





'''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									=				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.
_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, 10_00))


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       str     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       str     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   random.randint(0		,							len(SCREAMING_SNAKE_CASE_     ) - 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 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       list[str]     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       tuple[str, float]		,							SCREAMING_SNAKE_CASE_	:       list[tuple[str, float]]		,							SCREAMING_SNAKE_CASE_	:       list[str]		,							):



    '''simple docstring'''





    _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(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   population_score[random.randint(0		,							SCREAMING_SNAKE_CASE_     )][0]

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


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       list[str]		,							SCREAMING_SNAKE_CASE_	:       bool = True     ):



    '''simple docstring'''





    if N_POPULATION < N_SELECTED:
        _lowerCAmelCase						      =   F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
        raise ValueError(SCREAMING_SNAKE_CASE_     )
    # 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(SCREAMING_SNAKE_CASE_     )

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

    # 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(SCREAMING_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.
        _lowerCAmelCase						      =   [evaluate(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ) for item in population]

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

        # This is selection
        for i in range(SCREAMING_SNAKE_CASE_     ):
            population.extend(select(population_score[int(SCREAMING_SNAKE_CASE_     )]		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_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(SCREAMING_SNAKE_CASE_     ) > 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}'''
       )









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "gpt_bigcode"
 __lowerCamelCase    :				Optional[int]								=       ["past_key_values"]
 __lowerCamelCase    :				List[str]								=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__(						self      ,  _lowerCAmelCase=50257      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="gelu_pytorch_tanh"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   n_positions
     _lowerCAmelCase						      =   n_embd
     _lowerCAmelCase						      =   n_layer
     _lowerCAmelCase						      =   n_head
     _lowerCAmelCase						      =   n_inner
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   resid_pdrop
     _lowerCAmelCase						      =   embd_pdrop
     _lowerCAmelCase						      =   attn_pdrop
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scale_attn_weights
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   attention_softmax_in_fpaa
     _lowerCAmelCase						      =   scale_attention_softmax_in_fpaa
     _lowerCAmelCase						      =   multi_query

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)


