

from __future__ import annotations

import random

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     tuple[str, float]:



  """simple docstring"""






  _snake_case						=							len([g for position, g in enumerate(_UpperCamelCase					) if g == main_target[position]]					)
  return (item, float(_UpperCamelCase					))



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     tuple[str, str]:



  """simple docstring"""






  _snake_case						=							random.randint(0			,	len(_UpperCamelCase					) - 1					)
  _snake_case						=							parent_a[:random_slice] + parent_a[random_slice:]
  _snake_case						=							parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							list(_UpperCamelCase					)
  if random.uniform(0			,	1					) < MUTATION_PROBABILITY:
    _snake_case						=							random.choice(_UpperCamelCase					)
  return "".join(_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	)							->     list[str]:



  """simple docstring"""






  _snake_case						=							[]
  # Generate more children proportionally to the fitness score.
  _snake_case						=							int(parent_a[1] * 100					) + 1
  _snake_case						=							10 if child_n >= 10 else child_n
  for _ in range(_UpperCamelCase					):
    _snake_case						=							population_score[random.randint(0			,	_UpperCamelCase					)][0]

    _snake_case,					_snake_case						=							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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = True					)							->     tuple[int, int, str]:



  """simple docstring"""






  if N_POPULATION < N_SELECTED:
    _snake_case						=							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.
  _snake_case						=							sorted({c for c in target if c not in genes}					)
  if not_in_genes_list:
    _snake_case						=							F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
    raise ValueError(_UpperCamelCase					)

  # Generate random starting population.
  _snake_case						=							[]
  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.
  _snake_case,					_snake_case						=							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.
    _snake_case						=							[evaluate(_UpperCamelCase			,	_UpperCamelCase					) for item in population]

    # Check if there is a matching evolution.
    _snake_case						=							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.
    _snake_case						=							population[: int(N_POPULATION / 3					)]
    population.clear()
    population.extend(_UpperCamelCase					)
    # Normalize population score to be between 0 and 1.
    _snake_case						=							[
        (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      		=     (
     '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
 )
 __A      		=     list(
     ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
     '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
 )
 __A		,					__A		,					__A      		=     basic(target_str, genes_list)
 print(
     f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
 )




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict()			,	_UpperCamelCase					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __A      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

