code: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   List[Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   int   ,    __snake_case:   List[Any]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         if start == end:
                return start if lst[start] > item else start + 1
         if start > end:
                return start
  
         __magic_name__										=						(start + end) // 2
         if lst[mid] < item:
                return binary_search(__snake_case   ,    __snake_case   ,    mid + 1   ,    __snake_case     )
         elif lst[mid] > item:
                return binary_search(__snake_case   ,    __snake_case   ,    __snake_case   ,    mid - 1     )
         else:
                return mid
  
  
  def        A	(							__snake_case:   Optional[int]     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						len(__snake_case     )
  
         for index in range(1   ,    __snake_case     ):
                __magic_name__										=						lst[index]
                __magic_name__										=						binary_search(__snake_case   ,    __snake_case   ,    0   ,    index - 1     )
                __magic_name__										=						lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]
  
         return lst
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   List[str]     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         if not left:
                return right
  
         if not right:
                return left
  
         if left[0] < right[0]:
                return [left[0], *merge(left[1:]   ,    __snake_case     )]
  
         return [right[0], *merge(__snake_case   ,    right[1:]     )]
  
  
  def        A	(							__snake_case:   Any     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						len(__snake_case     )
         __magic_name__							,     __magic_name__										=						[], []
         __magic_name__										=						[lst[0]]
         __magic_name__										=						[]
         __magic_name__										=						1
         while i < length:
                if lst[i] < lst[i - 1]:
                       runs.append(__snake_case     )
                       __magic_name__										=						[lst[i]]
                else:
                       new_run.append(lst[i]     )
                i += 1
         runs.append(__snake_case     )
  
         for run in runs:
                sorted_runs.append(insertion_sort(__snake_case     )     )
         for run in sorted_runs:
                __magic_name__										=						merge(__snake_case   ,    __snake_case     )
  
         return sorted_array
  
  
  def        A	(							)						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						[5, 9, 1_0, 3, -4, 5, 1_7_8, 9_2, 4_6, -1_8, 0, 7]
         __magic_name__										=						tim_sort(__snake_case     )
         print(__snake_case     )
  
  
  if __name__ == "__main__":
   main()
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						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
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 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)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    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]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test