
"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				# Return True if there is node that has not iterated.
				A__							 =       [False] * len(lowerCAmelCase__				)
				A__							 =       []
				queue.append(lowerCAmelCase__				)
				A__							 =       True

				while queue:
								A__							 =       queue.pop(0				)
								for ind in range(len(graph[u]				)				):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(lowerCAmelCase__				)
																A__							 =       True
																A__							 =       u

				return visited[t]



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				# This array is filled by BFS and to store path
				A__							 =       [-1] * (len(lowerCAmelCase__				))
				A__							 =       0
				while bfs(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       float('Inf'				)
								A__							 =       sink

								while s != source:
												# Find the minimum value in select path
												A__							 =       min(lowerCAmelCase__       ,graph[parent[s]][s]				)
												A__							 =       parent[s]

								max_flow += path_flow
								A__							 =       sink

								while v != source:
												A__							 =       parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												A__							 =       parent[v]
				return max_flow


SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE						:						str					   =       0, 5







print(ford_fulkerson(graph, source, sink))










"""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)


