



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):

							'''simple docstring'''





							__lowerCamelCase	:	Tuple    =['input_features', 'attention_mask']





							def __init__(   self		:		Dict      ,				__lowercase		:		List[Any]=80      ,				__lowercase		:		Tuple=16000      ,				__lowercase		:		List[str]=80      ,				__lowercase		:		int=0.0      ,				__lowercase		:		str=True      ,				__lowercase		:		List[Any]=True      ,				__lowercase		:		Any=True      ,				**__lowercase		:		int      ,				):

											'''simple docstring'''
											super().__init__(feature_size=__lowercase      ,				sampling_rate=__lowercase      ,				padding_value=__lowercase      ,				**__lowercase	)
											__a       =		num_mel_bins
											__a       =		do_ceptral_normalize
											__a       =		normalize_means
											__a       =		normalize_vars
											__a       =		True





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		np.ndarray      ,				):

											'''simple docstring'''
											__a       =		waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
											__a       =		torch.from_numpy(__lowercase	).unsqueeze(0	)
											__a       =		ta_kaldi.fbank(__lowercase      ,				num_mel_bins=self.num_mel_bins      ,				sample_frequency=self.sampling_rate	)
											return features.numpy()





							@staticmethod
							def 						UpperCamelCase_					(   __lowercase		:		np.ndarray      ,				__lowercase		:		int      ,				__lowercase		:		Optional[bool] = True      ,				__lowercase		:		Optional[bool] = True      ,				__lowercase		:		float = 0.0      ,				):

											'''simple docstring'''







											if normalize_means:
															__a       =		x[:input_length].mean(axis=0	)
															__a       =		np.subtract(__lowercase      ,				__lowercase	)
											if normalize_vars:
															__a       =		x[:input_length].std(axis=0	)
															__a       =		np.divide(__lowercase      ,				__lowercase	)

											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		:		Dict      ,				__lowercase		:		List[np.ndarray]      ,				__lowercase		:		Optional[np.ndarray] = None	):

											'''simple docstring'''
											__a       =		attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [
											    self.utterance_cmvn(__lowercase      ,				__lowercase      ,				self.normalize_means      ,				self.normalize_vars      ,				self.padding_value	)
											    for x, n in zip(__lowercase      ,				__lowercase	)
											]







							def __call__(   self		:		Any      ,				__lowercase		:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,				__lowercase		:		Union[bool, str, PaddingStrategy] = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[bool] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''







											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(__lowercase      ,				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(__lowercase      ,				(list, tuple)	) and (isinstance(raw_speech[0]      ,				(np.ndarray, tuple, list)	))
											)

											if is_batched:
															__a       =		[np.asarray(__lowercase      ,				dtype=np.floataa	) for speech in raw_speech]
											elif not is_batched and not isinstance(__lowercase      ,				np.ndarray	):
															__a       =		np.asarray(__lowercase      ,				dtype=np.floataa	)
											elif isinstance(__lowercase      ,				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_fbank_features(__lowercase	) for waveform in raw_speech]

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

											__a       =		self.pad(
											    __lowercase      ,				padding=__lowercase      ,				max_length=__lowercase      ,				truncation=__lowercase      ,				pad_to_multiple_of=__lowercase      ,				return_attention_mask=__lowercase      ,				**__lowercase      ,				)

											# make sure list is in array format
											__a       =		padded_inputs.get("""input_features"""	)
											if isinstance(input_features[0]      ,				__lowercase	):
															__a       =		[np.asarray(__lowercase      ,				dtype=np.floataa	) for feature in input_features]

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

											# Utterance-level cepstral mean and variance normalization
											if self.do_ceptral_normalize:
															__a       =		(
															    np.array(__lowercase      ,				dtype=np.intaa	)
															    if self._get_padding_strategies(__lowercase      ,				max_length=__lowercase	) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															__a       =		self.normalize(
															    padded_inputs["""input_features"""]      ,				attention_mask=__lowercase	)

											if return_tensors is not None:
															__a       =		padded_inputs.convert_to_tensors(__lowercase	)

											return padded_inputs





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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Iterable[str]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		iter(_SCREAMING_SNAKE_CASE    )
				while True:
								__a       =		tuple(itertools.islice(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    )
								if not chunk:
												return
								yield chunk

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""""".join([c.upper() for c in dirty if c in string.ascii_letters]    )
				__a       =		""""""

				if len(_SCREAMING_SNAKE_CASE    ) < 2:
								return dirty

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

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

				clean += dirty[-1]

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

				return clean

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""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
				__a       =		[]

				# 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(_SCREAMING_SNAKE_CASE    )

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

				return table

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		prepare_input(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(_SCREAMING_SNAKE_CASE				,     2    ):
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

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