



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case__					:     Dict    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Tuple      							=      ["""input_features""", """attention_mask"""]



						def __init__(	self	:							Any							,      lowerCamelCase	:							List[str]=80							,      lowerCamelCase	:							Optional[int]=16_000							,      lowerCamelCase	:							List[str]=80							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							Dict=True							,      **lowerCamelCase	:							List[str]							,      ):






							'''simple docstring'''



							super().__init__(feature_size=lowerCamelCase							,      sampling_rate=lowerCamelCase							,      padding_value=lowerCamelCase							,      **lowerCamelCase	)
							__lowercase		       =			num_mel_bins
							__lowercase		       =			do_ceptral_normalize
							__lowercase		       =			normalize_means
							__lowercase		       =			normalize_vars
							__lowercase		       =			True



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							np.ndarray							,      ):






							'''simple docstring'''



							__lowercase		       =			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							__lowercase		       =			torch.from_numpy(lowerCamelCase	).unsqueeze(0	)
							__lowercase		       =			ta_kaldi.fbank(lowerCamelCase							,      num_mel_bins=self.num_mel_bins							,      sample_frequency=self.sampling_rate	)
							return features.numpy()



						@staticmethod
						def     _snake_case	(	lowerCamelCase	:							np.ndarray							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[bool] = True							,      lowerCamelCase	:							Optional[bool] = True							,      lowerCamelCase	:							float = 0.0							,      ):






							'''simple docstring'''



							if normalize_means:
								__lowercase		       =			x[:input_length].mean(axis=0	)
								__lowercase		       =			np.subtract(lowerCamelCase							,      lowerCamelCase	)
							if normalize_vars:
								__lowercase		       =			x[:input_length].std(axis=0	)
								__lowercase		       =			np.divide(lowerCamelCase							,      lowerCamelCase	)

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

							# make sure array is in float32
							__lowercase		       =			x.astype(np.floataa	)

							return x



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							List[np.ndarray]							,      lowerCamelCase	:							Optional[np.ndarray] = None	):






							'''simple docstring'''



							__lowercase		       =			attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(lowerCamelCase							,      lowerCamelCase							,      self.normalize_means							,      self.normalize_vars							,      self.padding_value	)
							    for x, n in zip(lowerCamelCase							,      lowerCamelCase	)
							]



						def __call__(	self	:							Dict							,      lowerCamelCase	:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,      lowerCamelCase	:							Union[bool, str, PaddingStrategy] = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[Union[str, TensorType]] = None							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      **lowerCamelCase	:							Dict							,      ):






							'''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		       =			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}"""	)
							__lowercase		       =			is_batched_numpy or (
							    isinstance(lowerCamelCase							,      (list, tuple)	) and (isinstance(raw_speech[0]							,      (np.ndarray, tuple, list)	))
							)

							if is_batched:
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.floataa	) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCamelCase							,      np.ndarray	):
								__lowercase		       =			np.asarray(lowerCamelCase							,      dtype=np.floataa	)
							elif isinstance(lowerCamelCase							,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								__lowercase		       =			raw_speech.astype(np.floataa	)

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

							# extract fbank features
							__lowercase		       =			[self._extract_fbank_features(lowerCamelCase	) for waveform in raw_speech]

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

							__lowercase		       =			self.pad(
							    lowerCamelCase							,      padding=lowerCamelCase							,      max_length=lowerCamelCase							,      truncation=lowerCamelCase							,      pad_to_multiple_of=lowerCamelCase							,      return_attention_mask=lowerCamelCase							,      **lowerCamelCase							,      )

							# make sure list is in array format
							__lowercase		       =			padded_inputs.get("input_features"	)
							if isinstance(input_features[0]							,      lowerCamelCase	):
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.floataa	) for feature in input_features]

							__lowercase		       =			padded_inputs.get("attention_mask"	)
							if attention_mask is not None:
								__lowercase		       =			[np.asarray(lowerCamelCase							,      dtype=np.intaa	) for array in attention_mask]

							# Utterance-level cepstral mean and variance normalization
							if self.do_ceptral_normalize:
								__lowercase		       =			(
								    np.array(lowerCamelCase							,      dtype=np.intaa	)
								    if self._get_padding_strategies(lowerCamelCase							,      max_length=lowerCamelCase	) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								__lowercase		       =			self.normalize(
								    padded_inputs["input_features"]							,      attention_mask=lowerCamelCase	)

							if return_tensors is not None:
								__lowercase		       =			padded_inputs.convert_to_tensors(lowerCamelCase	)

							return padded_inputs








import numpy as np

snake_case__					:     Tuple    		=   [
    ["""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   _A   :


						'''simple docstring'''





						def __init__(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			np.array(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			np.where(letter == self.SQUARE	)
							__lowercase		       =			np.concatenate([indexa + 1, indexa + 1]	)
							return indexes



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.SQUARE[indexa - 1, indexa - 1]
							return letter



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			message.lower()
							__lowercase		       =			message.replace(" "							,      ""	)
							__lowercase		       =			message.replace("j"							,      "i"	)

							__lowercase		       =			np.empty((2, len(lowerCamelCase	))	)
							for letter_index in range(len(lowerCamelCase	)	):
								__lowercase		       =			self.letter_to_numbers(message[letter_index]	)

								__lowercase		       =			numbers[0]
								__lowercase		       =			numbers[1]

							__lowercase		       =			first_step.reshape(2 * len(lowerCamelCase	)	)
							__lowercase		       =			""
							for numbers_index in range(len(lowerCamelCase	)	):
								__lowercase		       =			int(second_step[numbers_index * 2]	)
								__lowercase		       =			int(second_step[(numbers_index * 2) + 1]	)
								__lowercase		       =			self.numbers_to_letter(lowerCamelCase							,      lowerCamelCase	)
								__lowercase		       =			encoded_message + letter

							return encoded_message



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			message.lower()
							message.replace(" "							,      ""	)
							__lowercase		       =			np.empty(2 * len(lowerCamelCase	)	)
							for letter_index in range(len(lowerCamelCase	)	):
								__lowercase		       =			self.letter_to_numbers(message[letter_index]	)
								__lowercase		       =			numbers[0]
								__lowercase		       =			numbers[1]

							__lowercase		       =			first_step.reshape((2, len(lowerCamelCase	))	)
							__lowercase		       =			""
							for numbers_index in range(len(lowerCamelCase	)	):
								__lowercase		       =			int(second_step[0, numbers_index]	)
								__lowercase		       =			int(second_step[1, numbers_index]	)
								__lowercase		       =			self.numbers_to_letter(lowerCamelCase							,      lowerCamelCase	)
								__lowercase		       =			decoded_message + letter

							return decoded_message



