
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE						       =						datasets.utils.logging.get_logger(__name__)





@dataclass
class 							lowerCAmelCase_						(    datasets.BuilderConfig							):




			'''simple docstring'''


			_lowercase      =							10_000
			_lowercase      =							None
			_lowercase      =							None





class 							lowerCAmelCase_						(    datasets.ArrowBasedBuilder							):




			'''simple docstring'''


			_lowercase      =							ParquetConfig







			def      __lowerCamelCase    (	self  ):
										return datasets.DatasetInfo(features=self.config.features  )







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):
										if not self.config.data_files:
																	raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""  )
										SCREAMING_SNAKE_CASE_	:							List[str]											=dl_manager.download_and_extract(self.config.data_files  )
										if isinstance(UpperCAmelCase__    ,							(str, list, tuple)  ):
																	SCREAMING_SNAKE_CASE_	:							Dict											=data_files
																	if isinstance(UpperCAmelCase__    ,							UpperCAmelCase__  ):
																								SCREAMING_SNAKE_CASE_	:							Tuple											=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	SCREAMING_SNAKE_CASE_	:							Optional[int]											=[dl_manager.iter_files(UpperCAmelCase__  ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,							gen_kwargs={'files': files}  )]
										SCREAMING_SNAKE_CASE_	:							int											=[]
										for split_name, files in data_files.items():
																	if isinstance(UpperCAmelCase__    ,							UpperCAmelCase__  ):
																								SCREAMING_SNAKE_CASE_	:							List[Any]											=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	SCREAMING_SNAKE_CASE_	:							Optional[int]											=[dl_manager.iter_files(UpperCAmelCase__  ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(UpperCAmelCase__  ):
																															with open(UpperCAmelCase__    ,							'rb'  ) as f:
																																						SCREAMING_SNAKE_CASE_	:							Any											=datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase__  )  )
																															break
																	splits.append(datasets.SplitGenerator(name=UpperCAmelCase__    ,							gen_kwargs={'files': files}  )  )
										return splits







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	SCREAMING_SNAKE_CASE_	:							Dict											=table_cast(UpperCAmelCase__    ,							self.info.features.arrow_schema  )
										return pa_table







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'"""  )
										for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__  )  ):
																	with open(UpperCAmelCase__    ,							'rb'  ) as f:
																								SCREAMING_SNAKE_CASE_	:							Dict											=pq.ParquetFile(UpperCAmelCase__  )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size    ,							columns=self.config.columns  )  ):
																																						SCREAMING_SNAKE_CASE_	:							int											=pa.Table.from_batches([record_batch]  )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase__  )
																								except ValueError as e:
																															logger.error(F"""Failed to read file \'{file}\' with error {type(UpperCAmelCase__  )}: {e}"""  )
																															raise








'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase_    						=       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_    						=       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_    						=       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, float]:
						lowercase :							int    		=len([g for position, g in enumerate(__magic_name__						) if g == main_target[position]]						)
						return (item, float(__magic_name__						))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, str]:
						lowercase :							Any    		=random.randint(0   , len(__magic_name__						) - 1						)
						lowercase :							Tuple    		=parent_a[:random_slice] + parent_a[random_slice:]
						lowercase :							List[str]    		=parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]						)				->  str:
						lowercase :							Union[str, Any]    		=list(__magic_name__						)
						if random.uniform(0   , 1						) < MUTATION_PROBABILITY:
												lowercase :							Dict    		=random.choice(__magic_name__						)
						return "".join(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     tuple[str, float]   , __magic_name__						:     list[tuple[str, float]]   , __magic_name__						:     list[str]   , )				->  list[str]:
						lowercase :							Any    		=[]
						# Generate more children proportionally to the fitness score.
						lowercase :							Dict    		=int(parent_a[1] * 100						) + 1
						lowercase :							List[str]    		=10 if child_n >= 10 else child_n
						for _ in range(__magic_name__						):
												lowercase :							List[str]    		=population_score[random.randint(0   , __magic_name__						)][0]

												lowercase		,	lowercase :							Dict    		=crossover(parent_a[0]   , __magic_name__						)
												# Append new string to the population list.
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
						return pop






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]   , __magic_name__						:     bool = True						)				->  tuple[int, int, str]:

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												lowercase :							List[str]    		=f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
												raise ValueError(__magic_name__						)
						# Verify that the target contains no genes besides the ones inside genes variable.
						lowercase :							Optional[int]    		=sorted({c for c in target if c not in genes}						)
						if not_in_genes_list:
												lowercase :							Dict    		=f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
												raise ValueError(__magic_name__						)

						# Generate random starting population.
						lowercase :							int    		=[]
						for _ in range(__magic_name__						):
												population.append(''''''.join([random.choice(__magic_name__						) for i in range(len(__magic_name__						)						)]						)						)

						# Just some logs to know what the algorithms is doing.
						lowercase		,	lowercase :							Optional[int]    		=0, 0

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

												# 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.
												lowercase :							List[str]    		=[evaluate(__magic_name__   , __magic_name__						) for item in population]

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

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


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





