






"""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)







"""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