



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a_      :      Union[str, Any]    =     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.
a_      :      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.
a_      :      str    =     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= len([g for position, g in enumerate(__UpperCAmelCase       ) if g == main_target[position]]       )
 return (item, float(__UpperCAmelCase       ))






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 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     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= list(__UpperCAmelCase       )
 if random.uniform(0							,  1       ) < MUTATION_PROBABILITY:
  a__												= random.choice(__UpperCAmelCase       )
 return "".join(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  ):
 a__												= []
 # Generate more children proportionally to the fitness score.
 a__												= int(parent_a[1] * 100       ) + 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     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase = True       ):

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










import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 a__												= (
     ('''layer.''', '''layer_'''),
     ('''word_embeddings.weight''', '''word_embeddings'''),
     ('''position_embeddings.weight''', '''position_embeddings'''),
     ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
     ('''.''', '''/'''),
     ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
     ('''LayerNorm/bias''', '''LayerNorm/beta'''),
     ('''weight''', '''kernel'''),
 )

 if not os.path.isdir(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   a__												= state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose       ):
    a__												= torch_tensor.T
   a__												= create_tf_var(tensor=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 a__												= BertModel.from_pretrained(
     pretrained_model_name_or_path=args.model_name							,  state_dict=torch.load(args.pytorch_model_path       )							,  cache_dir=args.cache_dir							,  )

 convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





