

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_    :						List[str]    		=      basic(target_str, genes_list)
						print(
						    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
						)




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,			_KWARGS_DESCRIPTION   )
class      UpperCamelCase (     datasets.Metric   ):

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
             {
                 'predictions': datasets.Value('string'							,    id='sequence'    ),
                 'references': datasets.Value('string'							,    id='sequence'    ),
             }    )							,    codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

