




"""simple docstring"""




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[Any]     							=							{"vocab_file": "spiece.model"}

lowerCAmelCase							:			Union[str, Any]     							=							{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

lowerCAmelCase							:			List[Any]     							=							{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
lowerCAmelCase							:			str     							=							0
lowerCAmelCase							:			str     							=							1
lowerCAmelCase							:			List[str]     							=							2
lowerCAmelCase							:			Optional[Any]     							=							3
lowerCAmelCase							:			List[Any]     							=							4






class     __magic_name__							(       _lowerCamelCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						"left"





				def __init__(       self		, _a		, _a=False		, _a=True		, _a=False		, _a="<s>"		, _a="</s>"		, _a="<unk>"		, _a="<sep>"		, _a="<pad>"		, _a="<cls>"		, _a="<mask>"		, _a=["<eop>", "<eod>"]		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					AddedToken(lowerCAmelCase_		, lstrip=lowerCAmelCase_		, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		, lowerCAmelCase_ ) else mask_token

							lowerCamelCase			      =					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=lowerCAmelCase_		, remove_space=lowerCAmelCase_		, keep_accents=lowerCAmelCase_		, bos_token=lowerCAmelCase_		, eos_token=lowerCAmelCase_		, unk_token=lowerCAmelCase_		, sep_token=lowerCAmelCase_		, pad_token=lowerCAmelCase_		, cls_token=lowerCAmelCase_		, mask_token=lowerCAmelCase_		, additional_special_tokens=lowerCAmelCase_		, sp_model_kwargs=self.sp_model_kwargs		, **lowerCAmelCase_		, )

							lowerCamelCase			      =					3

							lowerCamelCase			      =					do_lower_case
							lowerCamelCase			      =					remove_space
							lowerCamelCase			      =					keep_accents
							lowerCamelCase			      =					vocab_file

							lowerCamelCase			      =					spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(lowerCAmelCase_ )





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return len(self.sp_model )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					{self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab





				def __getstate__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.__dict__.copy()
							lowerCamelCase			      =					None
							return state





				def __setstate__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					d

							# for backward compatibility
							if not hasattr(self		, """sp_model_kwargs""" ):
										lowerCamelCase			      =					{}

							lowerCamelCase			      =					spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""






							if self.remove_space:
										lowerCamelCase			      =					""" """.join(inputs.strip().split() )
							else:
										lowerCamelCase			      =					inputs
							lowerCamelCase			      =					outputs.replace("""``"""		, """\"""" ).replace("""\'\'"""		, """\"""" )

							if not self.keep_accents:
										lowerCamelCase			      =					unicodedata.normalize("""NFKD"""		, lowerCAmelCase_ )
										lowerCamelCase			      =					"""""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase_ )] )
							if self.do_lower_case:
										lowerCamelCase			      =					outputs.lower()

							return outputs





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.preprocess_text(lowerCAmelCase_ )
							lowerCamelCase			      =					self.sp_model.encode(lowerCAmelCase_		, out_type=lowerCAmelCase_ )
							lowerCamelCase			      =					[]
							for piece in pieces:
										if len(lowerCAmelCase_ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
													lowerCamelCase			      =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase_		, """""" ) )
													if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																if len(cur_pieces[0] ) == 1:
																			lowerCamelCase			      =					cur_pieces[1:]
																else:
																			lowerCamelCase			      =					cur_pieces[0][1:]
													cur_pieces.append(piece[-1] )
													new_pieces.extend(lowerCAmelCase_ )
										else:
													new_pieces.append(lowerCAmelCase_ )

							return new_pieces





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return self.sp_model.PieceToId(lowerCAmelCase_ )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return self.sp_model.IdToPiece(lowerCAmelCase_ )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					"""""".join(lowerCAmelCase_ ).replace(lowerCAmelCase_		, """ """ ).strip()
							return out_string





				def    _lowerCAmelCase				(       self		, _a		, _a = False		, _a = None		, _a = True		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					kwargs.pop("""use_source_tokenizer"""		, lowerCAmelCase_ )

							lowerCamelCase			      =					self.convert_ids_to_tokens(lowerCAmelCase_		, skip_special_tokens=lowerCAmelCase_ )

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]
							for token in filtered_tokens:
										if skip_special_tokens and token in self.all_special_ids:
													continue
										if token in self.added_tokens_encoder:
													if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )
																lowerCamelCase			      =					[]
													sub_texts.append(lowerCAmelCase_ )
										else:
													current_sub_text.append(lowerCAmelCase_ )
							if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )

							# Mimic the behavior of the Rust tokenizer:
							# By default, there are no spaces between special tokens
							lowerCamelCase			      =					"""""".join(lowerCAmelCase_ )

							lowerCamelCase			      =					(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
										lowerCamelCase			      =					self.clean_up_tokenization(lowerCAmelCase_ )
										return clean_text
							else:
										return text





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[self.cls_token_id]
							if token_ids_a is None:
										return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = False ):





							"""simple docstring"""






							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=lowerCAmelCase_		, token_ids_a=lowerCAmelCase_		, already_has_special_tokens=lowerCAmelCase_ )

							if token_ids_a is not None:
										return ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1, 1]
							return ([0] * len(lowerCAmelCase_ )) + [1, 1]





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[2]

							if token_ids_a is None:
										return len(token_ids_a + sep ) * [0] + cls_segment_id
							return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""






							if not os.path.isdir(lowerCAmelCase_ ):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
										return
							lowerCamelCase			      =					os.path.join(
							    lowerCAmelCase_		, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.vocab_file ):
										copyfile(self.vocab_file		, lowerCAmelCase_ )
							elif not os.path.isfile(self.vocab_file ):
										with open(lowerCAmelCase_		, """wb""" ) as fi:
													lowerCamelCase			      =					self.sp_model.serialized_model_proto()
													fi.write(lowerCAmelCase_ )

							return (out_vocab_file,)





'''simple docstring'''







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase    :							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.
lowercase    :							Optional[int]      =					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    :							Optional[Any]      =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, str]:
	_snake_case   						=		random.randint(0			,				len(__A	) - 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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				)       ->	list[str]:
	_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(__A	):
		_snake_case   						=		population_score[random.randint(0			,				__A	)][0]

		_snake_case						, _snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		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.
	_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(__A	)

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

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

