


'''simple docstring'''

from math import factorial





def __snake_case					(      UpperCAmelCase_						:						int = 100		):
 return sum(map(UpperCAmelCase_					,				str(factorial(UpperCAmelCase_		)		)		)		)


if __name__ == "__main__":
     print(solution(int(input("""Enter the Number: """).strip())))







'''simple docstring'''

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


a_  :				int						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["input_features", "attention_mask"]






  def __init__(    self			,  UpperCamelCase=80			,  UpperCamelCase=1_6000			,  UpperCamelCase=80			,  UpperCamelCase=0.0			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(feature_size=UpperCamelCase			,  sampling_rate=UpperCamelCase			,  padding_value=UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  num_mel_bins
   lowerCamelCase_      =  do_ceptral_normalize
   lowerCamelCase_      =  normalize_means
   lowerCamelCase_      =  normalize_vars
   lowerCamelCase_      =  True






  def   snake_case		(    self			,  UpperCamelCase			,  ):







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






  @staticmethod
  def   snake_case		(    UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = 0.0			,  ):







   """simple docstring"""
   # make sure we normalize float32 arrays
   if normalize_means:
    lowerCamelCase_      =  x[:input_length].mean(axis=0     )
    lowerCamelCase_      =  np.subtract(UpperCamelCase			,  UpperCamelCase     )
   if normalize_vars:
    lowerCamelCase_      =  x[:input_length].std(axis=0     )
    lowerCamelCase_      =  np.divide(UpperCamelCase			,  UpperCamelCase     )

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

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

   return x






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







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







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







   """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."     )

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

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

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

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

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

   lowerCamelCase_      =  self.pad(
       UpperCamelCase			,  padding=UpperCamelCase			,  max_length=UpperCamelCase			,  truncation=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  return_attention_mask=UpperCamelCase			,  **UpperCamelCase			,  )

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

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

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

   if return_tensors is not None:
    lowerCamelCase_      =  padded_inputs.convert_to_tensors(UpperCamelCase     )

   return padded_inputs



