
"""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_					     =	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.
SCREAMING_SNAKE_CASE_					     =	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_					     =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  len([g for position, g in enumerate(lowerCAmelCase__						) if g == main_target[position]]						)
						return (item, float(lowerCAmelCase__						))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  random.randint(0 ,    len(lowerCAmelCase__						) - 1						)
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  list(lowerCAmelCase__						)
						if random.uniform(0 ,    1						) < MUTATION_PROBABILITY:
												__lowerCAmelCase     =  random.choice(lowerCAmelCase__						)
						return "".join(lowerCAmelCase__						)

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

												__lowerCAmelCase   ,     __lowerCAmelCase     =  crossover(parent_a[0] ,    lowerCAmelCase__						)
												# Append new string to the population list.
												pop.append(mutate(lowerCAmelCase__ ,    lowerCAmelCase__						)						)
												pop.append(mutate(lowerCAmelCase__ ,    lowerCAmelCase__						)						)
						return pop

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True						):






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

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

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

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

												# 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.
												__lowerCAmelCase     =  [evaluate(lowerCAmelCase__ ,    lowerCAmelCase__						) for item in population]

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

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


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





import math
import sys






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    ''
							try:
														with open(lowerCAmelCase__			,	'rb'				) as binary_file:
																					A      						=    binary_file.read()
														for dat in data:
																					A      						=    F'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    {'0': '0', '1': '1'}
							A						,   A      						=    '', ''
							A      						=    len(lowerCAmelCase__				)

							for i in range(len(lowerCAmelCase__				)				):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														A      						=    lexicon[curr_string]
														result += last_match_id
														A      						=    last_match_id + '0'

														if math.loga(lowerCAmelCase__				).is_integer():
																					A      						=    {}
																					for curr_key in list(lowerCAmelCase__				):
																												A      						=    lexicon.pop(lowerCAmelCase__				)
																					A      						=    new_lex

														A      						=    last_match_id + '1'
														index += 1
														A      						=    ''
							return result






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    8
							try:
														with open(lowerCAmelCase__			,	'wb'				) as opened_file:
																					A      						=    [
																					    to_write[i : i + byte_length]
																					    for i in range(0			,	len(lowerCAmelCase__				)			,	lowerCAmelCase__				)
																					]

																					if len(result_byte_array[-1]				) % byte_length == 0:
																												result_byte_array.append('10000000'				)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]				) - 1
																												)

																					for elem in result_byte_array[:-1]:
																												opened_file.write(int(lowerCAmelCase__			,	2				).to_bytes(1			,	byteorder='big'				)				)
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A      						=    data_bits[counter:]
							A      						=    data_bits[counter + 1 :]
							return data_bits






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    read_file_binary(lowerCAmelCase__				)
							A      						=    remove_prefix(lowerCAmelCase__				)
							A      						=    decompress_data(lowerCAmelCase__				)
							write_file_binary(lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])