




class       _A				:  # Public class to implement a graph






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			row
								_UpperCAmelCase      =			col
								_UpperCAmelCase      =			graph






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								return (
								    0 <= i < self.ROW
								    and 0 <= j < self.COL
								    and not visited[i][j]
								    and self.graph[i][j]
								)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# Checking all 8 elements surrounding nth element
								_UpperCAmelCase      =			[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
								_UpperCAmelCase      =			[-1, 0, 1, -1, 1, -1, 0, 1]
								_UpperCAmelCase      =			True  # Make those cells visited
								for k in range(8			):
														if self.is_safe(i + row_nbr[k]						,		j + col_nbr[k]						,		_SCREAMING_SNAKE_CASE			):
																				self.diffs(i + row_nbr[k]						,		j + col_nbr[k]						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):  # And finally, count all islands.
								_UpperCAmelCase      =			[[False for j in range(self.COL			)] for i in range(self.ROW			)]
								_UpperCAmelCase      =			0
								for i in range(self.ROW			):
														for j in range(self.COL			):
																				if visited[i][j] is False and self.graph[i][j] == 1:
																										self.diffs(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																										count += 1
								return count





from typing import Union

import fire
import torch
from tqdm import tqdm




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


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