

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


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''input_values''', '''attention_mask''']




 def __init__(			self  ,  _UpperCAmelCase=1  ,  _UpperCAmelCase=16_000  ,  _UpperCAmelCase=0.0  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  **_UpperCAmelCase  ,  ):
   super().__init__(feature_size=_UpperCAmelCase  ,  sampling_rate=_UpperCAmelCase  ,  padding_value=_UpperCAmelCase  ,  **_UpperCAmelCase							)
   __UpperCAmelCase	:  List[str]		=     return_attention_mask
   __UpperCAmelCase	:  Dict		=     do_normalize




 @staticmethod
 def     a       (			_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = 0.0							):
   if attention_mask is not None:
     __UpperCAmelCase	:  List[str]		=     np.array(_UpperCAmelCase  ,  np.intaa							)
     __UpperCAmelCase	:  Any		=     []

     for vector, length in zip(_UpperCAmelCase  ,  attention_mask.sum(-1							)							):
       __UpperCAmelCase	:  Tuple		=     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7							)
       if length < normed_slice.shape[0]:
         __UpperCAmelCase	:  List[Any]		=     padding_value

       normed_input_values.append(_UpperCAmelCase							)
   else:
     __UpperCAmelCase	:  Optional[int]		=     [(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

   return normed_input_values




 def __call__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):

   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.'							)

   __UpperCAmelCase	:  Optional[Any]		=     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	:  Tuple		=     is_batched_numpy or (
       isinstance(_UpperCAmelCase  ,  (list, tuple)							) and (isinstance(raw_speech[0]  ,  (np.ndarray, tuple, list)							))
   )

   # always return batch
   if not is_batched:
     __UpperCAmelCase	:  Optional[Any]		=     [raw_speech]

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

   __UpperCAmelCase	:  Tuple		=     self.pad(
       _UpperCAmelCase  ,  padding=_UpperCAmelCase  ,  max_length=_UpperCAmelCase  ,  truncation=_UpperCAmelCase  ,  pad_to_multiple_of=_UpperCAmelCase  ,  return_attention_mask=_UpperCAmelCase  ,  )

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

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

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

   if return_tensors is not None:
     __UpperCAmelCase	:  List[Any]		=     padded_inputs.convert_to_tensors(_UpperCAmelCase							)

   return padded_inputs








"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int.")
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

   __lowercase								=		(math.floor(math.log(n * (phi + 2),     UpperCamelCase_) - 1) // 3) * 3 + 2
   __lowercase								=		Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
   __lowercase								=		num // 2
   return int(UpperCamelCase_)


if __name__ == "__main__":
   print(F"{solution() = }")




