




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


_lowerCamelCase      :  List[Any]      						=						logging.get_logger(__name__)
class        __snake_case  (lowercase__		):
				lowerCAmelCase__				=     ["audio_values", "audio_mask"]



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





								'''simple docstring'''




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

								_lowerCAmelCase    :    Dict										=      spectrogram_length
								_lowerCAmelCase    :    List[str]										=      num_channels
								_lowerCAmelCase    :    str										=      patch_size
								_lowerCAmelCase    :    Dict										=      feature_size // self.patch_size[1]
								_lowerCAmelCase    :    List[Any]										=      n_fft
								_lowerCAmelCase    :    str										=      sampling_rate // hop_length_to_sampling_rate
								_lowerCAmelCase    :    Optional[int]										=      sampling_rate
								_lowerCAmelCase    :    Tuple										=      padding_value
								_lowerCAmelCase    :    Dict										=      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       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		np.array      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[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		,	)
								_lowerCAmelCase    :    str										=      log_spec[:, :-1]
								_lowerCAmelCase    :    str										=      log_spec - 20.0
								_lowerCAmelCase    :    int										=      np.clip(log_spec / 40.0		,	-2.0		,	0.0      ) + 1.0
								return log_spec



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





								'''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."""      )

								_lowerCAmelCase    :    str										=      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}"      )
								_lowerCAmelCase    :    Any										=      is_batched_numpy or (
								    isinstance(__lowerCamelCase		,	(list, tuple)      ) and (isinstance(raw_speech[0]		,	(np.ndarray, tuple, list)      ))
								)
								if is_batched:
												_lowerCAmelCase    :    List[str]										=      [np.asarray([speech]		,	dtype=np.floataa      ).T for speech in raw_speech]
								elif not is_batched and not isinstance(__lowerCamelCase		,	np.ndarray      ):
												_lowerCAmelCase    :    Optional[Any]										=      np.asarray(__lowerCamelCase		,	dtype=np.floataa      )
								elif isinstance(__lowerCamelCase		,	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    :    int										=      [np.asarray([raw_speech]      ).T]

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

								# Create audio attention mask
								_lowerCAmelCase    :    Any										=      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    :    Optional[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
												]
												_lowerCAmelCase    :    Tuple										=      np.array(__lowerCamelCase      ).astype(np.floataa      )

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

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

								_lowerCAmelCase    :    Any										=      BatchFeature(data=__lowerCamelCase		,	tensor_type=__lowerCamelCase      )
								return encoded_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")

