




from __future__ import annotations

import random

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




def 			__magic_name__				( A		:					str,  A		:					str   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					str,  A		:					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 			__magic_name__				( A		:					str,  A		:					list[str]   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					tuple[str, float],  A		:					list[tuple[str, float]],  A		:					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 			__magic_name__				( A		:					str,  A		:					list[str],  A		:					bool = True   ):






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












# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and column:
										if all(
										    isinstance(__lowerCamelCase					,      torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
										    for x in column							):
													return torch.stack(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

							if isinstance(__lowerCamelCase					,      (str, bytes, type(__lowerCamelCase							))							):
										return value
							elif isinstance(__lowerCamelCase					,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype					,      np.character							):
										return value.tolist()

							a    =			{}

							if isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.integer							):
										a    =			{"dtype": torch.intaa}
							elif isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.floating							):
										a    =			{"dtype": torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__lowerCamelCase					,      PIL.Image.Image							):
													a    =			np.asarray(__lowerCamelCase							)
							return torch.tensor(__lowerCamelCase					,      **{**default_dtype, **self.torch_tensor_kwargs}							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(__lowerCamelCase					,      "__array__"							) and not isinstance(__lowerCamelCase					,      torch.Tensor							):
										a    =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCamelCase					,      np.ndarray							):
										if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							elif isinstance(__lowerCamelCase					,      (list, tuple)							):
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							return self._tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






