


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


lowerCAmelCase__			:      Dict								=logging.get_logger(__name__)

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

lowerCAmelCase__			:      Optional[Any]								={
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__			:      Optional[Any]								={
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}







class 	__lowercase    (A__  ):





							"""simple docstring"""







							_UpperCAmelCase      =						VOCAB_FILES_NAMES
							_UpperCAmelCase      =						PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      =						["""input_ids""", """attention_mask"""]
							_UpperCAmelCase      =						GPTaTokenizer






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__="<|endoftext|>"			,						lowerCAmelCase__="<|endoftext|>"			,						lowerCAmelCase__="<|endoftext|>"			,						lowerCAmelCase__=False			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(
										    snake_case_			,						snake_case_			,						tokenizer_file=snake_case_			,						unk_token=snake_case_			,						bos_token=snake_case_			,						eos_token=snake_case_			,						add_prefix_space=snake_case_			,						**snake_case_			,						)

										SCREAMING_SNAKE_CASE_		:     List[str]					=					kwargs.pop('add_bos_token'			,						snake_case_       )

										SCREAMING_SNAKE_CASE_		:     str					=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
										if pre_tok_state.get('add_prefix_space'			,						snake_case_       ) != add_prefix_space:
													SCREAMING_SNAKE_CASE_		:     int					=					getattr(snake_case_			,						pre_tok_state.pop('type'       )       )
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					add_prefix_space
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pre_tok_class(**snake_case_       )

										SCREAMING_SNAKE_CASE_		:     str					=					add_prefix_space






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					kwargs.get('is_split_into_words'			,						snake_case_       )
										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*snake_case_			,						**snake_case_       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					kwargs.get('is_split_into_words'			,						snake_case_       )

										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*snake_case_			,						**snake_case_       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self._tokenizer.model.save(snake_case_			,						name=snake_case_       )
										return tuple(snake_case_       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					[]
										for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(snake_case_			,						add_special_tokens=snake_case_       ) + [self.eos_token_id]       )

										if len(snake_case_       ) > self.model_max_length:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					input_ids[-self.model_max_length :]
										return input_ids







"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  len([g for position, g in enumerate(_lowerCAmelCase						) if g == main_target[position]]						)
						return (item, float(_lowerCAmelCase						))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  random.randint(0 ,    len(_lowerCAmelCase						) - 1						)
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  list(_lowerCAmelCase						)
						if random.uniform(0 ,    1						) < MUTATION_PROBABILITY:
												__lowerCAmelCase     =  random.choice(_lowerCAmelCase						)
						return "".join(_lowerCAmelCase						)

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

												__lowerCAmelCase   ,     __lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True						):

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												__lowerCAmelCase     =  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     =  sorted({c for c in target if c not in genes}						)
						if not_in_genes_list:
												__lowerCAmelCase     =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_lowerCAmelCase						)

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

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