
"""simple docstring"""






import sys



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       len(UpperCamelCase__				)
				A__							 =       [[0 for x in range(UpperCamelCase__				)] for x in range(UpperCamelCase__				)]
				A__							 =       [[0 for x in range(UpperCamelCase__				)] for x in range(UpperCamelCase__				)]

				for chain_length in range(2       ,UpperCamelCase__				):
								for a in range(1       ,n - chain_length + 1				):
												A__							 =       a + chain_length - 1

												A__							 =       sys.maxsize
												for c in range(UpperCamelCase__       ,UpperCamelCase__				):
																A__							 =       (
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				A__							 =       cost
																				A__							 =       c
				return matrix, sol



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				if i == j:
								print('A' + str(UpperCamelCase__				)       ,end=' '				)
				else:
								print('('       ,end=' '				)
								print_optiomal_solution(UpperCamelCase__       ,UpperCamelCase__       ,optimal_solution[i][j]				)
								print_optiomal_solution(UpperCamelCase__       ,optimal_solution[i][j] + 1       ,UpperCamelCase__				)
								print(')'       ,end=' '				)



def  __lowerCamelCase					(					):
				A__							 =       [30, 35, 15, 5, 10, 20, 25]
				A__							 =       len(UpperCamelCase__				)
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				A__							 =       matrix_chain_order(UpperCamelCase__				)

				print('No. of Operation required: ' + str(matrix[1][n - 1]				)				)
				print_optiomal_solution(UpperCamelCase__       ,1       ,n - 1				)


if __name__ == "__main__":
					main()










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


