     Optional[int]								=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								A__       :     int								=			mel.shape[0]
																								if chunk_frames == total_frames:
																															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																															# In this case, we just use the whole audio.
																															A__       :     Union[str, Any]								=			np.stack([mel, mel, mel, mel]   ,      axis=0							)
																															A__       :     Dict								=			False
																								else:
																															A__       :     List[Any]								=			self._random_mel_fusion(snake_case_   ,      snake_case_   ,      snake_case_							)
																															A__       :     Optional[Any]								=			True
																	else:
																								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

										else:
																	A__       :     Tuple								=			False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																								if padding == "repeat":
																															A__       :     Union[str, Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     List[Any]								=			np.stack(np.tile(snake_case_   ,      n_repeat + 1							)							)[:max_length]
																								if padding == "repeatpad":
																															A__       :     List[Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     Tuple								=			np.stack(np.tile(snake_case_   ,      snake_case_							)							)
																								A__       :     Tuple								=			np.pad(snake_case_   ,      (0, max_length - waveform.shape[0])   ,      mode="""constant"""   ,      constant_values=0							)

																	if truncation == "fusion":
																								A__       :     str								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     List[Any]								=			np.stack([input_mel, input_mel, input_mel, input_mel]   ,      axis=0							)
																	else:
																								A__       :     List[Any]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]

										return input_mel, longer





			def __call__(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			truncation if truncation is not None else self.truncation
										A__       :     Union[str, Any]								=			padding if padding else self.padding

										if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																								    F''' was sampled 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."""							)

										A__       :     List[Any]								=			isinstance(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}'''							)
										A__       :     int								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

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

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_							)]

										# convert to mel spectrogram, truncate and pad if needed.
										A__       :     Optional[Any]								=			[
										    self._get_input_mel(snake_case_   ,      max_length if max_length else self.nb_max_samples   ,      snake_case_   ,      snake_case_							)
										    for waveform in raw_speech
										]

										A__       :     List[str]								=			[]
										A__       :     Optional[Any]								=			[]
										for mel, longer in padded_inputs:
																	input_mel.append(snake_case_							)
																	is_longer.append(snake_case_							)

										if truncation == "fusion" and sum(snake_case_							) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	A__       :     Optional[Any]								=			np.random.randint(0   ,      len(snake_case_							)							)
																	A__       :     str								=			True

										if isinstance(input_mel[0]   ,      snake_case_							):
																	A__       :     Optional[Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_mel]

										# is_longer is a list of bool
										A__       :     Union[str, Any]								=			[[longer] for longer in is_longer]

										A__       :     str								=			{"""input_features""": input_mel, """is_longer""": is_longer}
										A__       :     Optional[int]								=			BatchFeature(snake_case_							)

										if return_tensors is not None:
																	A__       :     Dict								=			input_features.convert_to_tensors(snake_case_							)

										return input_features





