


'''simple docstring'''

from __future__ import annotations

import random

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





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   int					     =					len([g for position, g in enumerate(lowerCAmelCase		) if g == main_target[position]]		)
	return (item, float(lowerCAmelCase		))





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					random.randint(0		,		len(lowerCAmelCase		) - 1		)
	__magic_name__	:   List[str]					     =					parent_a[:random_slice] + parent_a[random_slice:]
	__magic_name__	:   Optional[Any]					     =					parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     list[str]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					list(lowerCAmelCase		)
	if random.uniform(0		,		1		) < MUTATION_PROBABILITY:
		__magic_name__	:   str					     =					random.choice(lowerCAmelCase		)
	return "".join(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     tuple[str, float]		,		lowerCAmelCase    :     list[tuple[str, float]]		,		lowerCAmelCase    :     list[str]		,		):





	"""simple docstring"""







	__magic_name__	:   str					     =					[]
	# Generate more children proportionally to the fitness score.
	__magic_name__	:   Optional[Any]					     =					int(parent_a[1] * 100		) + 1
	__magic_name__	:   Any					     =					10 if child_n >= 10 else child_n
	for _ in range(lowerCAmelCase		):
		__magic_name__	:   Dict					     =					population_score[random.randint(0		,		lowerCAmelCase		)][0]

		__magic_name__	:   Optional[Any]					     =					crossover(parent_a[0]		,		lowerCAmelCase		)
		# Append new string to the population list.
		pop.append(mutate(lowerCAmelCase		,		lowerCAmelCase		)		)
		pop.append(mutate(lowerCAmelCase		,		lowerCAmelCase		)		)
	return pop





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     list[str]		,		lowerCAmelCase    :     bool = True		):





	"""simple docstring"""







	if N_POPULATION < N_SELECTED:
		__magic_name__	:   List[Any]					     =					f'{N_POPULATION} must be bigger than {N_SELECTED}'
		raise ValueError(lowerCAmelCase		)
	# Verify that the target contains no genes besides the ones inside genes variable.
	__magic_name__	:   str					     =					sorted({c for c in target if c not in genes}		)
	if not_in_genes_list:
		__magic_name__	:   Optional[int]					     =					f'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(lowerCAmelCase		)

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

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

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

		# 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.
		__magic_name__	:   Optional[int]					     =					[evaluate(lowerCAmelCase		,		lowerCAmelCase		) for item in population]

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

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


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



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase				:str        =      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:List[str]        =      ['''VisionEncoderDecoderModel''']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:List[str]        =      ['''TFVisionEncoderDecoderModel''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:Optional[Any]        =      ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
							from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
							import sys

							lowerCAmelCase				:Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)