






'''simple docstring'''




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 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						tuple[str, float]:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						tuple[str, str]:

      '''simple docstring'''


      _A			     =	random.randint(0   ,  len(__lowercase       ) - 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 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						str:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  )       ->						list[str]:

      '''simple docstring'''


      _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(__lowercase       ):
            _A			     =	population_score[random.randint(0   ,  __lowercase       )][0]

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

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

      '''simple docstring'''


      if N_POPULATION < N_SELECTED:
            _A			     =	F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
            raise ValueError(__lowercase       )
      # 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(__lowercase       )

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

      # 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(__lowercase       )

            # 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(__lowercase   ,  __lowercase       ) for item in population]

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

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

import numpy as np
import torch

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


def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[Any]							=      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]    )
              snake_case       :   str							=      {name: i for i, name in enumerate(__lowerCamelCase    )}
              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    )

       snake_case       :   Optional[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   List[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.floataa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      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
       snake_case       :   List[Any]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   str							=      restype_atomaa_mask[protein_aatype]

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

       # create the gather indices for mapping back
       snake_case       :   List[str]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   List[Any]							=      residx_atomaa_to_atomaa.long()

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      residx_atomaa_mask

       return protein




def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   Dict							=      tree_map(lambda __lowerCamelCase    : torch.tensor(__lowerCamelCase				,					device=batch["aatype"].device    )				,					__lowerCamelCase				,					np.ndarray    )
       snake_case       :   List[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





