



'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_A				:    List[Any]     						=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				:    Optional[Any]     						=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				:    Any     						=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			tuple[str, float]:
	_lowercase					:       List[Any]       =		len([g for position, g in enumerate(_lowercase					) if g == main_target[position]]					)
	return (item, float(_lowercase					))







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			tuple[str, str]:
	_lowercase					:       List[str]       =		random.randint(0,				len(_lowercase					) - 1					)
	_lowercase					:       Optional[int]       =		parent_a[:random_slice] + parent_a[random_slice:]
	_lowercase					:       str       =		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       int       =		list(_lowercase					)
	if random.uniform(0,				1					) < MUTATION_PROBABILITY:
		_lowercase					:       List[str]       =		random.choice(_lowercase					)
	return "".join(_lowercase					)







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

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







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase = True					) ->			tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_lowercase					:       Optional[Any]       =		f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
		raise ValueError(_lowercase					)
	# 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					:       List[Any]       =		f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
		raise ValueError(_lowercase					)

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

	# Just some logs to know what the algorithms is doing.
	_lowercase  ,    _lowercase					:       str       =		0, 0

	# This loop will end when we find a perfect match for our target.
	while True:
		generation += 1
		total_population += len(_lowercase					)

		# 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					:       List[Any]       =		[evaluate(_lowercase,				_lowercase					) for item in population]

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

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


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





'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_A				:    Optional[int]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  **UpperCamelCase_				:	List[str]					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
