

"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a    :Optional[Any]			=       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    :Any			=       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    :int			=       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))




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




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase							)				-> Tuple:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		random.randint(0     ,    len(_UpperCamelCase							) - 1							)
      SCREAMING_SNAKE_CASE__      :					Tuple											=		parent_a[:random_slice] + parent_a[random_slice:]
      SCREAMING_SNAKE_CASE__      :					Optional[int]											=		parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase							)				-> Optional[Any]:
      SCREAMING_SNAKE_CASE__      :					Dict											=		list(_UpperCamelCase							)
      if random.uniform(0     ,    1							) < MUTATION_PROBABILITY:
            SCREAMING_SNAKE_CASE__      :					Any											=		random.choice(_UpperCamelCase							)
      return "".join(_UpperCamelCase							)




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

            SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		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        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase = True							)				-> Tuple:




      if N_POPULATION < N_SELECTED:
            SCREAMING_SNAKE_CASE__      :					Dict											=		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__      :					str											=		sorted({c for c in target if c not in genes}							)
      if not_in_genes_list:
            SCREAMING_SNAKE_CASE__      :					Any											=		F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
            raise ValueError(_UpperCamelCase							)

      # Generate random starting population.
      SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[]
      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__      :					Any											=		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__      :					Optional[Any]											=		[evaluate(_UpperCamelCase     ,    _UpperCamelCase							) for item in population]

            # Check if there is a matching evolution.
            SCREAMING_SNAKE_CASE__      :					List[Any]											=		sorted(_UpperCamelCase     ,    key=lambda __lowerCAmelCase							: 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__      :					Any											=		population[: int(N_POPULATION / 3							)]
            population.clear()
            population.extend(_UpperCamelCase							)
            # Normalize population score to be between 0 and 1.
            SCREAMING_SNAKE_CASE__      :					Tuple											=		[
                (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    :Tuple			=       list(
      " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
      "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
  )
  a    :List[Any]			=       basic(target_str, genes_list)
  print(
      f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
  )








"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=None    ,   __a=True    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			config_class
										__lowerCAmelCase				    =			has_text_modality
										__lowerCAmelCase				    =			kwargs
										__lowerCAmelCase				    =			common_properties

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
													common_properties.extend(["vocab_size"]	)

										# Test that config has the common properties as getters
										for prop in common_properties:
													self.parent.assertTrue(hasattr(__a    ,   __a	)    ,   msg=f"`{prop}` does not exist"	)

										# Test that config has the common properties as setter
										for idx, name in enumerate(__a	):
													try:
																setattr(__a    ,   __a    ,   __a	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(__a	):
													try:
																__lowerCAmelCase				    =			self.config_class(**{name: idx}	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			json.loads(config.to_json_string()	)
										for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   "config.json"	)
													config_first.to_json_file(__a	)
													__lowerCAmelCase				    =			self.config_class.from_json_file(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										__lowerCAmelCase				    =			"test"
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a    ,   subfolder=__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict    ,   num_labels=5	)
										self.parent.assertEqual(len(config.idalabel	)    ,   5	)
										self.parent.assertEqual(len(config.labelaid	)    ,   5	)

										__lowerCAmelCase				    =			3
										self.parent.assertEqual(len(config.idalabel	)    ,   3	)
										self.parent.assertEqual(len(config.labelaid	)    ,   3	)

							def 			snake_case				(     self	):
										if self.config_class.is_composition:
													return
										__lowerCAmelCase				    =			self.config_class()
										self.parent.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			copy.deepcopy(__a	)
										__lowerCAmelCase				    =			self.config_class(**__a	)
										__lowerCAmelCase				    =			[]
										for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																if not is_torch_available():
																			continue
																else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)	)
													elif getattr(__a    ,   __a	) != value:
																wrong_values.append((key, getattr(__a    ,   __a	), value)	)

										if len(__a	) > 0:
													__lowerCAmelCase				    =			"\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]	)
													raise ValueError(f"The following keys were not properly set in the config:\n{errors}"	)



							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()




