

"""simple docstring"""






import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase__ :     int							  =     logging.get_logger(__name__)






class 			_UpperCAmelCase	(						_UpperCAmelCase):
			__a       :				Union[str, Any]     =							["""input_values""", """attention_mask"""]
			def __init__(						self					,						_A = 1					,						_A = 1_60_00					,						_A = 0.0					,						_A = False					,						_A = 80					,						_A = 16					,						_A = 64					,						_A = "hann_window"					,						_A = 1.0					,						_A = 80					,						_A = 76_00					,						_A = 1e-10					,						_A = 2					,						_A = True					,						**_A					,						)			->			List[str]:






				'''simple docstring'''







				super().__init__(feature_size=_A					,						sampling_rate=_A					,						padding_value=_A					,						**_A   )
				_UpperCAmelCase			:							List[str]	  =    do_normalize
				_UpperCAmelCase			:							Any	  =    return_attention_mask

				_UpperCAmelCase			:							List[Any]	  =    num_mel_bins
				_UpperCAmelCase			:							Tuple	  =    hop_length
				_UpperCAmelCase			:							Tuple	  =    win_length
				_UpperCAmelCase			:							Optional[Any]	  =    win_function
				_UpperCAmelCase			:							Union[str, Any]	  =    frame_signal_scale
				_UpperCAmelCase			:							Dict	  =    fmin
				_UpperCAmelCase			:							int	  =    fmax
				_UpperCAmelCase			:							str	  =    mel_floor
				_UpperCAmelCase			:							Dict	  =    reduction_factor

				_UpperCAmelCase			:							Tuple	  =    win_length * sampling_rate // 10_00
				_UpperCAmelCase			:							int	  =    hop_length * sampling_rate // 10_00
				_UpperCAmelCase			:							Optional[int]	  =    optimal_fft_length(self.sample_size   )
				_UpperCAmelCase			:							Optional[Any]	  =    (self.n_fft // 2) + 1

				_UpperCAmelCase			:							Any	  =    window_function(window_length=self.sample_size					,						name=self.win_function					,						periodic=_A   )

				_UpperCAmelCase			:							List[Any]	  =    mel_filter_bank(
				    num_frequency_bins=self.n_freqs					,						num_mel_filters=self.num_mel_bins					,						min_frequency=self.fmin					,						max_frequency=self.fmax					,						sampling_rate=self.sampling_rate					,						norm="""slaney"""					,						mel_scale="""slaney"""					,						)

				if frame_signal_scale != 1.0:
					warnings.warn(
					    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""					,						_A					,						)
				if reduction_factor != 2.0:
					warnings.warn(
					    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""					,						_A					,						)
			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							__snake_case       (						_A					,						_A					,						_A = 0.0   )			->			List[np.ndarray]:






				'''simple docstring'''







				if attention_mask is not None:
					_UpperCAmelCase			:							Optional[Any]	  =    np.array(_A					,						np.intaa   )
					_UpperCAmelCase			:							List[Any]	  =    []

					for vector, length in zip(_A					,						attention_mask.sum(-1   )   ):
						_UpperCAmelCase			:							Optional[Any]	  =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
						if length < normed_slice.shape[0]:
							_UpperCAmelCase			:							Dict	  =    padding_value

						normed_input_values.append(_A   )
				else:
					_UpperCAmelCase			:							Union[str, Any]	  =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

				return normed_input_values
			def 							__snake_case       (						self					,						_A					,						)			->			np.ndarray:






				'''simple docstring'''







				_UpperCAmelCase			:							Any	  =    spectrogram(
				    _A					,						window=self.window					,						frame_length=self.sample_size					,						hop_length=self.sample_stride					,						fft_length=self.n_fft					,						mel_filters=self.mel_filters					,						mel_floor=self.mel_floor					,						log_mel="""log10"""					,						)
				return log_mel_spec.T
			def __call__(						self					,						_A = None					,						_A = None					,						_A = False					,						_A = None					,						_A = False					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						**_A					,						)			->			BatchFeature:






				'''simple docstring'''







				if audio is None and audio_target is None:
					raise ValueError("""You must provide either `audio` or `audio_target` values."""   )

