




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase__ :     Union[str, Any]			       =   200
# 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.
lowercase__ :     int			       =   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.
lowercase__ :     str			       =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase)     ->	int:
     a           = len([g for position, g in enumerate(__UpperCAmelCase) if g == main_target[position]])
     return (item, float(__UpperCAmelCase))

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase)     ->	Tuple:
     a           = random.randint(0						,   len(__UpperCAmelCase) - 1)
     a           = parent_a[:random_slice] + parent_a[random_slice:]
     a           = parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase)     ->	int:
     a           = list(__UpperCAmelCase)
     if random.uniform(0						,   1) < MUTATION_PROBABILITY:
          a           = random.choice(__UpperCAmelCase)
     return "".join(__UpperCAmelCase)

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase						,   )     ->	int:
     a           = []
     # Generate more children proportionally to the fitness score.
     a           = int(parent_a[1] * 1_00) + 1
     a           = 10 if child_n >= 10 else child_n
     for _ in range(__UpperCAmelCase):
          a           = population_score[random.randint(0						,   __UpperCAmelCase)][0]

          a    , a           = crossover(parent_a[0]						,   __UpperCAmelCase)
          # Append new string to the population list.
          pop.append(mutate(__UpperCAmelCase						,   __UpperCAmelCase))
          pop.append(mutate(__UpperCAmelCase						,   __UpperCAmelCase))
     return pop

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase = True)     ->	List[Any]:

     # Verify if N_POPULATION is bigger than N_SELECTED
     if N_POPULATION < N_SELECTED:
          a           = f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
          raise ValueError(__UpperCAmelCase)
     # Verify that the target contains no genes besides the ones inside genes variable.
     a           = sorted({c for c in target if c not in genes})
     if not_in_genes_list:
          a           = f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(__UpperCAmelCase)

     # Generate random starting population.
     a           = []
     for _ in range(__UpperCAmelCase):
          population.append("".join([random.choice(__UpperCAmelCase) for i in range(len(__UpperCAmelCase))]))

     # Just some logs to know what the algorithms is doing.
     a    , a           = 0, 0

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

          # 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.
          a           = [evaluate(__UpperCAmelCase						,   __UpperCAmelCase) for item in population]

          # Check if there is a matching evolution.
          a           = sorted(__UpperCAmelCase						,   key=lambda __UpperCamelCase: x[1]						,   reverse=__UpperCAmelCase)
          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.
          a           = population[: int(N_POPULATION / 3)]
          population.clear()
          population.extend(__UpperCAmelCase)
          # Normalize population score to be between 0 and 1.
          a           = [
              (item, score / len(__UpperCAmelCase)) for item, score in population_score
          ]

          # This is selection
          for i in range(__UpperCAmelCase):
               population.extend(select(population_score[int(__UpperCAmelCase)]						,   __UpperCAmelCase						,   __UpperCAmelCase))
               # 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(__UpperCAmelCase) > N_POPULATION:
                    break


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






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def     __a					(		__UpperCAmelCase       ):
 if "model" in orig_key:
  a__												= orig_key.replace('''model.'''							,  ''''''       )
 if "norm1" in orig_key:
  a__												= orig_key.replace('''norm1'''							,  '''attention.output.LayerNorm'''       )
 if "norm2" in orig_key:
  a__												= orig_key.replace('''norm2'''							,  '''output.LayerNorm'''       )
 if "norm" in orig_key:
  a__												= orig_key.replace('''norm'''							,  '''LayerNorm'''       )
 if "transformer" in orig_key:
  a__												= orig_key.split('''.'''       )[0].split('''_'''       )[-1]
  a__												= orig_key.replace(f"transformer_{layer_num}"							,  f"encoder.layer.{layer_num}"       )
 if "mha.attn" in orig_key:
  a__												= orig_key.replace('''mha.attn'''							,  '''attention.self'''       )
 if "mha" in orig_key:
  a__												= orig_key.replace('''mha'''							,  '''attention'''       )
 if "W_q" in orig_key:
  a__												= orig_key.replace('''W_q'''							,  '''self.query'''       )
 if "W_k" in orig_key:
  a__												= orig_key.replace('''W_k'''							,  '''self.key'''       )
 if "W_v" in orig_key:
  a__												= orig_key.replace('''W_v'''							,  '''self.value'''       )
 if "ff1" in orig_key:
  a__												= orig_key.replace('''ff1'''							,  '''intermediate.dense'''       )
 if "ff2" in orig_key:
  a__												= orig_key.replace('''ff2'''							,  '''output.dense'''       )
 if "ff" in orig_key:
  a__												= orig_key.replace('''ff'''							,  '''output.dense'''       )
 if "mlm_class" in orig_key:
  a__												= orig_key.replace('''mlm.mlm_class'''							,  '''cls.predictions.decoder'''       )
 if "mlm" in orig_key:
  a__												= orig_key.replace('''mlm'''							,  '''cls.predictions.transform'''       )
 if "cls" not in orig_key:
  a__												= '''yoso.''' + orig_key

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   a__												= val

 a__												= orig_state_dict['''cls.predictions.decoder.bias''']
 a__												= torch.arange(__UpperCAmelCase       ).expand((1, -1)       ) + 2

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

 print(model.load_state_dict(__UpperCAmelCase       )       )
 model.eval()
 model.save_pretrained(__UpperCAmelCase       )

 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
     a_      :      int    =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





