

'''simple docstring'''
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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	jnp.ndarray




@flax_register_to_config
class 					_snake_case		(nn.Module			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE):
			__A  :	int        =32
			__A  :	int        =4
			__A  :	int        =4
			__A  :	Tuple[str]        =(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			__A  :	Tuple[str]        =("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
			__A  :	Union[bool, Tuple[bool]]        =False
			__A  :	Tuple[int]        =(3_20, 6_40, 12_80, 12_80)
			__A  :	int        =2
			__A  :	Union[int, Tuple[int]]        =8
			__A  :	Optional[Union[int, Tuple[int]]]        =None
			__A  :	int        =12_80
			__A  :	float        =0.0
			__A  :	bool        =False
			__A  :	jnp.dtype        =jnp.floataa
			__A  :	bool        =True
			__A  :	int        =0
			__A  :	bool        =False






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				# init input tensors
				UpperCAmelCase_     :      List[str]						=  (1, self.in_channels, self.sample_size, self.sample_size)
				UpperCAmelCase_     :      Tuple						=  jnp.zeros(_snake_case			,dtype=jnp.floataa     )
				UpperCAmelCase_     :      Optional[Any]						=  jnp.ones((1,)			,dtype=jnp.intaa     )
				UpperCAmelCase_     :      Optional[Any]						=  jnp.zeros((1, 1, self.cross_attention_dim)			,dtype=jnp.floataa     )

				UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  jax.random.split(_snake_case     )
				UpperCAmelCase_     :      int						=  {"params": params_rng, "dropout": dropout_rng}

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  self.block_out_channels
				UpperCAmelCase_     :      Tuple						=  block_out_channels[0] * 4

				if self.num_attention_heads is not None:
					raise ValueError(
					    "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."     )

				# 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_     :      Union[str, Any]						=  self.num_attention_heads or self.attention_head_dim

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

				# time
				UpperCAmelCase_     :      Tuple						=  FlaxTimesteps(
				    block_out_channels[0]			,flip_sin_to_cos=self.flip_sin_to_cos			,freq_shift=self.config.freq_shift     )
				UpperCAmelCase_     :      int						=  FlaxTimestepEmbedding(_snake_case			,dtype=self.dtype     )

				UpperCAmelCase_     :      str						=  self.only_cross_attention
				if isinstance(_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      Dict						=  (only_cross_attention,) * len(self.down_block_types     )

				if isinstance(_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      List[str]						=  (num_attention_heads,) * len(self.down_block_types     )

				# down
				UpperCAmelCase_     :      int						=  []
				UpperCAmelCase_     :      Optional[Any]						=  block_out_channels[0]
				for i, down_block_type in enumerate(self.down_block_types     ):
					UpperCAmelCase_     :      Any						=  output_channel
					UpperCAmelCase_     :      List[Any]						=  block_out_channels[i]
					UpperCAmelCase_     :      str						=  i == len(_snake_case     ) - 1

					if down_block_type == "CrossAttnDownBlock2D":
						UpperCAmelCase_     :      Optional[Any]						=  FlaxCrossAttnDownBlockaD(
						    in_channels=_snake_case			,out_channels=_snake_case			,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]			,use_memory_efficient_attention=self.use_memory_efficient_attention			,dtype=self.dtype			,)
					else:
						UpperCAmelCase_     :      Optional[int]						=  FlaxDownBlockaD(
						    in_channels=_snake_case			,out_channels=_snake_case			,dropout=self.dropout			,num_layers=self.layers_per_block			,add_downsample=not is_final_block			,dtype=self.dtype			,)

					down_blocks.append(_snake_case     )
				UpperCAmelCase_     :      Any						=  down_blocks

				# mid
				UpperCAmelCase_     :      Union[str, Any]						=  FlaxUNetMidBlockaDCrossAttn(
				    in_channels=block_out_channels[-1]			,dropout=self.dropout			,num_attention_heads=num_attention_heads[-1]			,use_linear_projection=self.use_linear_projection			,use_memory_efficient_attention=self.use_memory_efficient_attention			,dtype=self.dtype			,)

				# up
				UpperCAmelCase_     :      str						=  []
				UpperCAmelCase_     :      Union[str, Any]						=  list(reversed(_snake_case     )     )
				UpperCAmelCase_     :      Union[str, Any]						=  list(reversed(_snake_case     )     )
				UpperCAmelCase_     :      str						=  list(reversed(_snake_case     )     )
				UpperCAmelCase_     :      Any						=  reversed_block_out_channels[0]
				for i, up_block_type in enumerate(self.up_block_types     ):
					UpperCAmelCase_     :      Tuple						=  output_channel
					UpperCAmelCase_     :      Any						=  reversed_block_out_channels[i]
					UpperCAmelCase_     :      List[Any]						=  reversed_block_out_channels[min(i + 1			,len(_snake_case     ) - 1     )]

