




from __future__ import annotations

import random

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







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							len([g for position, g in enumerate(__A				) if g == main_target[position]]				)
					return (item, float(__A				))







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	str    							=							random.randint(0							,len(__A				) - 1				)
					__magic_name__							:	Optional[Any]    							=							parent_a[:random_slice] + parent_a[random_slice:]
					__magic_name__							:	List[str]    							=							parent_a[:random_slice] + parent_a[random_slice:]
					return (child_a, child_a)







def 		lowercase__   (		__A:	str							,__A:	list[str]				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							list(__A				)
					if random.uniform(0							,1				) < MUTATION_PROBABILITY:
										__magic_name__							:	List[Any]    							=							random.choice(__A				)
					return "".join(__A				)







def 		lowercase__   (		__A:	tuple[str, float]							,__A:	list[tuple[str, float]]							,__A:	list[str]							,):





					'''simple docstring'''






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

										__magic_name__							,    __magic_name__							:	Dict    							=							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 		lowercase__   (		__A:	str							,__A:	list[str]							,__A:	bool = True				):





					'''simple docstring'''






					if N_POPULATION < N_SELECTED:
										__magic_name__							:	Dict    							=							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.
					__magic_name__							:	List[Any]    							=							sorted({c for c in target if c not in genes}				)
					if not_in_genes_list:
										__magic_name__							:	List[str]    							=							F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
										raise ValueError(__A				)

					# Generate random starting population.
					__magic_name__							:	Union[str, Any]    							=							[]
					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.
					__magic_name__							,    __magic_name__							:	Dict    							=							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.
										__magic_name__							:	Tuple    							=							[evaluate(__A							,__A				) for item in population]

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









import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowerCamelCase   :						List[Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Any    							=							os.getenv('''SM_HP_MP_PARAMETERS'''							,'''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__magic_name__							:	Tuple    							=							json.loads(__A				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__magic_name__							:	int    							=							os.getenv('''SM_FRAMEWORK_PARAMS'''							,'''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__magic_name__							:	List[Any]    							=							json.loads(__A				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''							,__A				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
	import smdistributed.modelparallel.torch as smp

	smp.init()


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=field(
			    default=''''''				,metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}				,)

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''       ,						lowerCamelCase_       ,						)

			@cached_property
			def 							UpperCAmelCase__       ( self			:   int  )						->   "torch.device":
								logger.info('''PyTorch: setting up devices'''  )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''  )
								if self.no_cuda:
													__magic_name__							:	Optional[int]    							=							torch.device('''cpu'''  )
													__magic_name__							:	int    							=							0
								elif is_sagemaker_model_parallel_available():
													__magic_name__							:	Any    							=							smp.local_rank()
													__magic_name__							:	int    							=							torch.device('''cuda'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	Optional[int]    							=							int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
													__magic_name__							:	Optional[int]    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	str    							=							1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													__magic_name__							:	List[str]    							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													__magic_name__							:	Dict    							=							torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	str    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	Dict    							=							1

								if device.type == "cuda":
													torch.cuda.set_device(lowerCamelCase_  )

								return device

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   int:
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[str]:
								return not is_sagemaker_model_parallel_available()


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return False



