code: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   list[list[int]]     )						->		bool:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						[list(__snake_case     ) for row in matrix]
         if matrix and isinstance(__snake_case   ,    __snake_case     ):
                if isinstance(matrix[0]   ,    __snake_case     ):
                       __magic_name__										=						0
                       for row in matrix:
                              if prev_len == 0:
                                     __magic_name__										=						len(__snake_case     )
                                     __magic_name__										=						True
                              else:
                                     __magic_name__										=						prev_len == len(__snake_case     )
                else:
                       __magic_name__										=						True
         else:
                __magic_name__										=						False
  
         return result
  
  
  def        A	(							__snake_case:   list[list[int]]     )						->		None:
  
         """simple docstring"""
  
  
  
  
  
         if check_matrix(__snake_case     ) and len(__snake_case     ) > 0:
                __magic_name__										=						[list(__snake_case     ) for row in a]
                __magic_name__										=						len(__snake_case     )
                if isinstance(a[0]   ,    __snake_case     ):
                       __magic_name__										=						len(a[0]     )
                else:
                       for dat in a:
                              print(__snake_case     )
                       return
  
                # horizotal printing increasing
                for i in range(0   ,    __snake_case     ):
                       print(a[0][i]     )
                # vertical printing down
                for i in range(1   ,    __snake_case     ):
                       print(a[i][mat_col - 1]     )
                # horizotal printing decreasing
                if mat_row > 1:
                       for i in range(mat_col - 2   ,    -1   ,    -1     ):
                              print(a[mat_row - 1][i]     )
          # vertical printing up
                for i in range(mat_row - 2   ,    0   ,    -1     ):
                       print(a[i][0]     )
                __magic_name__										=						[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
                if len(__snake_case     ) > 0:
                       spiral_print_clockwise(__snake_case     )
                else:
                       return
         else:
                print('Not a valid matrix'     )
                return
  
  
  def        A	(							__snake_case:   list[list]     )						->		list[int]:
  
         """simple docstring"""
  
  
  
  
  
         if matrix:
                return list(matrix.pop(0     )     ) + spiral_traversal(list(zip(*__snake_case     )     )[::-1]     )
         else:
                return []
  
  
  # driver code
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   snake_case   :   Tuple    		=		[[1, 2, 3, 4], [5, 6, 7, 8], [9, 1_0, 1_1, 1_2]]
   spiral_print_clockwise(a)
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