



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


UpperCAmelCase_							:      int      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Optional[int]      =  {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}






class      UpperCamelCase    (						snake_case__     ):
 lowerCAmelCase	:   Tuple				=	"""donut-swin"""

 lowerCAmelCase	:   Optional[int]				=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }



 def __init__(       self			,	UpperCAmelCase__=224			,	UpperCAmelCase__=4			,	UpperCAmelCase__=3			,	UpperCAmelCase__=96			,	UpperCAmelCase__=[2, 2, 6, 2]			,	UpperCAmelCase__=[3, 6, 12, 24]			,	UpperCAmelCase__=7			,	UpperCAmelCase__=4.0			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=False			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	**UpperCAmelCase__			,	):
       super().__init__(**_A		)

       A__							=       image_size
       A__							=       patch_size
       A__							=       num_channels
       A__							=       embed_dim
       A__							=       depths
       A__							=       len(_A		)
       A__							=       num_heads
       A__							=       window_size
       A__							=       mlp_ratio
       A__							=       qkv_bias
       A__							=       hidden_dropout_prob
       A__							=       attention_probs_dropout_prob
       A__							=       drop_path_rate
       A__							=       hidden_act
       A__							=       use_absolute_embeddings
       A__							=       layer_norm_eps
       A__							=       initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       A__							=       int(embed_dim * 2 ** (len(_A		) - 1)		)










from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase_							:      Tuple      =  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.
UpperCAmelCase_							:      Any      =  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.
UpperCAmelCase_							:      Union[str, Any]      =  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       tuple[str, float]:





						"""simple docstring"""





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





						"""simple docstring"""





						A__							=       random.randint(0			,							len(_A    ) - 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  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]    )->       str:





						"""simple docstring"""





						A__							=       list(_A    )
						if random.uniform(0			,							1    ) < MUTATION_PROBABILITY:
												A__							=       random.choice(_A    )
						return "".join(_A    )
def  UpperCamelCase						(       _A			:				tuple[str, float]			,							_A			:				list[tuple[str, float]]			,							_A			:				list[str]			,							)->       list[str]:





						"""simple docstring"""





						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(_A    ):
												A__							=       population_score[random.randint(0			,							_A    )][0]

												A__						,       A__							=       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  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]			,							_A			:				bool = True    )->       tuple[int, int, str]:





						"""simple docstring"""





						if N_POPULATION < N_SELECTED:
												A__							=       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.
						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(_A    )

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

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





