





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_a 						=      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.
_a 						=      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.
_a 						=      0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[str, float]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[str, str]:







	'''simple docstring'''







	lowerCamelCase__								=		random.randint(0       ,len(__snake_case							) - 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 lowerCAmelCase__(__snake_case       ,__snake_case							)				->      str:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,)				->      list[str]:







	'''simple docstring'''







	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(__snake_case							):
		lowerCamelCase__								=		population_score[random.randint(0       ,__snake_case							)][0]

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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case = True							)				->      tuple[int, int, str]:







	'''simple docstring'''







	if N_POPULATION < N_SELECTED:
		lowerCamelCase__								=		F'{N_POPULATION} must be bigger than {N_SELECTED}'
		raise ValueError(__snake_case							)
	# 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(__snake_case							)

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

	# 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(__snake_case							)

		# 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(__snake_case       ,__snake_case							) for item in population]

		# Check if there is a matching evolution.
		lowerCamelCase__								=		sorted(__snake_case       ,key=lambda __snake_case							: x[1]       ,reverse=__snake_case							)
		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(__snake_case							)
		# Normalize population score to be between 0 and 1.
		lowerCamelCase__								=		[
		    (item, score / len(__snake_case							)) for item, score in population_score
		]

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


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










from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						42






class 					__A    (    nn.Module ):

			'''simple docstring'''







			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						(16, 32, 96, 256)
			lowerCAmelCase_							     =						jnp.floataa





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		nn.Conv(
				    self.block_out_channels[0]       ,  kernel_size=(3, 3)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )

				lowerCamelCase__								=		[]
				for i in range(len(self.block_out_channels							) - 1							):
					lowerCamelCase__								=		self.block_out_channels[i]
					lowerCamelCase__								=		self.block_out_channels[i + 1]
					lowerCamelCase__								=		nn.Conv(
					    __lowerCAmelCase       ,  kernel_size=(3, 3)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )
					blocks.append(__lowerCAmelCase							)
					lowerCamelCase__								=		nn.Conv(
					    __lowerCAmelCase       ,  kernel_size=(3, 3)       ,  strides=(2, 2)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )
					blocks.append(__lowerCAmelCase							)
				lowerCamelCase__								=		blocks

				lowerCamelCase__								=		nn.Conv(
				    self.conditioning_embedding_channels       ,  kernel_size=(3, 3)       ,  padding=((1, 1), (1, 1))       ,  kernel_init=nn.initializers.zeros_init()       ,  bias_init=nn.initializers.zeros_init()       ,  dtype=self.dtype       ,  )





			def __call__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.conv_in(__lowerCAmelCase							)
				lowerCamelCase__								=		nn.silu(__lowerCAmelCase							)

				for block in self.blocks:
					lowerCamelCase__								=		block(__lowerCAmelCase							)
					lowerCamelCase__								=		nn.silu(__lowerCAmelCase							)

				lowerCamelCase__								=		self.conv_out(__lowerCAmelCase							)

				return embedding






@flax_register_to_config
class 					__A    (    nn.Module			,				lowerCAmelCase			,				lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						32
			lowerCAmelCase_							     =						4
			lowerCAmelCase_							     =						(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						(320, 640, 1280, 1280)
			lowerCAmelCase_							     =						2
			lowerCAmelCase_							     =						8
			lowerCAmelCase_							     =						None
			lowerCAmelCase_							     =						1280
			lowerCAmelCase_							     =						0.0
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						jnp.floataa
			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						0
			lowerCAmelCase_							     =						"rgb"
			lowerCAmelCase_							     =						(16, 32, 96, 256)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		(1, self.in_channels, self.sample_size, self.sample_size)
				lowerCamelCase__								=		jnp.zeros(__lowerCAmelCase       ,  dtype=jnp.floataa							)
				lowerCamelCase__								=		jnp.ones((1,)       ,  dtype=jnp.intaa							)
				lowerCamelCase__								=		jnp.zeros((1, 1, self.cross_attention_dim)       ,  dtype=jnp.floataa							)
				lowerCamelCase__								=		(1, 3, self.sample_size * 8, self.sample_size * 8)
				lowerCamelCase__								=		jnp.zeros(__lowerCAmelCase       ,  dtype=jnp.floataa							)

				lowerCamelCase__				,	lowerCamelCase__								=		jax.random.split(__lowerCAmelCase							)
				lowerCamelCase__								=		{'''params''': params_rng, '''dropout''': dropout_rng}

				return self.init(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)["params"]