					UpperCAmelCase_     :      Optional[Any]						=  i == len(_snake_case     ) - 1

					if up_block_type == "CrossAttnUpBlock2D":
						UpperCAmelCase_     :      Dict						=  FlaxCrossAttnUpBlockaD(
						    in_channels=_snake_case			,out_channels=_snake_case			,prev_output_channel=_snake_case			,num_layers=self.layers_per_block + 1			,num_attention_heads=reversed_num_attention_heads[i]			,add_upsample=not is_final_block			,dropout=self.dropout			,use_linear_projection=self.use_linear_projection			,only_cross_attention=only_cross_attention[i]			,use_memory_efficient_attention=self.use_memory_efficient_attention			,dtype=self.dtype			,)
					else:
						UpperCAmelCase_     :      str						=  FlaxUpBlockaD(
						    in_channels=_snake_case			,out_channels=_snake_case			,prev_output_channel=_snake_case			,num_layers=self.layers_per_block + 1			,add_upsample=not is_final_block			,dropout=self.dropout			,dtype=self.dtype			,)

					up_blocks.append(_snake_case     )
					UpperCAmelCase_     :      List[Any]						=  output_channel
				UpperCAmelCase_     :      List[str]						=  up_blocks

				# out
				UpperCAmelCase_     :      Optional[int]						=  nn.GroupNorm(num_groups=32			,epsilon=1E-5     )
				UpperCAmelCase_     :      str						=  nn.Conv(
				    self.out_channels			,kernel_size=(3, 3)			,strides=(1, 1)			,padding=((1, 1), (1, 1))			,dtype=self.dtype			,)






			def __call__(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case=None			,_snake_case=None			,_snake_case = True			,_snake_case = False			,):
				# 1. time
				if not isinstance(_snake_case			,jnp.ndarray     ):
					UpperCAmelCase_     :      Optional[int]						=  jnp.array([timesteps]			,dtype=jnp.intaa     )
				elif isinstance(_snake_case			,jnp.ndarray     ) and len(timesteps.shape     ) == 0:
					UpperCAmelCase_     :      List[str]						=  timesteps.astype(dtype=jnp.floataa     )
					UpperCAmelCase_     :      Optional[int]						=  jnp.expand_dims(_snake_case			,0     )

				UpperCAmelCase_     :      Union[str, Any]						=  self.time_proj(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  self.time_embedding(_snake_case     )

				# 2. pre-process
				UpperCAmelCase_     :      Optional[int]						=  jnp.transpose(_snake_case			,(0, 2, 3, 1)     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.conv_in(_snake_case     )

				# 3. down
				UpperCAmelCase_     :      int						=  (sample,)
				for down_block in self.down_blocks:
					if isinstance(_snake_case			,_snake_case     ):
						UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  down_block(_snake_case			,_snake_case			,_snake_case			,deterministic=not train     )
					else:
						UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  down_block(_snake_case			,_snake_case			,deterministic=not train     )
					down_block_res_samples += res_samples

				if down_block_additional_residuals is not None:
					UpperCAmelCase_     :      int						=  ()

					for down_block_res_sample, down_block_additional_residual in zip(
					    _snake_case			,_snake_case     ):
						down_block_res_sample += down_block_additional_residual
						new_down_block_res_samples += (down_block_res_sample,)

					UpperCAmelCase_     :      Optional[Any]						=  new_down_block_res_samples

				# 4. mid
				UpperCAmelCase_     :      Dict						=  self.mid_block(_snake_case			,_snake_case			,_snake_case			,deterministic=not train     )

				if mid_block_additional_residual is not None:
					sample += mid_block_additional_residual

				# 5. up
				for up_block in self.up_blocks:
					UpperCAmelCase_     :      List[str]						=  down_block_res_samples[-(self.layers_per_block + 1) :]
					UpperCAmelCase_     :      Tuple						=  down_block_res_samples[: -(self.layers_per_block + 1)]
					if isinstance(_snake_case			,_snake_case     ):
						UpperCAmelCase_     :      Dict						=  up_block(
						    _snake_case			,temb=_snake_case			,encoder_hidden_states=_snake_case			,res_hidden_states_tuple=_snake_case			,deterministic=not train			,)
					else:
						UpperCAmelCase_     :      Tuple						=  up_block(_snake_case			,temb=_snake_case			,res_hidden_states_tuple=_snake_case			,deterministic=not train     )

        # 6. post-process
				UpperCAmelCase_     :      Optional[int]						=  self.conv_norm_out(_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  nn.silu(_snake_case     )
				UpperCAmelCase_     :      str						=  self.conv_out(_snake_case     )
				UpperCAmelCase_     :      str						=  jnp.transpose(_snake_case			,(0, 3, 1, 2)     )

				if not return_dict:
					return (sample,)

				return FlaxUNetaDConditionOutput(sample=_snake_case     )







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__      ( __A    :str	,__A    :str		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :str		):


       """simple docstring"""




       __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 						lowerCamelCase__      ( __A    :str	,__A    :list[str]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :tuple[str, float]	,__A    :list[tuple[str, float]]	,__A    :list[str]	,):


       """simple docstring"""




       __snake_case       	=							[]
       # Generate more children proportionally to the fitness score.
       __snake_case       	=							int(parent_a[1] * 1_0_0		) + 1
       __snake_case       	=							1_0 if child_n >= 1_0 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 						lowerCamelCase__      ( __A    :str	,__A    :list[str]	,__A    :bool = True		):


       """simple docstring"""




       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 % 1_0 == 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__":
    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}'
    )

