




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


_lowerCamelCase      :  List[Any]      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     ["input_features", "attention_mask"]



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		int=80		,	_UpperCAmelCase      :		List[Any]=1_6000		,	_UpperCAmelCase      :		Optional[Any]=0.0		,	_UpperCAmelCase      :		List[Any]=10		,	_UpperCAmelCase      :		List[Any]=25		,	_UpperCAmelCase      :		List[Any]="hamming_window"		,	_UpperCAmelCase      :		int=32768.0		,	_UpperCAmelCase      :		Optional[int]=0.97		,	_UpperCAmelCase      :		List[Any]=1.0		,	_UpperCAmelCase      :		Dict=True		,	_UpperCAmelCase      :		Any=True		,	_UpperCAmelCase      :		List[Any]=False		,	**_UpperCAmelCase      :		str		,	)  ->    Optional[Any]:





								'''simple docstring'''




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

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

								_lowerCAmelCase    :    Dict										=      win_length * sampling_rate // 1000
								_lowerCAmelCase    :    Tuple										=      hop_length * sampling_rate // 1000

								_lowerCAmelCase    :    Tuple										=      optimal_fft_length(self.sample_size      )
								_lowerCAmelCase    :    Optional[int]										=      (self.n_fft // 2) + 1



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		np.array      )  ->    np.ndarray:





								'''simple docstring'''




								if self.win_function == "hamming_window":
												_lowerCAmelCase    :    Union[str, Any]										=      window_function(window_length=self.sample_size		,	name=self.win_function		,	periodic=_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    Optional[int]										=      window_function(window_length=self.sample_size		,	name=self.win_function      )

								_lowerCAmelCase    :    Optional[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		,	)

								_lowerCAmelCase    :    List[Any]										=      spectrogram(
								    one_waveform * self.frame_signal_scale		,	window=_UpperCAmelCase		,	frame_length=self.sample_size		,	hop_length=self.sample_stride		,	fft_length=self.n_fft		,	center=_UpperCAmelCase		,	preemphasis=self.preemphasis_coeff		,	mel_filters=_UpperCAmelCase		,	mel_floor=self.mel_floor		,	log_mel="""log"""		,	)
								return msfc_features.T



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[Any]      )  ->    List[str]:





								'''simple docstring'''




								if self.normalize_means:
												_lowerCAmelCase    :    List[str]										=      x[:input_length].mean(axis=0      )
												_lowerCAmelCase    :    Dict										=      np.subtract(_UpperCAmelCase		,	_UpperCAmelCase      )
								if self.normalize_vars:
												_lowerCAmelCase    :    Dict										=      x[:input_length].std(axis=0      )
												_lowerCAmelCase    :    Union[str, Any]										=      np.divide(_UpperCAmelCase		,	_UpperCAmelCase      )

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

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

								return x



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[np.ndarray]		,	_UpperCAmelCase      :		Optional[np.ndarray] = None      )  ->    List[np.ndarray]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
								return [self._normalize_one(_UpperCAmelCase		,	_UpperCAmelCase		,	self.padding_value      ) for x, n in zip(_UpperCAmelCase		,	_UpperCAmelCase      )]



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





								'''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."""      )

								_lowerCAmelCase    :    Optional[Any]										=      isinstance(_UpperCAmelCase		,	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    :    Tuple										=      is_batched_numpy or (
								    isinstance(_UpperCAmelCase		,	(list, tuple)      ) and (isinstance(raw_speech[0]		,	(np.ndarray, tuple, list)      ))
								)

								if is_batched:
												_lowerCAmelCase    :    Union[str, Any]										=      [np.asarray(_UpperCAmelCase		,	dtype=np.floataa      ) for speech in raw_speech]
								elif not is_batched and not isinstance(_UpperCAmelCase		,	np.ndarray      ):
												_lowerCAmelCase    :    str										=      np.asarray(_UpperCAmelCase		,	dtype=np.floataa      )
								elif isinstance(_UpperCAmelCase		,	np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
												_lowerCAmelCase    :    List[str]										=      raw_speech.astype(np.floataa      )

								# always return batch
								if not is_batched:
												_lowerCAmelCase    :    Optional[int]										=      [raw_speech]

								# extract fbank features
								_lowerCAmelCase    :    Optional[int]										=      [self._extract_mfsc_features(_UpperCAmelCase      ) for one_waveform in raw_speech]

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

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

								_lowerCAmelCase    :    Tuple										=      padded_inputs.get("""attention_mask"""      )
								if attention_mask is not None:
												_lowerCAmelCase    :    Union[str, Any]										=      [np.asarray(_UpperCAmelCase		,	dtype=np.intaa      ) for array in attention_mask]

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

								if return_tensors is not None:
												_lowerCAmelCase    :    Dict										=      padded_inputs.convert_to_tensors(_UpperCAmelCase      )

								return padded_inputs







from __future__ import annotations
class        __snake_case  :



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int = 0      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      key



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      key or self.__key or 1

								# make sure key is an appropriate size
								key %= 255

								return [chr(ord(_UpperCAmelCase      ) ^ key      ) for ch in content]



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      key or self.__key or 1

								# make sure key is an appropriate size
								key %= 255

								return [chr(ord(_UpperCAmelCase      ) ^ key      ) for ch in content]



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      key or self.__key or 1

								# make sure key can be any size
								while key > 255:
												key -= 255

								# This will be returned
								_lowerCAmelCase    :    List[str]										=      """"""

								for ch in content:
												ans += chr(ord(_UpperCAmelCase      ) ^ key      )

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      key or self.__key or 1

								# make sure key can be any size
								while key > 255:
												key -= 255

								# This will be returned
								_lowerCAmelCase    :    List[Any]										=      """"""

								for ch in content:
												ans += chr(ord(_UpperCAmelCase      ) ^ key      )

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    bool:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								try:
												with open(_UpperCAmelCase      ) as fin, open("""encrypt.out"""		,	"""w+"""      ) as fout:
																# actual encrypt-process
																for line in fin:
																				fout.write(self.encrypt_string(_UpperCAmelCase		,	_UpperCAmelCase      )      )

								except OSError:
												return False

								return True



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    bool:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								try:
												with open(_UpperCAmelCase      ) as fin, open("""decrypt.out"""		,	"""w+"""      ) as fout:
																# actual encrypt-process
																for line in fin:
																				fout.write(self.decrypt_string(_UpperCAmelCase		,	_UpperCAmelCase      )      )

								except OSError:
												return False

								return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