				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 audio 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."""   )

				if audio is not None:
					_UpperCAmelCase			:							Optional[int]	  =    self._process_audio(
					    _A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						**_A					,						)
				else:
					_UpperCAmelCase			:							Optional[Any]	  =    None

				if audio_target is not None:
					_UpperCAmelCase			:							str	  =    self._process_audio(
					    _A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						**_A					,						)

					if inputs is None:
						return inputs_target
					else:
						_UpperCAmelCase			:							int	  =    inputs_target["""input_values"""]
						_UpperCAmelCase			:							Any	  =    inputs_target.get("""attention_mask"""   )
						if decoder_attention_mask is not None:
							_UpperCAmelCase			:							str	  =    decoder_attention_mask

				return inputs
			def 							__snake_case       (						self					,						_A					,						_A = False					,						_A = False					,						_A = None					,						_A = False					,						_A = None					,						_A = None					,						_A = None					,						**_A					,						)			->			BatchFeature:






				'''simple docstring'''







				_UpperCAmelCase			:							Union[str, Any]	  =    isinstance(_A					,						np.ndarray   ) and len(speech.shape   ) > 1
				if is_batched_numpy and len(speech.shape   ) > 2:
					raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''   )
				_UpperCAmelCase			:							int	  =    is_batched_numpy or (
				    isinstance(_A					,						(list, tuple)   ) and (isinstance(speech[0]					,						(np.ndarray, tuple, list)   ))
				)

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

				# always return batch
				if not is_batched:
					_UpperCAmelCase			:							Optional[Any]	  =    [speech]

				# needed to make pad() work on spectrogram inputs
				_UpperCAmelCase			:							Dict	  =    self.feature_size

				# convert into correct format for padding
				if is_target:
					_UpperCAmelCase			:							Tuple	  =    [self._extract_mel_features(_A   ) for waveform in speech]
					_UpperCAmelCase			:							List[str]	  =    BatchFeature({"""input_values""": features}   )
					_UpperCAmelCase			:							Union[str, Any]	  =    self.num_mel_bins
				else:
					_UpperCAmelCase			:							Tuple	  =    BatchFeature({"""input_values""": speech}   )

				_UpperCAmelCase			:							Tuple	  =    self.pad(
				    _A					,						padding=_A					,						max_length=_A					,						truncation=_A					,						pad_to_multiple_of=_A					,						return_attention_mask=_A					,						**_A					,						)

				_UpperCAmelCase			:							Dict	  =    feature_size_hack

				# convert input values to correct format
				_UpperCAmelCase			:							Optional[int]	  =    padded_inputs["""input_values"""]
				if not isinstance(input_values[0]					,						np.ndarray   ):
					_UpperCAmelCase			:							Tuple	  =    [np.asarray(_A					,						dtype=np.floataa   ) for array in input_values]
				elif (
				    not isinstance(_A					,						np.ndarray   )
				    and isinstance(input_values[0]					,						np.ndarray   )
				    and input_values[0].dtype is np.dtype(np.floataa   )
				):
					_UpperCAmelCase			:							List[str]	  =    [array.astype(np.floataa   ) for array in input_values]
				elif isinstance(_A					,						np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
					_UpperCAmelCase			:							List[str]	  =    input_values.astype(np.floataa   )

				# convert attention_mask to correct format
				_UpperCAmelCase			:							List[Any]	  =    padded_inputs.get("""attention_mask"""   )
				if attention_mask is not None:
					_UpperCAmelCase			:							List[str]	  =    [np.asarray(_A					,						dtype=np.intaa   ) for array in attention_mask]

				# zero-mean and unit-variance normalization
				if not is_target and self.do_normalize:
					_UpperCAmelCase			:							Tuple	  =    (
					    attention_mask
					    if self._get_padding_strategies(_A					,						max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
					    else None
					)
					_UpperCAmelCase			:							int	  =    self.zero_mean_unit_var_norm(
					    padded_inputs["""input_values"""]					,						attention_mask=_A					,						padding_value=self.padding_value   )

				if return_tensors is not None:
					_UpperCAmelCase			:							Optional[int]	  =    padded_inputs.convert_to_tensors(_A   )

				return padded_inputs
			def 							__snake_case       (						self   )			->			Dict[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							List[Any]	  =    super().to_dict()

				# Don't serialize these as they are derived from the other properties.
				_UpperCAmelCase			:							Union[str, Any]	  =    ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
				for name in names:
					if name in output:
						del output[name]

				return output












"""simple docstring"""





SCREAMING_SNAKE_CASE__					=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__					=   {value: key for key, value in MORSE_CODE_DICT.items()}


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return " ".join(MORSE_CODE_DICT[char] for char in message.upper()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return "".join(REVERSE_DICT[char] for char in message.split()			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """Morse code here!"""
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       encrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       decrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       main()






