import numpy as np



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





def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase=10    )							->	Any:
		__A  ,       __A							:   str	  =				np.unique(_lowercase ,     return_inverse=_lowercase    )
		__A							:   List[Any]	  =				classes.shape[0]
		__A							:   List[str]	  =				np.bincount(_lowercase    )
		if np.min(_lowercase    ) < 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)    )
		__A							:   Union[str, Any]	  =				np.split(np.argsort(_lowercase ,     kind="mergesort"    ) ,     np.cumsum(_lowercase    )[:-1]    )
		for _ in range(_lowercase    ):
				__A							:   List[str]	  =				approximate_mode(_lowercase ,     _lowercase ,     _lowercase    )
				__A							:   str	  =				class_counts - n_i
				__A							:   Optional[Any]	  =				approximate_mode(_lowercase ,     _lowercase ,     _lowercase    )

				__A							:   Tuple	  =				[]
				__A							:   List[str]	  =				[]

				for i in range(_lowercase    ):
						__A							:   str	  =				rng.permutation(class_counts[i]    )
						__A							:   Tuple	  =				class_indices[i].take(_lowercase ,     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]]    )
				__A							:   int	  =				rng.permutation(_lowercase    )
				__A							:   Optional[int]	  =				rng.permutation(_lowercase    )

				yield train, test






import os



def    lowerCamelCase_   (  )							->	List[str]:
		__A							:   Any	  =				0
		__A							:   Any	  =				0
		with open(os.path.dirname(_lowercase    ) + "/p022_names.txt"    ) as file:
				__A							:   int	  =				str(file.readlines()[0]    )
				__A							:   Optional[Any]	  =				name.replace("\"" ,     ""    ).split(","    )

		name.sort()
		for i in range(len(_lowercase    )    ):
				for j in name[i]:
						temp_sum += ord(_lowercase    ) - ord("A"    ) + 1
				total_sum += (i + 1) * temp_sum
				__A							:   List[Any]	  =				0
		return total_sum


if __name__ == "__main__":
					print(solution())





