

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCAmelCase    				=     2_0_0
# 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.
__UpperCAmelCase    				=     5_0
# 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.
__UpperCAmelCase    				=     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[str, float]:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    len([g for position, g in enumerate(SCREAMING_SNAKE_CASE				) if g == main_target[position]]				)
     return (item, float(SCREAMING_SNAKE_CASE				))







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[str, str]:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    random.randint(0		,							len(SCREAMING_SNAKE_CASE				) - 1				)
     _lowerCAmelCase   :					str        =    parent_a[:random_slice] + parent_a[random_slice:]
     _lowerCAmelCase   :					Tuple        =    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    list(SCREAMING_SNAKE_CASE				)
     if random.uniform(0		,							1				) < MUTATION_PROBABILITY:
          _lowerCAmelCase   :					Tuple        =    random.choice(SCREAMING_SNAKE_CASE				)
     return "".join(SCREAMING_SNAKE_CASE				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							)       ->      list[str]:

     """simple docstring"""







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

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = True				)       ->      tuple[int, int, str]:

     """simple docstring"""







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

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

     # Just some logs to know what the algorithms is doing.
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    0, 0

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

          # 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.
          _lowerCAmelCase   :					List[Any]        =    [evaluate(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				) for item in population]

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

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


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









from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					int        =    rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
          restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
          _lowerCAmelCase   :					str        =    {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE				)}
          restype_atomaa_to_atomaa_list.append(
              [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

          restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

     # Add dummy mapping for restype 'UNK'
     restype_atomaa_to_atomaa_list.append([0] * 14				)
     restype_atomaa_to_atomaa_list.append([0] * 37				)
     restype_atomaa_mask_list.append([0.0] * 14				)

     _lowerCAmelCase   :					List[str]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					str        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.floataa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					List[str]        =    protein["aatype"].to(torch.long				)

     # create the mapping for (residx, atom14) --> atom37, i.e. an array
     # with shape (num_res, 14) containing the atom37 indices for this protein
     _lowerCAmelCase   :					Dict        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					Optional[Any]        =    restype_atomaa_mask[protein_aatype]

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     _lowerCAmelCase   :					Optional[int]        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					str        =    residx_atomaa_to_atomaa.long()

     # create the corresponding mask
     _lowerCAmelCase   :					Dict        =    torch.zeros([21, 37]		,							dtype=torch.floataa		,							device=protein["aatype"].device				)
     for restype, restype_letter in enumerate(rc.restypes				):
          _lowerCAmelCase   :					Optional[Any]        =    rc.restype_atoa[restype_letter]
          _lowerCAmelCase   :					int        =    rc.residue_atoms[restype_name]
          for atom_name in atom_names:
               _lowerCAmelCase   :					Union[str, Any]        =    rc.atom_order[atom_name]
               _lowerCAmelCase   :					Dict        =    1

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    tree_map(lambda SCREAMING_SNAKE_CASE				: torch.tensor(SCREAMING_SNAKE_CASE		,							device=batch["aatype"].device				)		,							SCREAMING_SNAKE_CASE		,							np.ndarray				)
     _lowerCAmelCase   :					Optional[int]        =    tensor_tree_map(lambda SCREAMING_SNAKE_CASE				: np.array(SCREAMING_SNAKE_CASE				)		,							make_atomaa_masks(SCREAMING_SNAKE_CASE				)				)
     return out






