


"""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     :   Any			    =			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , ):

			'''simple docstring'''




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

						__lowerCAmelCase    ,	__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase = True      ):

			'''simple docstring'''




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

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

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








"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




