





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase_												=	2_00
# 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, 10_00))






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=random.randint(0				,							len(A     ) - 1     )
   UpperCAmelCase__						=parent_a[:random_slice] + parent_a[random_slice:]
   UpperCAmelCase__						=parent_a[:random_slice] + parent_a[random_slice:]
   return (child_a, child_a)






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A				,							A				,							):




   '''simple docstring'''

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

      UpperCAmelCase__			,							UpperCAmelCase__						=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 				_UpperCAmelCase			(    A				,							A				,							A = True     ):




   '''simple docstring'''

   if N_POPULATION < N_SELECTED:
      UpperCAmelCase__						=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.
   UpperCAmelCase__						=sorted({c for c in target if c not in genes}     )
   if not_in_genes_list:
      UpperCAmelCase__						=F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
      raise ValueError(A     )

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

      # Check if there is a matching evolution.
      UpperCAmelCase__						=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.
      UpperCAmelCase__						=population[: int(N_POPULATION / 3     )]
      population.clear()
      population.extend(A     )
      # Normalize population score to be between 0 and 1.
      UpperCAmelCase__						=[
          (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__":
    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}"""
    )










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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42
 __UpperCamelCase       =      42

class     snake_case_		(   nn.Module							):






 '''simple docstring'''





 __UpperCamelCase       =      42
 __UpperCamelCase       =      (1_6, 3_2, 9_6, 2_5_6)
 __UpperCamelCase       =      jnp.floataa

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=nn.Conv(
        self.block_out_channels[0],		kernel_size=(3, 3),		padding=((1, 1), (1, 1)),		dtype=self.dtype,		)

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

    UpperCAmelCase__						=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,		A_		)	->       Any:
    UpperCAmelCase__						=self.conv_in(A_		)
    UpperCAmelCase__						=nn.silu(A_		)

    for block in self.blocks:
       UpperCAmelCase__						=block(A_		)
       UpperCAmelCase__						=nn.silu(A_		)

    UpperCAmelCase__						=self.conv_out(A_		)

    return embedding

@flax_register_to_config
class     snake_case_		(   nn.Module,		a,		a							):






 '''simple docstring'''





 __UpperCamelCase       =      3_2
 __UpperCamelCase       =      4
 __UpperCamelCase       =      (
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 __UpperCamelCase       =      False
 __UpperCamelCase       =      (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
 __UpperCamelCase       =      2
 __UpperCamelCase       =      8
 __UpperCamelCase       =      None
 __UpperCamelCase       =      1_2_8_0
 __UpperCamelCase       =      0.0
 __UpperCamelCase       =      False
 __UpperCamelCase       =      jnp.floataa
 __UpperCamelCase       =      True
 __UpperCamelCase       =      0
 __UpperCamelCase       =      "rgb"
 __UpperCamelCase       =      (1_6, 3_2, 9_6, 2_5_6)

 def  __UpperCAmelCase						(  self,		A_		)	->       FrozenDict:
    # init input tensors
    UpperCAmelCase__						=(1, self.in_channels, self.sample_size, self.sample_size)
    UpperCAmelCase__						=jnp.zeros(A_,		dtype=jnp.floataa		)
    UpperCAmelCase__						=jnp.ones((1,),		dtype=jnp.intaa		)
    UpperCAmelCase__						=jnp.zeros((1, 1, self.cross_attention_dim),		dtype=jnp.floataa		)
    UpperCAmelCase__						=(1, 3, self.sample_size * 8, self.sample_size * 8)
    UpperCAmelCase__						=jnp.zeros(A_,		dtype=jnp.floataa		)

    UpperCAmelCase__			,							UpperCAmelCase__						=jax.random.split(A_		)
    UpperCAmelCase__						={"params": params_rng, "dropout": dropout_rng}

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

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=self.block_out_channels
    UpperCAmelCase__						=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.
    UpperCAmelCase__						=self.num_attention_heads or self.attention_head_dim

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

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

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

    UpperCAmelCase__						=self.only_cross_attention
    if isinstance(A_,		A_		):
       UpperCAmelCase__						=(only_cross_attention,) * len(self.down_block_types		)

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=(num_attention_heads,) * len(self.down_block_types		)

    # down
    UpperCAmelCase__						=[]
    UpperCAmelCase__						=[]

    UpperCAmelCase__						=block_out_channels[0]

    UpperCAmelCase__						=nn.Conv(
        A_,		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(A_		)

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

       if down_block_type == "CrossAttnDownBlock2D":
          UpperCAmelCase__						=FlaxCrossAttnDownBlockaD(
              in_channels=A_,		out_channels=A_,		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:
          UpperCAmelCase__						=FlaxDownBlockaD(
              in_channels=A_,		out_channels=A_,		dropout=self.dropout,		num_layers=self.layers_per_block,		add_downsample=not is_final_block,		dtype=self.dtype,		)

       down_blocks.append(A_		)

       for _ in range(self.layers_per_block		):
          UpperCAmelCase__						=nn.Conv(
              A_,		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(A_		)

       if not is_final_block:
          UpperCAmelCase__						=nn.Conv(
              A_,		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(A_		)

    UpperCAmelCase__						=down_blocks
    UpperCAmelCase__						=controlnet_down_blocks

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

    UpperCAmelCase__						=nn.Conv(
        A_,		kernel_size=(1, 1),		padding="VALID",		kernel_init=nn.initializers.zeros_init(),		bias_init=nn.initializers.zeros_init(),		dtype=self.dtype,		)






 def __call__(  self,		A_,		A_,		A_,		A_,		A_ = 1.0,		A_ = True,		A_ = False,		)	->       Union[FlaxControlNetOutput, Tuple]:
    UpperCAmelCase__						=self.controlnet_conditioning_channel_order
    if channel_order == "bgr":
       UpperCAmelCase__						=jnp.flip(A_,		axis=1		)

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

    UpperCAmelCase__						=self.time_proj(A_		)
    UpperCAmelCase__						=self.time_embedding(A_		)

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

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

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

    # 4. mid
    UpperCAmelCase__						=self.mid_block(A_,		A_,		A_,		deterministic=not train		)

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

    UpperCAmelCase__						=controlnet_down_block_res_samples

    UpperCAmelCase__						=self.controlnet_mid_block(A_		)

    # 6. scaling
    UpperCAmelCase__						=[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=A_,		mid_block_res_sample=A_		)



