






'''simple docstring'''
import numpy as np




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      int          =       n_draws * class_counts / class_counts.sum()
   # floored means we don't overshoot n_samples, but probably undershoot
   lowercase_		:      Optional[int]          =       np.floor(_UpperCamelCase							)
   # we add samples according to how much "left over" probability
   # they had, until we arrive at n_samples
   lowercase_		:      List[str]          =       int(n_draws - floored.sum()							)
   if need_to_add > 0:
      lowercase_		:      Any          =       continuous - floored
      lowercase_		:      Optional[Any]          =       np.sort(np.unique(_UpperCamelCase							)							)[::-1]
      # add according to remainder, but break ties
      # randomly to avoid biases
      for value in values:
         ((lowercase_)      ,						)		:      Any          =       np.where(remainder == value							)
         # if we need_to_add less than what's in inds
         # we draw randomly from them.
         # if we need to add more, we add them all and
         # go to the next value
         lowercase_		:      Dict          =       min(len(_UpperCamelCase							)  ,						_UpperCamelCase							)
         lowercase_		:      Union[str, Any]          =       rng.choice(_UpperCamelCase  ,						size=_UpperCamelCase  ,						replace=_UpperCamelCase							)
         floored[inds] += 1
         need_to_add -= add_now
         if need_to_add == 0:
            break
   return floored.astype(np.intaa							)





def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase=10							):


   """simple docstring"""




   lowercase_      ,						lowercase_		:      Optional[int]          =       np.unique(_UpperCamelCase  ,						return_inverse=_UpperCamelCase							)
   lowercase_		:      Optional[int]          =       classes.shape[0]
   lowercase_		:      Optional[Any]          =       np.bincount(_UpperCamelCase							)
   if np.min(_UpperCamelCase							) < 2:
      raise ValueError("Minimum class count error"							)
   if n_train < n_classes:
      raise ValueError(
          "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)							)
   if n_test < n_classes:
      raise ValueError(
          "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)							)
   lowercase_		:      List[Any]          =       np.split(np.argsort(_UpperCamelCase  ,						kind="mergesort"							)  ,						np.cumsum(_UpperCamelCase							)[:-1]							)
   for _ in range(_UpperCamelCase							):
      lowercase_		:      Dict          =       approximate_mode(_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							)
      lowercase_		:      Tuple          =       class_counts - n_i
      lowercase_		:      List[str]          =       approximate_mode(_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							)

      lowercase_		:      Any          =       []
      lowercase_		:      Union[str, Any]          =       []

      for i in range(_UpperCamelCase							):
         lowercase_		:      str          =       rng.permutation(class_counts[i]							)
         lowercase_		:      int          =       class_indices[i].take(_UpperCamelCase  ,						mode="clip"							)
         train.extend(perm_indices_class_i[: n_i[i]]							)
         test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
      lowercase_		:      Union[str, Any]          =       rng.permutation(_UpperCamelCase							)
      lowercase_		:      Union[str, Any]          =       rng.permutation(_UpperCamelCase							)

      yield train, test












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




