




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase_						=			2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCAmelCase_						=			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCAmelCase_						=			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							tuple[str, float]:
     lowercase :       Tuple       =			len([g for position, g in enumerate(__magic_name__   ) if g == main_target[position]]   )
     return (item, float(__magic_name__   ))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							tuple[str, str]:
     lowercase :       Tuple       =			random.randint(0			,		len(__magic_name__   ) - 1   )
     lowercase :       List[Any]       =			parent_a[:random_slice] + parent_a[random_slice:]
     lowercase :       int       =			parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:
     lowercase :       Union[str, Any]       =			list(__magic_name__   )
     if random.uniform(0			,		1   ) < MUTATION_PROBABILITY:
          lowercase :       Dict       =			random.choice(__magic_name__   )
     return "".join(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		)			->							list[str]:
     lowercase :       Optional[Any]       =			[]
     # Generate more children proportionally to the fitness score.
     lowercase :       Optional[int]       =			int(parent_a[1] * 1_00   ) + 1
     lowercase :       int       =			10 if child_n >= 10 else child_n
     for _ in range(__magic_name__   ):
          lowercase :       str       =			population_score[random.randint(0			,		__magic_name__   )][0]

          lowercase :       Optional[Any]       =			crossover(parent_a[0]			,		__magic_name__   )
          # Append new string to the population list.
          pop.append(mutate(__magic_name__			,		__magic_name__   )   )
          pop.append(mutate(__magic_name__			,		__magic_name__   )   )
     return pop






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__ = True   )			->							tuple[int, int, str]:


     if N_POPULATION < N_SELECTED:
          lowercase :       str       =			F"""{N_POPULATION} must be bigger than {N_SELECTED}"""
          raise ValueError(__magic_name__   )
     # Verify that the target contains no genes besides the ones inside genes variable.
     lowercase :       List[str]       =			sorted({c for c in target if c not in genes}   )
     if not_in_genes_list:
          lowercase :       List[Any]       =			F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
          raise ValueError(__magic_name__   )

     # Generate random starting population.
     lowercase :       str       =			[]
     for _ in range(__magic_name__   ):
          population.append(''''''.join([random.choice(__magic_name__   ) for i in range(len(__magic_name__   )   )]   )   )

     # Just some logs to know what the algorithms is doing.
     lowercase :       Optional[int]       =			0, 0

     # This loop will end when we find a perfect match for our target.
     while True:
          generation += 1
          total_population += len(__magic_name__   )

          # Random population created. Now it's time to evaluate.

          # Adding a bit of concurrency can make everything faster,
          #
          # import concurrent.futures
          # population_score: list[tuple[str, float]] = []
          # with concurrent.futures.ThreadPoolExecutor(
          #                                   max_workers=NUM_WORKERS) as executor:
          #     futures = {executor.submit(evaluate, item) for item in population}
          #     concurrent.futures.wait(futures)
          #     population_score = [item.result() for item in futures]
          #
          # but with a simple algorithm like this, it will probably be slower.
          # We just need to call evaluate for every item inside the population.
          lowercase :       Tuple       =			[evaluate(__magic_name__			,		__magic_name__   ) for item in population]

          # Check if there is a matching evolution.
          lowercase :       Tuple       =			sorted(__magic_name__			,		key=lambda __magic_name__   : x[1]			,		reverse=__magic_name__   )
          if population_score[0][0] == target:
               return (generation, total_population, population_score[0][0])

          # Print the best result every 10 generation.
          # Just to know that the algorithm is working.
          if debug and generation % 10 == 0:
               print(
                   F"""\nGeneration: {generation}"""
                   F"""\nTotal Population:{total_population}"""
                   F"""\nBest score: {population_score[0][1]}"""
                   F"""\nBest string: {population_score[0][0]}"""   )

          # Flush the old population, keeping some of the best evolutions.
          # Keeping this avoid regression of evolution.
          lowercase :       Dict       =			population[: int(N_POPULATION / 3   )]
          population.clear()
          population.extend(__magic_name__   )
          # Normalize population score to be between 0 and 1.
          lowercase :       Optional[int]       =			[
              (item, score / len(__magic_name__   )) for item, score in population_score
          ]

          # This is selection
          for i in range(__magic_name__   ):
               population.extend(select(population_score[int(__magic_name__   )]			,		__magic_name__			,		__magic_name__   )   )
               # Check if the population has already reached the maximum value and if so,
               # break the cycle.  If this check is disabled, the algorithm will take
               # forever to compute large strings, but will also calculate small strings in
               # a far fewer generations.
               if len(__magic_name__   ) > N_POPULATION:
                    break


if __name__ == "__main__":
 lowerCAmelCase_						=			(
     'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
 )
 lowerCAmelCase_						=			list(
     ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
     'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
 )
 lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_						=			basic(target_str, genes_list)
 print(
     f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
 )





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


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


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