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.block_out_channels
				lowerCamelCase__								=		block_out_channels[0] * 4

				# If `num_attention_heads` is not defined (which is the case for most models)
				# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
				# The reason for this behavior is to correct for incorrectly named variables that were introduced
				# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
				# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
				# which is why we correct for the naming here.
				lowerCamelCase__								=		self.num_attention_heads or self.attention_head_dim

				# input
				lowerCamelCase__								=		nn.Conv(
				    block_out_channels[0]       ,  kernel_size=(3, 3)       ,  strides=(1, 1)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )

				# time
				lowerCamelCase__								=		FlaxTimesteps(
				    block_out_channels[0]       ,  flip_sin_to_cos=self.flip_sin_to_cos       ,  freq_shift=self.config.freq_shift							)
				lowerCamelCase__								=		FlaxTimestepEmbedding(__lowerCAmelCase       ,  dtype=self.dtype							)

				lowerCamelCase__								=		FlaxControlNetConditioningEmbedding(
				    conditioning_embedding_channels=block_out_channels[0]       ,  block_out_channels=self.conditioning_embedding_out_channels       ,  )

				lowerCamelCase__								=		self.only_cross_attention
				if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		(only_cross_attention,) * len(self.down_block_types							)

				if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		(num_attention_heads,) * len(self.down_block_types							)

				# down
				lowerCamelCase__								=		[]
				lowerCamelCase__								=		[]

				lowerCamelCase__								=		block_out_channels[0]

				lowerCamelCase__								=		nn.Conv(
				    __lowerCAmelCase       ,  kernel_size=(1, 1)       ,  padding='''VALID'''       ,  kernel_init=nn.initializers.zeros_init()       ,  bias_init=nn.initializers.zeros_init()       ,  dtype=self.dtype       ,  )
				controlnet_down_blocks.append(__lowerCAmelCase							)

				for i, down_block_type in enumerate(self.down_block_types							):
					lowerCamelCase__								=		output_channel
					lowerCamelCase__								=		block_out_channels[i]
					lowerCamelCase__								=		i == len(__lowerCAmelCase							) - 1

					if down_block_type == "CrossAttnDownBlock2D":
						lowerCamelCase__								=		FlaxCrossAttnDownBlockaD(
						    in_channels=__lowerCAmelCase       ,  out_channels=__lowerCAmelCase       ,  dropout=self.dropout       ,  num_layers=self.layers_per_block       ,  num_attention_heads=num_attention_heads[i]       ,  add_downsample=not is_final_block       ,  use_linear_projection=self.use_linear_projection       ,  only_cross_attention=only_cross_attention[i]       ,  dtype=self.dtype       ,  )
					else:
						lowerCamelCase__								=		FlaxDownBlockaD(
						    in_channels=__lowerCAmelCase       ,  out_channels=__lowerCAmelCase       ,  dropout=self.dropout       ,  num_layers=self.layers_per_block       ,  add_downsample=not is_final_block       ,  dtype=self.dtype       ,  )

					down_blocks.append(__lowerCAmelCase							)

					for _ in range(self.layers_per_block							):
						lowerCamelCase__								=		nn.Conv(
						    __lowerCAmelCase       ,  kernel_size=(1, 1)       ,  padding='''VALID'''       ,  kernel_init=nn.initializers.zeros_init()       ,  bias_init=nn.initializers.zeros_init()       ,  dtype=self.dtype       ,  )
						controlnet_down_blocks.append(__lowerCAmelCase							)

					if not is_final_block:
						lowerCamelCase__								=		nn.Conv(
						    __lowerCAmelCase       ,  kernel_size=(1, 1)       ,  padding='''VALID'''       ,  kernel_init=nn.initializers.zeros_init()       ,  bias_init=nn.initializers.zeros_init()       ,  dtype=self.dtype       ,  )
						controlnet_down_blocks.append(__lowerCAmelCase							)

				lowerCamelCase__								=		down_blocks
				lowerCamelCase__								=		controlnet_down_blocks

