






'''simple docstring'''




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass







@dataclass
class       __lowercase					:
	_lowerCamelCase              =  42
	_lowerCamelCase              =  42







class       __lowercase					:

	def __init__(      self   :						Any  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					list[list[Edge]]					=					[[] for _ in range(UpperCAmelCase_)]
			UpperCamelCase__				:					Dict					=					size

	def __getitem__(      self   :						Any  ,					UpperCAmelCase_   :						int):
			return iter(self._graph[vertex])

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return self._size

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			if weight not in (0, 1):
					raise ValueError('Edge weight must be either 0 or 1.')

			if to_vertex < 0 or to_vertex >= self.size:
					raise ValueError('Vertex indexes must be in [0; size).')

			self._graph[from_vertex].append(Edge(UpperCAmelCase_  ,					UpperCAmelCase_))





	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[int]					=					deque([start_vertex])
			UpperCamelCase__				:					list[int | None]					=					[None] * self.size
			UpperCamelCase__				:					Union[str, Any]					=					0

			while queue:
					UpperCamelCase__				:					Dict					=					queue.popleft()
					UpperCamelCase__				:					Any					=					distances[current_vertex]
					if current_distance is None:
							continue

					for edge in self[current_vertex]:
							UpperCamelCase__				:					Optional[int]					=					current_distance + edge.weight
							UpperCamelCase__				:					Tuple					=					distances[edge.destination_vertex]
							if (
							    isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
							    and new_distance >= dest_vertex_distance
							):
									continue
							UpperCamelCase__				:					Dict					=					new_distance
							if edge.weight == 0:
									queue.appendleft(edge.destination_vertex)
							else:
									queue.append(edge.destination_vertex)

			if distances[finish_vertex] is None:
					raise ValueError('No path from start_vertex to finish_vertex.')

			return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()












'''simple docstring'''




from typing import Union

import fire
import torch
from tqdm import tqdm







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = "cpu"     ,      lowerCamelCase_ = None)							->     None:
		UpperCamelCase__				:					List[Any]					=					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')
				UpperCamelCase__				:					int					=					v.half()
		if save_path is None:  # overwrite src_path
				UpperCamelCase__				:					List[Any]					=					src_path
		torch.save(lowerCamelCase_     ,      lowerCamelCase_)


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




