




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__A						:							Optional[int]							=  2_0_0
# 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.
__A						:							Dict							=  5_0
# 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.
__A						:							Tuple							=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					len([g for position, g in enumerate(SCREAMING_SNAKE_CASE   ) if g == main_target[position]]   )
			return (item, float(SCREAMING_SNAKE_CASE   ))
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					random.randint(0					,	len(SCREAMING_SNAKE_CASE   ) - 1   )
			SCREAMING_SNAKE_CASE           =					parent_a[:random_slice] + parent_a[random_slice:]
			SCREAMING_SNAKE_CASE           =					parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					list(SCREAMING_SNAKE_CASE   )
			if random.uniform(0					,	1   ) < MUTATION_PROBABILITY:
						SCREAMING_SNAKE_CASE           =					random.choice(SCREAMING_SNAKE_CASE   )
			return "".join(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			# Generate more children proportionally to the fitness score.
			SCREAMING_SNAKE_CASE           =					int(parent_a[1] * 1_00   ) + 1
			SCREAMING_SNAKE_CASE           =					10 if child_n >= 10 else child_n
			for _ in range(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					population_score[random.randint(0					,	SCREAMING_SNAKE_CASE   )][0]

						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = True   ):
			'''simple docstring'''

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

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

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












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""			)
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""			)

										super().__init__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,     a       ,     )
										return self.image_processor_class







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






