




def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			bool:
							# must be
							lowercase__  :	List[str]        =							[list(SCREAMING_SNAKE_CASE_    ) for row in matrix]
							if matrix and isinstance(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ):
														if isinstance(matrix[0]    ,SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							0
																					for row in matrix:
																												if prev_len == 0:
																																			lowercase__  :	Union[str, Any]        =							len(SCREAMING_SNAKE_CASE_    )
																																			lowercase__  :	Optional[int]        =							True
																												else:
																																			lowercase__  :	List[str]        =							prev_len == len(SCREAMING_SNAKE_CASE_    )
														else:
																					lowercase__  :	Optional[int]        =							True
							else:
														lowercase__  :	Dict        =							False

							return result
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			None:
							if check_matrix(SCREAMING_SNAKE_CASE_    ) and len(SCREAMING_SNAKE_CASE_    ) > 0:
														lowercase__  :	Optional[int]        =							[list(SCREAMING_SNAKE_CASE_    ) for row in a]
														lowercase__  :	Tuple        =							len(SCREAMING_SNAKE_CASE_    )
														if isinstance(a[0]    ,SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Dict        =							len(a[0]    )
														else:
																					for dat in a:
																												print(SCREAMING_SNAKE_CASE_    )
																					return

														# horizotal printing increasing
														for i in range(0    ,SCREAMING_SNAKE_CASE_    ):
																					print(a[0][i]    )
														# vertical printing down
														for i in range(1    ,SCREAMING_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]    )
														lowercase__  :	Tuple        =							[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
														if len(SCREAMING_SNAKE_CASE_    ) > 0:
																					spiral_print_clockwise(SCREAMING_SNAKE_CASE_    )
														else:
																					return
							else:
														print("Not a valid matrix"    )
														return
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			list[int]:
							if matrix:
														return list(matrix.pop(0    )    ) + spiral_traversal(list(zip(*SCREAMING_SNAKE_CASE_    )    )[::-1]    )
							else:
														return []


# driver code
if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__a  :		Optional[Any]		     =   [[1, 2, 3, 4], [5, 6, 7, 8], [9, 1_0, 1_1, 1_2]]
							spiral_print_clockwise(a)





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[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(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_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)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_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]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test