






import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_a      	=  logging.getLogger(__name__)


def __A    (      )->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			argparse.ArgumentParser(
						    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'	)
						parser.add_argument(
						    '--dataset_name' ,		type=_UpperCAmelCase ,		default='wikitext' ,		help='Name of the training. Explore datasets at: hf.co/datasets.' ,		)
						parser.add_argument(
						    '--dataset_config' ,		type=_UpperCAmelCase ,		default='wikitext-103-raw-v1' ,		help='Configuration name of the dataset.'	)
						parser.add_argument(
						    '--tokenizer_name_or_path' ,		type=_UpperCAmelCase ,		default='sayakpaul/unigram-tokenizer-wikitext' ,		help='Tokenizer identifier. Can be a local filepath or a Hub identifier.' ,		)
						parser.add_argument(
						    '--shard_size' ,		type=_UpperCAmelCase ,		default=1_000 ,		help='Number of entries to go in a single shard.' ,		)
						parser.add_argument('--split' ,		type=_UpperCAmelCase ,		default='train' ,		choices=['train', 'test', 'validation']	)
						parser.add_argument(
						    '--limit' ,		default=_UpperCAmelCase ,		type=_UpperCAmelCase ,		help='Limit the number of shards (used for debugging).' ,		)
						parser.add_argument(
						    '--max_length' ,		type=_UpperCAmelCase ,		default=512 ,		help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
						    ' sequence length that is a multiple of 8.' ,		)
						parser.add_argument(
						    '--output_dir' ,		default='tf-tpu' ,		type=_UpperCAmelCase ,		help='Output directory where the TFRecord shards will be saved. If the'
						    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
						    ' shards will be directly saved to a Google Cloud Storage bucket.' ,		)

						_UpperCAmelCase									=			parser.parse_args()
						return args


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						def fn(__lowerCAmelCase	):
												return tokenizer(examples['text']	)

						return fn


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						for i in range(len(tokenized_data['input_ids']	)	):
												_UpperCAmelCase									=			{
												    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]	)	),
												    'attention_mask': tf.train.Feature(
												        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]	)	),
												}
												_UpperCAmelCase									=			tf.train.Features(feature=_UpperCAmelCase	)
												_UpperCAmelCase									=			tf.train.Example(features=_UpperCAmelCase	)
												_UpperCAmelCase									=			example.SerializeToString()
												records.append(_UpperCAmelCase	)
						return records


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			datasets.load_dataset(args.dataset_name ,		args.dataset_config ,		split=args.split	)

						if args.limit is not None:
												_UpperCAmelCase									=			min(len(_UpperCAmelCase	) ,		args.limit	)
												_UpperCAmelCase									=			dataset.select(range(_UpperCAmelCase	)	)
												print(F"""Limiting the dataset to {args.limit} entries."""	)

						_UpperCAmelCase									=			AutoTokenizer.from_pretrained(args.tokenizer_name_or_path	)

						# Handle output directory creation.
						# For serializing into a Google Cloud Storage Bucket, one needs to first
						# create a bucket.
						if "gs" not in args.output_dir:
												if not os.path.exists(args.output_dir	):
																		os.makedirs(args.output_dir	)
												_UpperCAmelCase									=			os.path.join(args.output_dir ,		args.split	)
												if not os.path.exists(_UpperCAmelCase	):
																		os.makedirs(_UpperCAmelCase	)
						else:
												_UpperCAmelCase									=			os.path.join(args.output_dir ,		args.split	)

						# Tokenize the whole dataset at once.
						_UpperCAmelCase									=			tokenize_function(_UpperCAmelCase	)
						_UpperCAmelCase									=			dataset.map(_UpperCAmelCase ,		batched=_UpperCAmelCase ,		num_proc=4 ,		remove_columns=['text']	)

						# We need to concatenate all our texts together, and then split the result
						# into chunks of a fixed size, which we will call block_size. To do this, we
						# will use the map method again, with the option batched=True. When we use batched=True,
						# the function we pass to map() will be passed multiple inputs at once, allowing us
						# to group them into more or fewer examples than we had in the input.
						# This allows us to create our new fixed-length samples. The advantage of this
						# method is that we don't lose a whole lot of content from the dataset compared to the
						# case where we simply tokenize with a pre-defined max_length.

						def group_texts(__lowerCAmelCase	):
												# Concatenate all texts.
												_UpperCAmelCase									=			{k: sum(examples[k] ,		[]	) for k in examples.keys()}
												_UpperCAmelCase									=			len(concatenated_examples[list(examples.keys()	)[0]]	)
												# We drop the small remainder, though you could add padding instead if the model supports it
												# In this, as in all things, we advise you to follow your heart 🫀
												_UpperCAmelCase									=			(total_length // args.max_length) * args.max_length
												# Split by chunks of max_len.
												_UpperCAmelCase									=			{
												    k: [t[i : i + args.max_length] for i in range(0 ,		_UpperCAmelCase ,		args.max_length	)]
												    for k, t in concatenated_examples.items()
												}
												return result

						_UpperCAmelCase									=			dataset_tokenized.map(_UpperCAmelCase ,		batched=_UpperCAmelCase ,		batch_size=1_000 ,		num_proc=4	)

						_UpperCAmelCase									=			0
						_UpperCAmelCase									=			0
						for shard in range(0 ,		len(_UpperCAmelCase	) ,		args.shard_size	):
												_UpperCAmelCase									=			grouped_dataset[shard : shard + args.shard_size]
												_UpperCAmelCase									=			len(dataset_snapshot['input_ids']	)
												_UpperCAmelCase									=			os.path.join(_UpperCAmelCase ,		F"""dataset-{shard_count}-{records_containing}.tfrecord"""	)
												_UpperCAmelCase									=			get_serialized_examples(_UpperCAmelCase	)

												with tf.io.TFRecordWriter(_UpperCAmelCase	) as out_file:
																		for i in range(len(_UpperCAmelCase	)	):
																								_UpperCAmelCase									=			serialized_examples[i]
																								out_file.write(_UpperCAmelCase	)
																		print('Wrote file {} containing {} records'.format(_UpperCAmelCase ,		_UpperCAmelCase	)	)

												shard_count += 1
												total_records += records_containing

						with open(F"""split-{args.split}-records-count.txt""" ,		'w'	) as f:
												print(F"""Total {args.split} records: {total_records}""" ,		file=_UpperCAmelCase	)


if __name__ == "__main__":
		_a      	=  parse_args()
		main(args)





from __future__ import annotations

import random

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				len([g for position, g in enumerate(_UpperCAmelCase				) if g == main_target[position]]				)
    return (item, float(_UpperCAmelCase				))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				random.randint(0   , len(_UpperCAmelCase				) - 1				)
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				list(_UpperCAmelCase				)
    if random.uniform(0   , 1				) < MUTATION_PROBABILITY:
        __a						       =				random.choice(_UpperCAmelCase				)
    return "".join(_UpperCAmelCase				)





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

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = True				):

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

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

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

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

        # 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.
        __a						       =				[evaluate(_UpperCAmelCase   , _UpperCAmelCase				) for item in population]

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

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


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





