

"""simple docstring"""




from __future__ import annotations

import random

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





def       lowercase__						(       snake_case_   :str			,		snake_case_   :str    ):
	__UpperCAmelCase      		= len([g for position, g in enumerate(snake_case_    ) if g == main_target[position]]    )
	return (item, float(snake_case_    ))





def       lowercase__						(       snake_case_   :str			,		snake_case_   :str    ):
	__UpperCAmelCase      		= random.randint(0			,		len(snake_case_    ) - 1    )
	__UpperCAmelCase      		= parent_a[:random_slice] + parent_a[random_slice:]
	__UpperCAmelCase      		= parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)





def       lowercase__						(       snake_case_   :str			,		snake_case_   :list[str]    ):
	__UpperCAmelCase      		= list(snake_case_    )
	if random.uniform(0			,		1    ) < MUTATION_PROBABILITY:
		__UpperCAmelCase      		= random.choice(snake_case_    )
	return "".join(snake_case_    )





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

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





def       lowercase__						(       snake_case_   :str			,		snake_case_   :list[str]			,		snake_case_   :bool = True    ):

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		__UpperCAmelCase      		= F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
		raise ValueError(snake_case_    )
	# Verify that the target contains no genes besides the ones inside genes variable.
	__UpperCAmelCase      		= sorted({c for c in target if c not in genes}    )
	if not_in_genes_list:
		__UpperCAmelCase      		= F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
		raise ValueError(snake_case_    )

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

	# Just some logs to know what the algorithms is doing.
	__UpperCAmelCase		,							__UpperCAmelCase      		= 0, 0

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

		# 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.
		__UpperCAmelCase      		= [evaluate(snake_case_			,		snake_case_    ) for item in population]

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

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


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




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE__					:     List[str]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'yjernite/retribert-base-uncased': 512,
}


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE     					=    RetriBertTokenizer
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


     def __init__(  self							,					A_=None							,					A_=None							,					A_=True							,					A_="[UNK]"							,					A_="[SEP]"							,					A_="[PAD]"							,					A_="[CLS]"							,					A_="[MASK]"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(
           A_							,					tokenizer_file=A_							,					do_lower_case=A_							,					unk_token=A_							,					sep_token=A_							,					pad_token=A_							,					cls_token=A_							,					mask_token=A_							,					tokenize_chinese_chars=A_							,					strip_accents=A_							,					**A_							,					)

       _UpperCAmelCase		:							List[str]							=     json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           normalizer_state.get("""lowercase"""							,					A_    ) != do_lower_case
           or normalizer_state.get("""strip_accents"""							,					A_    ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""							,					A_    ) != tokenize_chinese_chars
       ):
         _UpperCAmelCase		:							str							=     getattr(A_							,					normalizer_state.pop("""type"""    )    )
         _UpperCAmelCase		:							Optional[int]							=     do_lower_case
         _UpperCAmelCase		:							List[Any]							=     strip_accents
         _UpperCAmelCase		:							List[str]							=     tokenize_chinese_chars
         _UpperCAmelCase		:							Dict							=     normalizer_class(**A_    )

       _UpperCAmelCase		:							Dict							=     do_lower_case


     def        __snake_case(  self							,					A_							,					A_=None    ):
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Tuple							=     [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    ) * [0] + len(token_ids_a + sep    ) * [1]




     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Dict							=     self._tokenizer.model.save(A_							,					name=A_    )
       return tuple(A_    )





