


'''simple docstring'''
from typing import List, Optional, Union

import numpy as np

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


lowerCAmelCase__  =     logging.get_logger(__name__)







class 					_A						(						UpperCAmelCase__							):



   '''simple docstring'''






   _lowercase    						= ["""input_values""", """attention_mask"""]




   def __init__(	self	:					Union[str, Any]					, lowerCamelCase	:					int=1					, lowerCamelCase	:					Dict=16_000					, lowerCamelCase	:					List[Any]=0.0					, lowerCamelCase	:					int=False					, lowerCamelCase	:					Dict=True					, **lowerCamelCase	:					str					, )->	Tuple:
          super().__init__(feature_size=__lowercase					, sampling_rate=__lowercase					, padding_value=__lowercase					, **__lowercase						)
          snake_case__					:	Dict          =					return_attention_mask
          snake_case__					:	Union[str, Any]          =					do_normalize




   @staticmethod
   def   __lowerCAmelCase						(	lowerCamelCase	:					Tuple					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Dict = 0.0						)->	List[np.ndarray]:
          if attention_mask is not None:
                 snake_case__					:	Dict          =					np.array(__lowercase					, np.intaa						)
                 snake_case__					:	str          =					[]

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

                        normed_input_values.append(__lowercase						)
          else:
                 snake_case__					:	List[Any]          =					[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

          return normed_input_values




   def __call__(	self	:					Optional[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					Optional[int] = False					, lowerCamelCase	:					int = None					, lowerCamelCase	:					List[Any] = False					, lowerCamelCase	:					Any = None					, lowerCamelCase	:					Dict = None					, lowerCamelCase	:					List[str] = None					, lowerCamelCase	:					Optional[Any] = None					, **lowerCamelCase	:					str					, )->	BatchFeature:

          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."""						)

          snake_case__					:	Tuple          =					isinstance(__lowercase					, 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}"""						)
          snake_case__					:	str          =					is_batched_numpy or (
              isinstance(__lowercase					, (list, tuple)						) and (isinstance(raw_speech[0]					, (np.ndarray, tuple, list)						))
          )

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

          # convert into correct format for padding
          snake_case__					:	List[Any]          =					BatchFeature({"""input_values""": raw_speech}						)

          snake_case__					:	Any          =					self.pad(
              __lowercase					, padding=__lowercase					, max_length=__lowercase					, truncation=__lowercase					, pad_to_multiple_of=__lowercase					, return_attention_mask=__lowercase					, )

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

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

          # zero-mean and unit-variance normalization
          if self.do_normalize:
                 snake_case__					:	str          =					(
                     attention_mask
                     if self._get_padding_strategies(__lowercase					, max_length=__lowercase						) is not PaddingStrategy.DO_NOT_PAD
                     else None
                 )
                 snake_case__					:	List[Any]          =					self.zero_mean_unit_var_norm(
                     padded_inputs["""input_values"""]					, attention_mask=__lowercase					, padding_value=self.padding_value						)

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

          return padded_inputs








import numpy as np

_UpperCAmelCase      :	int  =[
    ["""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__:







						'''simple docstring'''




						def __init__(					self				)       ->     None:
										lowerCAmelCase_			:   List[Any]     					=					np.array(__lowercase				)




						def     lowercase_			(					self ,    __lowercase				)       ->     np.ndarray:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[str]     					=					np.where(letter == self.SQUARE				)
										lowerCAmelCase_			:   Union[str, Any]     					=					np.concatenate([indexa + 1, indexa + 1]				)
										return indexes




						def     lowercase_			(					self ,    __lowercase ,    __lowercase				)       ->     str:
										return self.SQUARE[indexa - 1, indexa - 1]




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   Dict     					=					message.lower()
										lowerCAmelCase_			:   Any     					=					message.replace('''j''' ,    '''i'''				)

										lowerCAmelCase_			:   Optional[Any]     					=					''''''
										for letter_index in range(len(__lowercase				)				):
														if message[letter_index] != " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					self.letter_to_numbers(message[letter_index]				)
																		lowerCAmelCase_			:   List[Any]     					=					encoded_message + str(numbers[0]				) + str(numbers[1]				)
														elif message[letter_index] == " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					encoded_message + ''' '''

										return encoded_message




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   List[str]     					=					message.replace(''' ''' ,    '''  '''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					''''''
										for numbers_index in range(int(len(__lowercase				) / 2				)				):
														if message[numbers_index * 2] != " ":
																		lowerCAmelCase_			:   Any     					=					message[numbers_index * 2]
																		lowerCAmelCase_			:   Dict     					=					message[numbers_index * 2 + 1]

																		lowerCAmelCase_			:   Dict     					=					self.numbers_to_letter(int(__lowercase				) ,    int(__lowercase				)				)
																		lowerCAmelCase_			:   Tuple     					=					decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		lowerCAmelCase_			:   List[str]     					=					decoded_message + ''' '''

										return decoded_message