






"""simple docstring"""







from __future__ import annotations

import random

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


def      UpperCamelCase       ( _A  ,				_A )     ->       tuple[str, float]:
				lowercase    :      List[str]       = len([g for position, g in enumerate(lowerCAmelCase__ ) if g == main_target[position]] )
				return (item, float(lowerCAmelCase__ ))


def      UpperCamelCase       ( _A  ,				_A )     ->       tuple[str, str]:
				lowercase    :      int       = random.randint(0  ,				len(lowerCAmelCase__ ) - 1 )
				lowercase    :      Union[str, Any]       = parent_a[:random_slice] + parent_a[random_slice:]
				lowercase    :      Optional[Any]       = parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)


def      UpperCamelCase       ( _A  ,				_A )     ->       str:
				lowercase    :      str       = list(lowerCAmelCase__ )
				if random.uniform(0  ,				1 ) < MUTATION_PROBABILITY:
								lowercase    :      str       = random.choice(lowerCAmelCase__ )
				return "".join(lowerCAmelCase__ )


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				)     ->       list[str]:
				lowercase    :      Tuple       = []
				# Generate more children proportionally to the fitness score.
				lowercase    :      List[Any]       = int(parent_a[1] * 100 ) + 1
				lowercase    :      Optional[int]       = 10 if child_n >= 10 else child_n
				for _ in range(lowerCAmelCase__ ):
								lowercase    :      Dict       = population_score[random.randint(0  ,				lowerCAmelCase__ )][0]

								lowercase      ,      lowercase    :      List[str]       = 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      UpperCamelCase       ( _A  ,				_A  ,				_A = True )     ->       tuple[int, int, str]:


				if N_POPULATION < N_SELECTED:
								lowercase    :      Tuple       = 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.
				lowercase    :      Tuple       = sorted({c for c in target if c not in genes} )
				if not_in_genes_list:
								lowercase    :      Tuple       = F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
								raise ValueError(lowerCAmelCase__ )

				# Generate random starting population.
				lowercase    :      Optional[int]       = []
				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.
				lowercase      ,      lowercase    :      Dict       = 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.
								lowercase    :      Any       = [evaluate(lowerCAmelCase__  ,				lowerCAmelCase__ ) for item in population]

								# Check if there is a matching evolution.
								lowercase    :      Dict       = sorted(lowerCAmelCase__  ,				key=lambda _A : 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.
								lowercase    :      Union[str, Any]       = population[: int(N_POPULATION / 3 )]
								population.clear()
								population.extend(lowerCAmelCase__ )
								# Normalize population score to be between 0 and 1.
								lowercase    :      Optional[int]       = [
								    (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__":
				_lowerCAmelCase            =	(
				    """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
				)
				_lowerCAmelCase            =	list(
				    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
				    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
				)
				_lowerCAmelCase            =	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


__lowerCAmelCase       :			int   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Optional[Any]   	={
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'mra'

      def __init__(		self						,			__lowerCAmelCase=5_0265						,			__lowerCAmelCase=768						,			__lowerCAmelCase=12						,			__lowerCAmelCase=12						,			__lowerCAmelCase=3072						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1E-5						,			__lowerCAmelCase="absolute"						,			__lowerCAmelCase=4						,			__lowerCAmelCase="full"						,			__lowerCAmelCase=0						,			__lowerCAmelCase=0						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0						,			__lowerCAmelCase=2						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase						,			bos_token_id=__lowerCAmelCase						,			eos_token_id=__lowerCAmelCase						,			**__lowerCAmelCase		)

            lowercase								=      vocab_size
            lowercase								=      max_position_embeddings
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      initializer_range
            lowercase								=      type_vocab_size
            lowercase								=      layer_norm_eps
            lowercase								=      position_embedding_type
            lowercase								=      block_per_row
            lowercase								=      approx_mode
            lowercase								=      initial_prior_first_n_blocks
            lowercase								=      initial_prior_diagonal_n_blocks



