




import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						NDArray[floataa]:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    np.shape(lowercase_   )

    __UpperCamelCase			=    np.zeros((rows, 1)       ,  dtype=lowercase_   )
    for row in reversed(range(lowercase_   )   ):
        __UpperCamelCase			=    0
        for col in range(row + 1       ,  lowercase_   ):
            total += coefficients[row, col] * x[col]

        __UpperCamelCase			=    (vector[row] - total) / coefficients[row, row]

    return x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						NDArray[floataa]:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    np.shape(lowercase_   )
    if rows != columns:
        return np.array(()       ,  dtype=lowercase_   )

    # augmented matrix
    __UpperCamelCase			=    np.concatenate((coefficients, vector)       ,  axis=1   )
    __UpperCamelCase			=    augmented_mat.astype('''float64'''   )

    # scale the matrix leaving it triangular
    for row in range(rows - 1   ):
        __UpperCamelCase			=    augmented_mat[row, row]
        for col in range(row + 1       ,  lowercase_   ):
            __UpperCamelCase			=    augmented_mat[col, row] / pivot
            augmented_mat[col, :] -= factor * augmented_mat[row, :]

    __UpperCamelCase			=    retroactive_resolution(
        augmented_mat[:, 0:columns]       ,  augmented_mat[:, columns : columns + 1]   )

    return x


if __name__ == "__main__":
 import doctest

 doctest.testmod()












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


UpperCamelCase__       				=	logging.get_logger(__name__)
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						["input_values", "attention_mask"]



     def __init__(  self   :							Union[str, Any]      ,		lowercase__   :							str=1      ,		lowercase__   :							int=1_6_0_0_0      ,		lowercase__   :							Optional[int]=0.0      ,		lowercase__   :							List[Any]=False      ,		lowercase__   :							Dict=True      ,		**lowercase__   :							Any      ,		):
       super().__init__(feature_size=lowercase__      ,		sampling_rate=lowercase__      ,		padding_value=lowercase__      ,		**lowercase__       )
       a_		    =  return_attention_mask
       a_		    =  do_normalize



     @staticmethod
     def 				__magic_name__		(  lowercase__   :							List[np.ndarray]      ,		lowercase__   :							List[np.ndarray]      ,		lowercase__   :							float = 0.0       ):
       if attention_mask is not None:
         a_		    =  np.array(lowercase__      ,		np.intaa       )
         a_		    =  []

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

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

       return normed_input_values



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

       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.'''       )

       a_		    =  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}"       )
       a_		    =  is_batched_numpy or (
           isinstance(lowercase__      ,		(list, tuple)       ) and (isinstance(raw_speech[0]      ,		(np.ndarray, tuple, list)       ))
       )

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

       # convert into correct format for padding
       a_		    =  BatchFeature({'''input_values''': raw_speech}       )

       a_		    =  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
       a_		    =  padded_inputs['''input_values''']
       if not isinstance(input_values[0]      ,		np.ndarray       ):
         a_		    =  [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       )
       ):
         a_		    =  [array.astype(np.floataa       ) for array in input_values]
       elif isinstance(lowercase__      ,		np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
         a_		    =  input_values.astype(np.floataa       )

       # convert attention_mask to correct format
       a_		    =  padded_inputs.get('''attention_mask'''       )
       if attention_mask is not None:
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.intaa       ) for array in attention_mask]

       # zero-mean and unit-variance normalization
       if self.do_normalize:
         a_		    =  (
             attention_mask
             if self._get_padding_strategies(lowercase__      ,		max_length=lowercase__       ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a_		    =  self.zero_mean_unit_var_norm(
             padded_inputs['''input_values''']      ,		attention_mask=lowercase__      ,		padding_value=self.padding_value       )

       if return_tensors is not None:
         a_		    =  padded_inputs.convert_to_tensors(lowercase__       )

       return padded_inputs
