
import numpy as np

lowercase  : Dict						   =							[
    ["""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 __snake_case		:
    def __init__(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				np.array(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				np.where(letter == self.SQUARE				)
           lowercase							:					List[str]	     =				np.concatenate([indexa + 1, indexa + 1]				)
           return indexes
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.SQUARE[indexa - 1, indexa - 1]
           return letter
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				message.lower()
           lowercase							:					List[Any]	     =				message.replace(""" """		,""""""				)
           lowercase							:					Dict	     =				message.replace("""j"""		,"""i"""				)

           lowercase							:					Tuple	     =				np.empty((2, len(snake_case				))				)
           for letter_index in range(len(snake_case				)				):
                  lowercase							:					int	     =				self.letter_to_numbers(message[letter_index]				)

                  lowercase							:					Optional[int]	     =				numbers[0]
                  lowercase							:					List[str]	     =				numbers[1]

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

           return encoded_message
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				message.lower()
           message.replace(""" """		,""""""				)
           lowercase							:					str	     =				np.empty(2 * len(snake_case				)				)
           for letter_index in range(len(snake_case				)				):
                  lowercase							:					Optional[Any]	     =				self.letter_to_numbers(message[letter_index]				)
                  lowercase							:					Union[str, Any]	     =				numbers[0]
                  lowercase							:					Union[str, Any]	     =				numbers[1]

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

           return decoded_message


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


lowercase  : Tuple						   =							logging.get_logger(__name__)

class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    ["input_features", "attention_mask"]
    def __init__(  self		,snake_case=80		,snake_case=16000		,snake_case=0.0		,snake_case=10		,snake_case=25		,snake_case="hamming_window"		,snake_case=32_768.0		,snake_case=0.97		,snake_case=1.0		,snake_case=True		,snake_case=True		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







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

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

           lowercase							:					Any	     =				win_length * sampling_rate // 1000
           lowercase							:					Tuple	     =				hop_length * sampling_rate // 1000

           lowercase							:					Tuple	     =				optimal_fft_length(self.sample_size				)
           lowercase							:					Dict	     =				(self.n_fft // 2) + 1
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.win_function == "hamming_window":
                  lowercase							:					Optional[Any]	     =				window_function(window_length=self.sample_size		,name=self.win_function		,periodic=snake_case				)
           else:
                  lowercase							:					Optional[Any]	     =				window_function(window_length=self.sample_size		,name=self.win_function				)

           lowercase							:					int	     =				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		,)

           lowercase							:					Dict	     =				spectrogram(
               one_waveform * self.frame_signal_scale		,window=snake_case		,frame_length=self.sample_size		,hop_length=self.sample_stride		,fft_length=self.n_fft		,center=snake_case		,preemphasis=self.preemphasis_coeff		,mel_filters=snake_case		,mel_floor=self.mel_floor		,log_mel="""log"""		,)
           return msfc_features.T
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







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

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

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

           return x
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
           return [self._normalize_one(snake_case		,snake_case		,self.padding_value				) for x, n in zip(snake_case		,snake_case				)]
    def __call__(  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = False		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''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."""				)

           lowercase							:					List[Any]	     =				isinstance(snake_case		,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}"				)
           lowercase							:					str	     =				is_batched_numpy or (
               isinstance(snake_case		,(list, tuple)				) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)				))
           )

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

           # always return batch
           if not is_batched:
                  lowercase							:					Dict	     =				[raw_speech]

           # extract fbank features
           lowercase							:					Tuple	     =				[self._extract_mfsc_features(snake_case				) for one_waveform in raw_speech]

           # convert into correct format for padding
           lowercase							:					Union[str, Any]	     =				BatchFeature({"""input_features""": features}				)

           lowercase							:					Optional[int]	     =				self.pad(
               snake_case		,padding=snake_case		,max_length=snake_case		,truncation=snake_case		,pad_to_multiple_of=snake_case		,return_attention_mask=snake_case		,**snake_case		,)
           # make sure list is in array format
           lowercase							:					Tuple	     =				padded_inputs.get("""input_features"""				)
           if isinstance(input_features[0]		,snake_case				):
                  lowercase							:					List[Any]	     =				[np.asarray(snake_case		,dtype=np.floataa				) for feature in input_features]

           lowercase							:					int	     =				padded_inputs.get("""attention_mask"""				)
           if attention_mask is not None:
                  lowercase							:					Any	     =				[np.asarray(snake_case		,dtype=np.intaa				) for array in attention_mask]

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

           if return_tensors is not None:
                  lowercase							:					str	     =				padded_inputs.convert_to_tensors(snake_case				)

           return padded_inputs
