


"""simple docstring"""






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


UpperCAmelCase    :     str   						=		logging.get_logger(__name__)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							["""audio_values""", """audio_mask"""]






					def __init__(       self    : List[Any] ,       UpperCamelCase    : Any=2_048 ,       UpperCamelCase    : str=1 ,       UpperCamelCase    : Optional[Any]=[16, 16] ,       UpperCamelCase    : List[Any]=128 ,       UpperCamelCase    : str=44_100 ,       UpperCamelCase    : Union[str, Any]=86 ,       UpperCamelCase    : Any=2_048 ,       UpperCamelCase    : Optional[int]=0.0 ,       **UpperCamelCase    : Dict ,       ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Dict      							=					spectrogram_length
												__UpperCAmelCase   :						Tuple      							=					num_channels
												__UpperCAmelCase   :						Union[str, Any]      							=					patch_size
												__UpperCAmelCase   :						Dict      							=					feature_size // self.patch_size[1]
												__UpperCAmelCase   :						Union[str, Any]      							=					n_fft
												__UpperCAmelCase   :						Optional[int]      							=					sampling_rate // hop_length_to_sampling_rate
												__UpperCAmelCase   :						str      							=					sampling_rate
												__UpperCAmelCase   :						Dict      							=					padding_value
												__UpperCAmelCase   :						Tuple      							=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2 ,       num_mel_filters=UpperCamelCase ,       min_frequency=0.0 ,       max_frequency=22050.0 ,       sampling_rate=UpperCamelCase ,       norm="""slaney""" ,       mel_scale="""slaney""" ,       ).T






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : np.array  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					spectrogram(
												    UpperCamelCase ,       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 ,       )
												__UpperCAmelCase   :						int      							=					log_spec[:, :-1]
												__UpperCAmelCase   :						List[str]      							=					log_spec - 20.0
												__UpperCAmelCase   :						List[Any]      							=					np.clip(log_spec / 40.0 ,       -2.0 ,       0.0  ) + 1.0
												return log_spec






					def __call__(       self    : Any ,       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    : List[str] ,       ):
												'''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."""  )

												__UpperCAmelCase   :						Optional[int]      							=					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}'''  )
												__UpperCAmelCase   :						Optional[Any]      							=					is_batched_numpy or (
												    isinstance(UpperCamelCase ,       (list, tuple)  ) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)  ))
												)
												if is_batched:
																			__UpperCAmelCase   :						Tuple      							=					[np.asarray([speech] ,       dtype=np.floataa  ).T for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase ,       np.ndarray  ):
																			__UpperCAmelCase   :						int      							=					np.asarray(UpperCamelCase ,       dtype=np.floataa  )
												elif isinstance(UpperCamelCase ,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
																			__UpperCAmelCase   :						Optional[int]      							=					raw_speech.astype(np.floataa  )
												# always return batch
												if not is_batched:
																			__UpperCAmelCase   :						Union[str, Any]      							=					[np.asarray([raw_speech]  ).T]

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

												# Create audio attention mask
												__UpperCAmelCase   :						str      							=					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:
																			__UpperCAmelCase   :						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
																			]
																			__UpperCAmelCase   :						Optional[int]      							=					np.array(UpperCamelCase  ).astype(np.floataa  )

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

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

												__UpperCAmelCase   :						Union[str, Any]      							=					BatchFeature(data=UpperCamelCase ,       tensor_type=UpperCamelCase  )
												return encoded_inputs




"""simple docstring"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
