"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase_     =	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.
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, 1000))



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    tuple[str, float]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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  )->    tuple[str, str]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    random.randint(0     ,	len(__SCREAMING_SNAKE_CASE  ) - 1  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    parent_a[:random_slice] + parent_a[random_slice:]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						Any			  =    list(__SCREAMING_SNAKE_CASE  )
							if random.uniform(0     ,	1  ) < MUTATION_PROBABILITY:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    random.choice(__SCREAMING_SNAKE_CASE  )
							return "".join(__SCREAMING_SNAKE_CASE  )



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

														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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  )->    tuple[int, int, str]:

							# Verify if N_POPULATION is bigger than N_SELECTED
							if N_POPULATION < N_SELECTED:
														_SCREAMING_SNAKE_CASE		:						int			  =    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		:						List[str]			  =    sorted({c for c in target if c not in genes}  )
							if not_in_genes_list:
														_SCREAMING_SNAKE_CASE		:						int			  =    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		:						List[Any]			  =    []
							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		:						Union[str, Any]			  =    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		:						Union[str, Any]			  =    [evaluate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for item in population]

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







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"ChineseCLIPImageProcessor"
				a			=						("BertTokenizer", "BertTokenizerFast")





				def __init__(      self							:							Dict      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    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		:						str			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						int			  =    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)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor





				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[Any]=None      ,    _A							:							Any=None      ,    _A							:							Tuple=None      ,    **_A							:							int):



											"""simple docstring"""







											if text is None and images is None:
																		raise ValueError("""You have to specify either text or images. Both cannot be none.""")

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(_A      ,    return_tensors=_A      ,    **_A)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor(_A      ,    return_tensors=_A      ,    **_A)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_A)      ,    tensor_type=_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    *_A							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.batch_decode(*_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							List[Any]      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.decode(*_A      ,    **_A)





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





				@property
				def 		_lowerCAmelCase		(      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