				# mid
				lowerCamelCase__								=		block_out_channels[-1]
				lowerCamelCase__								=		FlaxUNetMidBlockaDCrossAttn(
				    in_channels=__lowerCAmelCase       ,  dropout=self.dropout       ,  num_attention_heads=num_attention_heads[-1]       ,  use_linear_projection=self.use_linear_projection       ,  dtype=self.dtype       ,  )

				lowerCamelCase__								=		nn.Conv(
				    __lowerCAmelCase       ,  kernel_size=(1, 1)       ,  padding='''VALID'''       ,  kernel_init=nn.initializers.zeros_init()       ,  bias_init=nn.initializers.zeros_init()       ,  dtype=self.dtype       ,  )





			def __call__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = 1.0       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = False       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		self.controlnet_conditioning_channel_order
				if channel_order == "bgr":
					lowerCamelCase__								=		jnp.flip(__lowerCAmelCase       ,  axis=1							)

				# 1. time
				if not isinstance(__lowerCAmelCase       ,  jnp.ndarray							):
					lowerCamelCase__								=		jnp.array([timesteps]       ,  dtype=jnp.intaa							)
				elif isinstance(__lowerCAmelCase       ,  jnp.ndarray							) and len(timesteps.shape							) == 0:
					lowerCamelCase__								=		timesteps.astype(dtype=jnp.floataa							)
					lowerCamelCase__								=		jnp.expand_dims(__lowerCAmelCase       ,  0							)

				lowerCamelCase__								=		self.time_proj(__lowerCAmelCase							)
				lowerCamelCase__								=		self.time_embedding(__lowerCAmelCase							)

				# 2. pre-process
				lowerCamelCase__								=		jnp.transpose(__lowerCAmelCase       ,  (0, 2, 3, 1)							)
				lowerCamelCase__								=		self.conv_in(__lowerCAmelCase							)

				lowerCamelCase__								=		jnp.transpose(__lowerCAmelCase       ,  (0, 2, 3, 1)							)
				lowerCamelCase__								=		self.controlnet_cond_embedding(__lowerCAmelCase							)
				sample += controlnet_cond

				# 3. down
				lowerCamelCase__								=		(sample,)
				for down_block in self.down_blocks:
					if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							):
						lowerCamelCase__				,	lowerCamelCase__								=		down_block(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  deterministic=not train							)
					else:
						lowerCamelCase__				,	lowerCamelCase__								=		down_block(__lowerCAmelCase       ,  __lowerCAmelCase       ,  deterministic=not train							)
					down_block_res_samples += res_samples

				# 4. mid
				lowerCamelCase__								=		self.mid_block(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  deterministic=not train							)

				# 5. contronet blocks
				lowerCamelCase__								=		()
				for down_block_res_sample, controlnet_block in zip(__lowerCAmelCase       ,  self.controlnet_down_blocks							):
					lowerCamelCase__								=		controlnet_block(__lowerCAmelCase							)
					controlnet_down_block_res_samples += (down_block_res_sample,)

				lowerCamelCase__								=		controlnet_down_block_res_samples

				lowerCamelCase__								=		self.controlnet_mid_block(__lowerCAmelCase							)

				# 6. scaling
				lowerCamelCase__								=		[sample * conditioning_scale for sample in down_block_res_samples]
				mid_block_res_sample *= conditioning_scale

				if not return_dict:
					return (down_block_res_samples, mid_block_res_sample)

				return FlaxControlNetOutput(
				    down_block_res_samples=__lowerCAmelCase       ,  mid_block_res_sample=__lowerCAmelCase							)



