






'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:      Optional[Any]  				=				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.
A:      int  				=				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.
A:      int  				=				0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))






def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			tuple[str, float]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			len([g for position, g in enumerate(a							) if g == main_target[position]]							)
						return (item, float(a							))






def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			tuple[str, str]:





						"""simple docstring"""
						lowercase_      :   Dict        =			random.randint(0						,			len(a							) - 1							)
						lowercase_      :   List[Any]        =			parent_a[:random_slice] + parent_a[random_slice:]
						lowercase_      :   List[str]        =			parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def 	_UpperCAmelCase    (  a		:	str						,			a		:	list[str]							)			->			str:





						"""simple docstring"""
						lowercase_      :   int        =			list(a							)
						if random.uniform(0						,			1							) < MUTATION_PROBABILITY:
												lowercase_      :   List[str]        =			random.choice(a							)
						return "".join(a							)






def 	_UpperCAmelCase    (  a		:	tuple[str, float]						,			a		:	list[tuple[str, float]]						,			a		:	list[str]						,			)			->			list[str]:





						"""simple docstring"""
						lowercase_      :   int        =			[]
						# Generate more children proportionally to the fitness score.
						lowercase_      :   List[str]        =			int(parent_a[1] * 1_0_0							) + 1
						lowercase_      :   Optional[Any]        =			1_0 if child_n >= 1_0 else child_n
						for _ in range(a							):
												lowercase_      :   List[str]        =			population_score[random.randint(0						,			a							)][0]

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






def 	_UpperCAmelCase    (  a		:	str						,			a		:	list[str]						,			a		:	bool = True							)			->			tuple[int, int, str]:





						"""simple docstring"""

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

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

						# Just some logs to know what the algorithms is doing.
						lowercase_      :   Optional[Any]        =			0, 0

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

												# 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.
												lowercase_      :   Optional[Any]        =			[evaluate(a						,			a							) for item in population]

												# Check if there is a matching evolution.
												lowercase_      :   int        =			sorted(a						,			key=lambda a							: x[1]						,			reverse=a							)
												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 % 1_0 == 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.
												lowercase_      :   int        =			population[: int(N_POPULATION / 3							)]
												population.clear()
												population.extend(a							)
												# Normalize population score to be between 0 and 1.
												lowercase_      :   Union[str, Any]        =			[
												    (item, score / len(a							)) for item, score in population_score
												]

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


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








'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
