

"""simple docstring"""





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__UpperCamelCase  :      Optional[Any]							      =		'''scheduler_config.json'''







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       1
					lowercase__				 =       2
					lowercase__				 =       3
					lowercase__				 =       4
					lowercase__				 =       5







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       SCHEDULER_CONFIG_NAME
					lowercase__				 =       ["dtype"]
					lowercase__				 =       []
					lowercase__				 =       True







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]   ,lowercase_			:				Dict[str, Any] = None   ,lowercase_			:				Optional[str] = None   ,lowercase_			:				Optional[int]=False   ,**lowercase_			:				Any   ,):
						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	cls.load_config(
						    pretrained_model_name_or_path=lowercase_   ,subfolder=lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_   ,)
						lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	cls.from_config(lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_		)

						if hasattr(lowercase_   ,'''create_state'''		) and getattr(lowercase_   ,'''has_state'''   ,lowercase_		):
							lowerCAmelCase__	:						List[Any]  				=	scheduler.create_state()

						if return_unused_kwargs:
							return scheduler, state, unused_kwargs

						return scheduler, state







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union[str, os.PathLike]   ,lowercase_			:				bool = False   ,**lowercase_			:				str		):
						self.save_config(save_directory=lowercase_   ,push_to_hub=lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return self._get_compatibles()







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	list(set([cls.__name__] + cls._compatibles		)		)
						lowerCAmelCase__	:						Tuple  				=	importlib.import_module(__name__.split('''.'''		)[0]		)
						lowerCAmelCase__	:						Union[str, Any]  				=	[
						    getattr(lowercase_   ,lowercase_		) for c in compatible_classes_str if hasattr(lowercase_   ,lowercase_		)
						]
						return compatible_classes

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	assert len(A_ ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_ ) - x.ndim) )						,     A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=0.999						,     A_=jnp.floataa ):

	def alpha_bar(A_ ):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

	lowerCAmelCase__	:						Optional[Any]  				=	[]
	for i in range(A_ ):
		lowerCAmelCase__	:						str  				=	i / num_diffusion_timesteps
		lowerCAmelCase__	:						Union[str, Any]  				=	(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(A_ ) / alpha_bar(A_ )						,     A_ ) )
	return jnp.array(A_						,     dtype=A_ )







@flax.struct.dataclass
class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       42
					lowercase__				 =       42







					@classmethod
					def        __lowerCAmelCase (       cls			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	scheduler.config

						if config.trained_betas is not None:
							lowerCAmelCase__	:						Any  				=	jnp.asarray(config.trained_betas   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "linear":
							lowerCAmelCase__	:						Union[str, Any]  				=	jnp.linspace(config.beta_start   ,config.beta_end   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase__	:						int  				=	(
							    jnp.linspace(
							        config.beta_start**0.5   ,config.beta_end**0.5   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
							    ** 2
							)
						elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase__	:						List[Any]  				=	betas_for_alpha_bar(config.num_train_timesteps   ,dtype=scheduler.dtype		)
						else:
							raise NotImplementedError(
							    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'		)

						lowerCAmelCase__	:						str  				=	1.0 - betas

						lowerCAmelCase__	:						Union[str, Any]  				=	jnp.cumprod(lowercase_   ,axis=0		)

						return cls(
						    alphas=lowercase_   ,betas=lowercase_   ,alphas_cumprod=lowercase_   ,)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	state.alphas_cumprod

	lowerCAmelCase__	:						Optional[Any]  				=	alphas_cumprod[timesteps] ** 0.5
	lowerCAmelCase__	:						Tuple  				=	sqrt_alpha_prod.flatten()
	lowerCAmelCase__	:						str  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	lowerCAmelCase__	:						Optional[Any]  				=	(1 - alphas_cumprod[timesteps]) ** 0.5
	lowerCAmelCase__	:						Optional[Any]  				=	sqrt_one_minus_alpha_prod.flatten()
	lowerCAmelCase__	:						Optional[int]  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						str  				=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity






'''simple docstring'''







from __future__ import annotations

import random

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, float]:
	_snake_case   						=		len([g for position, g in enumerate(__A	) if g == main_target[position]]	)
	return (item, float(__A	))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, str]:
	_snake_case   						=		random.randint(0			,				len(__A	) - 1	)
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







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

		_snake_case						, _snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		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.
	_snake_case   						=		sorted({c for c in target if c not in genes}	)
	if not_in_genes_list:
		_snake_case   						=		F'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(__A	)

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

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

