

from __future__ import annotations

import random

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							tuple[str, float]:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							tuple[str, str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			random.randint(0		,							len(_UpperCamelCase	) - 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    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  list[str]	)		->							str:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  tuple[str, float]		,							_UpperCamelCase		:  list[tuple[str, float]]		,							_UpperCamelCase		:  list[str]		,							)		->							list[str]:
 '''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(_UpperCamelCase	):
  SCREAMING_SNAKE_CASE								=			population_score[random.randint(0		,							_UpperCamelCase	)][0]

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  list[str]		,							_UpperCamelCase		:  bool = True	)		->							tuple[int, int, str]:
 '''simple docstring'''


 if N_POPULATION < N_SELECTED:
  SCREAMING_SNAKE_CASE								=			f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
  raise ValueError(_UpperCamelCase	)
 # 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(_UpperCamelCase	)

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

 # 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(_UpperCamelCase	)

  # 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(_UpperCamelCase		,							_UpperCamelCase	) for item in population]

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

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


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




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

