






"""simple docstring"""



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A__	:    Dict			 = 200
# 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__	:    List[Any]			 = 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.
A__	:    str			 = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str  )    ->     tuple[str, float]:
							lowerCamelCase_	:							List[str]									=len([g for position, g in enumerate(lowerCamelCase__  ) if g == main_target[position]]  )
							return (item, float(lowerCamelCase__  ))

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str  )    ->     tuple[str, str]:
							lowerCamelCase_	:							int									=random.randint(0    ,						len(lowerCamelCase__  ) - 1  )
							lowerCamelCase_	:							Dict									=parent_a[:random_slice] + parent_a[random_slice:]
							lowerCamelCase_	:							List[Any]									=parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   list[str]  )    ->     str:
							lowerCamelCase_	:							int									=list(lowerCamelCase__  )
							if random.uniform(0    ,						1  ) < MUTATION_PROBABILITY:
														lowerCamelCase_	:							Union[str, Any]									=random.choice(lowerCamelCase__  )
							return "".join(lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   tuple[str, float]    ,						lowerCamelCase__     :   list[tuple[str, float]]    ,						lowerCamelCase__     :   list[str]    ,						)    ->     list[str]:
							lowerCamelCase_	:							Any									=[]
							# Generate more children proportionally to the fitness score.
							lowerCamelCase_	:							List[Any]									=int(parent_a[1] * 100  ) + 1
							lowerCamelCase_	:							Optional[Any]									=10 if child_n >= 10 else child_n
							for _ in range(lowerCamelCase__  ):
														lowerCamelCase_	:							Optional[Any]									=population_score[random.randint(0    ,						lowerCamelCase__  )][0]

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

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

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

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

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

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

														# 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(lowerCamelCase__    ,						lowerCamelCase__  ) for item in population]

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

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


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











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[Any]									=[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



