
"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_a								=						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.
_a								=						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.
_a								=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						tuple[str, float]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						tuple[str, str]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	)				->						list[str]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					# Generate more children proportionally to the fitness score.
					_UpperCamelCase									=    int(parent_a[1] * 1_00      ) + 1
					_UpperCamelCase									=    10 if child_n >= 10 else child_n
					for _ in range(__snake_case      ):
										_UpperCamelCase									=    population_score[random.randint(0,	__snake_case      )][0]

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case = True      )				->						tuple[int, int, str]:


					"""simple docstring"""





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

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

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

					# This loop will end when we find a perfect match for our target.
					while True:
										generation += 1
										total_population += len(__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.
										_UpperCamelCase									=    [evaluate(__snake_case,	__snake_case      ) for item in population]

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

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


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








"""simple docstring"""





import warnings

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






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    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							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    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							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''simple docstring'''
							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:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

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



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							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)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor






