



from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching
def  UpperCamelCase						(       )->       Any:





      """simple docstring"""





      import os as original_os
      from os import path as original_path
      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join

      A__							=       "__test_patch_submodule_mock__"
      with patch_submodule(_test_patching			,							"os.path.join"			,							_A    ):
            # Every way to access os.path.join must be patched, and the rest must stay untouched

            # check os.path.join
            assert isinstance(_test_patching.os			,							_PatchedModuleObj    )
            assert isinstance(_test_patching.os.path			,							_PatchedModuleObj    )
            assert _test_patching.os.path.join is mock

            # check path.join
            assert isinstance(_test_patching.path			,							_PatchedModuleObj    )
            assert _test_patching.path.join is mock

            # check join
            assert _test_patching.join is mock

            # check that the other attributes are untouched
            assert _test_patching.os.rename is original_rename
            assert _test_patching.path.dirname is original_dirname
            assert _test_patching.os.path.dirname is original_dirname

            # Even renamed modules or objects must be patched

            # check renamed_os.path.join
            assert isinstance(_test_patching.renamed_os			,							_PatchedModuleObj    )
            assert isinstance(_test_patching.renamed_os.path			,							_PatchedModuleObj    )
            assert _test_patching.renamed_os.path.join is mock

            # check renamed_path.join
            assert isinstance(_test_patching.renamed_path			,							_PatchedModuleObj    )
            assert _test_patching.renamed_path.join is mock

            # check renamed_join
            assert _test_patching.renamed_join is mock

            # check that the other attributes are untouched
            assert _test_patching.renamed_os.rename is original_rename
            assert _test_patching.renamed_path.dirname is original_dirname
            assert _test_patching.renamed_os.path.dirname is original_dirname

      # check that everthing is back to normal when the patch is over

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join
def  UpperCamelCase						(       )->       Optional[int]:





      """simple docstring"""





      assert _test_patching.open is open

      A__							=       "__test_patch_submodule_builtin_mock__"
      # _test_patching has "open" in its globals
      assert _test_patching.open is open
      with patch_submodule(_test_patching			,							"open"			,							_A    ):
            assert _test_patching.open is mock

      # check that everthing is back to normal when the patch is over

      assert _test_patching.open is open
def  UpperCamelCase						(       )->       Tuple:





      """simple docstring"""





      A__							=       "__test_patch_submodule_missing_mock__"
      with patch_submodule(_test_patching			,							"pandas.read_csv"			,							_A    ):
            pass
def  UpperCamelCase						(       )->       Any:





      """simple docstring"""





      A__							=       "__test_patch_submodule_missing_builtin_mock__"
      # _test_patching doesn't have "len" in its globals
      assert getattr(_test_patching			,							"len"			,							_A    ) is None
      with patch_submodule(_test_patching			,							"len"			,							_A    ):
            assert _test_patching.len is mock
      assert _test_patching.len is len
def  UpperCamelCase						(       )->       Any:





      """simple docstring"""





      A__							=       "__test_patch_submodule_start_and_stop_mock__"
      A__							=       patch_submodule(_test_patching			,							"open"			,							_A    )
      assert _test_patching.open is open
      patch.start()
      assert _test_patching.open is mock
      patch.stop()
      assert _test_patching.open is open
def  UpperCamelCase						(       )->       Tuple:





      """simple docstring"""





      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      A__							=       "__test_patch_submodule_successive_join__"
      A__							=       "__test_patch_submodule_successive_dirname__"
      A__							=       "__test_patch_submodule_successive_rename__"
      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename

      with patch_submodule(_test_patching			,							"os.path.join"			,							_A    ):
            with patch_submodule(_test_patching			,							"os.rename"			,							_A    ):
                  with patch_submodule(_test_patching			,							"os.path.dirname"			,							_A    ):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

    # try another order
      with patch_submodule(_test_patching			,							"os.rename"			,							_A    ):
            with patch_submodule(_test_patching			,							"os.path.join"			,							_A    ):
                  with patch_submodule(_test_patching			,							"os.path.dirname"			,							_A    ):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename
def  UpperCamelCase						(       )->       Optional[int]:





      """simple docstring"""





      A__							=       "__test_patch_submodule_doesnt_exist_mock__"
      with patch_submodule(_test_patching			,							"__module_that_doesn_exist__.__attribute_that_doesn_exist__"			,							_A    ):
            pass
      with patch_submodule(_test_patching			,							"os.__attribute_that_doesn_exist__"			,							_A    ):
            pass










from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase_							:      Tuple      =  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.
UpperCAmelCase_							:      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.
UpperCAmelCase_							:      Union[str, Any]      =  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       tuple[str, float]:





						"""simple docstring"""





						A__							=       len([g for position, g in enumerate(_A    ) if g == main_target[position]]    )
						return (item, float(_A    ))
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       tuple[str, str]:





						"""simple docstring"""





						A__							=       random.randint(0			,							len(_A    ) - 1    )
						A__							=       parent_a[:random_slice] + parent_a[random_slice:]
						A__							=       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)
def  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]    )->       str:





						"""simple docstring"""





						A__							=       list(_A    )
						if random.uniform(0			,							1    ) < MUTATION_PROBABILITY:
												A__							=       random.choice(_A    )
						return "".join(_A    )
def  UpperCamelCase						(       _A			:				tuple[str, float]			,							_A			:				list[tuple[str, float]]			,							_A			:				list[str]			,							)->       list[str]:





						"""simple docstring"""





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

												A__						,       A__							=       crossover(parent_a[0]			,							_A    )
												# Append new string to the population list.
												pop.append(mutate(_A			,							_A    )    )
												pop.append(mutate(_A			,							_A    )    )
						return pop
def  UpperCamelCase						(       _A			:				str			,							_A			:				list[str]			,							_A			:				bool = True    )->       tuple[int, int, str]:





						"""simple docstring"""





						if N_POPULATION < N_SELECTED:
												A__							=       f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
												raise ValueError(_A    )
						# Verify that the target contains no genes besides the ones inside genes variable.
						A__							=       sorted({c for c in target if c not in genes}    )
						if not_in_genes_list:
												A__							=       f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_A    )

						# Generate random starting population.
						A__							=       []
						for _ in range(_A    ):
												population.append("".join([random.choice(_A    ) for i in range(len(_A    )    )]    )    )

						# Just some logs to know what the algorithms is doing.
						A__						,       A__							=       0, 0

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

												# 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.
												A__							=       [evaluate(_A			,							_A    ) for item in population]

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

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


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





