

def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[list[float]]      )			->      list[list[float]]:




				'''simple docstring'''





				A__        =			[]
				for data in source_data:
								for i, el in enumerate(SCREAMING_SNAKE_CASE_      ):
												if len(SCREAMING_SNAKE_CASE_      ) < i + 1:
																data_lists.append([]      )
												data_lists[i].append(float(SCREAMING_SNAKE_CASE_      )      )
				return data_lists



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[list[float]]	,       SCREAMING_SNAKE_CASE_:							list[int]      )			->      list[list[float]]:




				'''simple docstring'''





				A__        =			[]
				for dlist, weight in zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			min(SCREAMING_SNAKE_CASE_      )
								A__        =			max(SCREAMING_SNAKE_CASE_      )

								A__        =			[]
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))      )
																except ZeroDivisionError:
																				score.append(1      )

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)      )
																except ZeroDivisionError:
																				score.append(0      )

        # weight not 0 or 1
								else:
												A__        =			F'Invalid weight of {weight:f} provided'
												raise ValueError(SCREAMING_SNAKE_CASE_      )

								score_lists.append(SCREAMING_SNAKE_CASE_      )

				return score_lists



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[list[float]]      )			->      list[float]:




				'''simple docstring'''





				A__        =			[0 for i in range(len(score_lists[0]      )      )]

				for slist in score_lists:
								for j, ele in enumerate(SCREAMING_SNAKE_CASE_      ):
												A__        =			final_scores[j] + ele

				return final_scores





def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[list[float]]	,       SCREAMING_SNAKE_CASE_:							list[int]      )			->      list[list[float]]:




				'''simple docstring'''





				A__        =			get_data(SCREAMING_SNAKE_CASE_      )
				A__        =			calculate_each_score(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				A__        =			generate_final_scores(SCREAMING_SNAKE_CASE_      )

				# append scores to source data
				for i, ele in enumerate(SCREAMING_SNAKE_CASE_      ):
								source_data[i].append(SCREAMING_SNAKE_CASE_      )

				return source_data




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       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(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


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

