'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_:	Union[str, Any]    =2_00
# 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.
SCREAMING_SNAKE_CASE_:	Optional[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.
SCREAMING_SNAKE_CASE_:	Optional[int]    =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]   ,       snake_case_			:					int     )       -> tuple[str, float]:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					str     )       -> tuple[str, str]:


     '''simple docstring'''




     UpperCAmelCase_			       =  random.randint(0   ,       len(__UpperCAmelCase     ) - 1     )
     UpperCAmelCase_			       =  parent_a[:random_slice] + parent_a[random_slice:]
     UpperCAmelCase_			       =  parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					Union[str, Any]     )       -> str:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					Any   ,       snake_case_			:					List[str]   ,       )       -> list[str]:


     '''simple docstring'''




     UpperCAmelCase_			       =  []
     # Generate more children proportionally to the fitness score.
     UpperCAmelCase_			       =  int(parent_a[1] * 1_00     ) + 1
     UpperCAmelCase_			       =  10 if child_n >= 10 else child_n
     for _ in range(__UpperCAmelCase     ):
          UpperCAmelCase_			       =  population_score[random.randint(0   ,       __UpperCAmelCase     )][0]

          UpperCAmelCase_			       =  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_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					int   ,       snake_case_			:					Union[str, Any] = True     )       -> tuple[int, int, str]:


     '''simple docstring'''




     if N_POPULATION < N_SELECTED:
          UpperCAmelCase_			       =  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.
     UpperCAmelCase_			       =  sorted({c for c in target if c not in genes}     )
     if not_in_genes_list:
          UpperCAmelCase_			       =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
          raise ValueError(__UpperCAmelCase     )

     # Generate random starting population.
     UpperCAmelCase_			       =  []
     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.
     UpperCAmelCase_			       =  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.
          UpperCAmelCase_			       =  [evaluate(__UpperCAmelCase   ,       __UpperCAmelCase     ) for item in population]

          # Check if there is a matching evolution.
          UpperCAmelCase_			       =  sorted(__UpperCAmelCase   ,       key=lambda snake_case_     : 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.
          UpperCAmelCase_			       =  population[: int(N_POPULATION / 3     )]
          population.clear()
          population.extend(__UpperCAmelCase     )
          # Normalize population score to be between 0 and 1.
          UpperCAmelCase_			       =  [
              (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__":
    SCREAMING_SNAKE_CASE_:	Dict    =(
        'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
    )
    SCREAMING_SNAKE_CASE_:	List[Any]    =list(
        ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
        'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
    )
    SCREAMING_SNAKE_CASE_,							SCREAMING_SNAKE_CASE_,							SCREAMING_SNAKE_CASE_:	Union[str, Any]    =basic(target_str, genes_list)
    print(
        f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
    )









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=self.get_successors(a_			)

													for child_node in successors:
																if child_node in self.closed_nodes:
																			continue

																if child_node not in self.open_nodes:
																			self.open_nodes.append(a_			)
																else:
																			# retrieve the best current path
																			snake_case:    List[Any]      							=self.open_nodes.pop(self.open_nodes.index(a_			)			)

																			if child_node.g_cost < better_node.g_cost:
																						self.open_nodes.append(a_			)
																			else:
																						self.open_nodes.append(a_			)

										if not self.reached:
													return [self.start.pos]
										return None


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=parent.pos_y + action[0]

													if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(a_			) - 1):
																continue

													if grid[pos_y][pos_x] != 0:
																continue

													successors.append(
													    Node(
													        a_      ,						a_      ,						self.target.pos_y      ,						self.target.pos_x      ,						parent.g_cost + 1      ,						a_      ,						)			)
										return successors




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	a							   =				(0, 0)
	a							   =				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




