

def 	lowerCamelCase__			(   __A						:int = 1_0_0_0		):


       """simple docstring"""




       __snake_case					  =       []
       __snake_case					  =       []
       __snake_case					  =       3
       __snake_case					  =       5
       __snake_case					  =       1
       while True:
              __snake_case					  =       z * temp
              if result < n:
                     zmulti.append(__A		)
                     temp += 1
              else:
                     __snake_case					  =       1
                     break
       while True:
              __snake_case					  =       x * temp
              if result < n:
                     xmulti.append(__A		)
                     temp += 1
              else:
                     break
       __snake_case					  =       list(set(xmulti + zmulti		)		)
       return sum(__A		)


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




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 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       ["input_values", "attention_mask"]


       def __init__(		self			,   _UpperCamelCase=1			,   _UpperCamelCase=1_60_00			,   _UpperCamelCase=0.0			,   _UpperCamelCase=False			,   _UpperCamelCase=True			,   **_UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              super().__init__(feature_size=_UpperCamelCase			,   sampling_rate=_UpperCamelCase			,   padding_value=_UpperCamelCase			,   **_UpperCamelCase      )
              __snake_case					  =       return_attention_mask
              __snake_case					  =       do_normalize


       @staticmethod
       def  a		(		_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 0.0      )			->       List[np.ndarray]:






              """simple docstring"""


              if attention_mask is not None:
                     __snake_case					  =       np.array(_UpperCamelCase			,   np.intaa      )
                     __snake_case					  =       []

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

                            normed_input_values.append(_UpperCamelCase      )
              else:
                     __snake_case					  =       [(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			,   )			->       BatchFeature:






              """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."""      )

              __snake_case					  =       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}'      )
              __snake_case					  =       is_batched_numpy or (
                  isinstance(_UpperCamelCase			,   (list, tuple)      ) and (isinstance(raw_speech[0]			,   (np.ndarray, tuple, list)      ))
              )

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

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

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

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

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

              if return_tensors is not None:
                     __snake_case					  =       padded_inputs.convert_to_tensors(_UpperCamelCase      )

              return padded_inputs

