
from __future__ import annotations

import random

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



def 						lowercase_      (       A__			,		A__		)							->							List[Any]:
				"""simple docstring"""

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



def 						lowercase_      (       A__			,		A__		)							->							Dict:
				"""simple docstring"""

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



def 						lowercase_      (       A__			,		A__		)							->							List[Any]:
				"""simple docstring"""

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



def 						lowercase_      (       A__			,		A__			,		A__			,		)							->							str:
				"""simple docstring"""

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

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



def 						lowercase_      (       A__			,		A__			,		A__ = True		)							->							Any:
				"""simple docstring"""

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

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

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

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

								# 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.
								snake_case								= [evaluate(lowercase__			,		lowercase__		) for item in population]

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

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


if __name__ == "__main__":
			_A   =			(
			    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
			)
			_A   =			list(
			    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
			    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
			)
			_A ,  _A ,  _A   =			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_bert import BertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

_A   =			{
    "bert-base-uncased": 5_12,
    "bert-large-uncased": 5_12,
    "bert-base-cased": 5_12,
    "bert-large-cased": 5_12,
    "bert-base-multilingual-uncased": 5_12,
    "bert-base-multilingual-cased": 5_12,
    "bert-base-chinese": 5_12,
    "bert-base-german-cased": 5_12,
    "bert-large-uncased-whole-word-masking": 5_12,
    "bert-large-cased-whole-word-masking": 5_12,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_12,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_12,
    "bert-base-cased-finetuned-mrpc": 5_12,
    "bert-base-german-dbmdz-cased": 5_12,
    "bert-base-german-dbmdz-uncased": 5_12,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_12,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_12,
    "wietsedv/bert-base-dutch-cased": 5_12,
}

_A   =			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Union[str, Any]									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Union[str, Any]									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Any									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							int									=		BertTokenizer





     def __init__(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=None    ,   _A				:   Optional[int]=None    ,   _A				:   List[str]=True    ,   _A				:   List[Any]="[UNK]"    ,   _A				:   Union[str, Any]="[SEP]"    ,   _A				:   List[Any]="[PAD]"    ,   _A				:   List[Any]="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   int=True    ,   _A				:   Tuple=None    ,   **_A				:   Optional[int]    ,   )						->						int:
         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    ,   )

         snake_case								= 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
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Union[str, Any]    ,   _A				:   int=None       )						->						Any:
         snake_case								= [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 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

