
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase__          =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__          =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__          =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  int		    =  len([g for position, g in enumerate(lowerCAmelCase__  ) if g == main_target[position]]  )
 return (item, float(lowerCAmelCase__  ))




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  int		    =  random.randint(0  ,       len(lowerCAmelCase__  ) - 1  )
 __a			:  str		    =  parent_a[:random_slice] + parent_a[random_slice:]
 __a			:  Any		    =  parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    list[str]  ):
 __a			:  Tuple		    =  list(lowerCAmelCase__  )
 if random.uniform(0  ,       1  ) < MUTATION_PROBABILITY:
  __a			:  List[str]		    =  random.choice(lowerCAmelCase__  )
 return "".join(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    tuple[str, float]  ,       lowerCAmelCase__							:    list[tuple[str, float]]  ,       lowerCAmelCase__							:    list[str]  ,       ):
 __a			:  Any		    =  []
 # Generate more children proportionally to the fitness score.
 __a			:  int		    =  int(parent_a[1] * 1_0_0  ) + 1
 __a			:  Any		    =  1_0 if child_n >= 1_0 else child_n
 for _ in range(lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  population_score[random.randint(0  ,       lowerCAmelCase__  )][0]

  __a    ,	__a			:  Any		    =  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        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    list[str]  ,       lowerCAmelCase__							:    bool = True  ):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  __a			:  Any		    =  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.
 __a			:  str		    =  sorted({c for c in target if c not in genes}  )
 if not_in_genes_list:
  __a			:  Union[str, Any]		    =  f"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(lowerCAmelCase__  )

 # Generate random starting population.
 __a			:  Optional[Any]		    =  []
 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.
 __a    ,	__a			:  str		    =  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.
  __a			:  Dict		    =  [evaluate(lowerCAmelCase__  ,       lowerCAmelCase__  ) for item in population]

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


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							CommonSchedulerState

   # setable values
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   @classmethod
   def 				lowerCAmelCase      (cls			: int ,    snake_case_			: CommonSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray       ):
    return cls(common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_       )





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							DDPMSchedulerState





class 		UpperCamelCase__  (	__lowercase   ,__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   _SCREAMING_SNAKE_CASE				:							jnp.dtype
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return True
   @register_to_config
   def __init__(self			: Any ,    snake_case_			: int = 1_0_0_0 ,    snake_case_			: float = 0.0001 ,    snake_case_			: float = 0.02 ,    snake_case_			: str = "linear" ,    snake_case_			: Optional[jnp.ndarray] = None ,    snake_case_			: str = "fixed_small" ,    snake_case_			: bool = True ,    snake_case_			: str = "epsilon" ,    snake_case_			: jnp.dtype = jnp.floataa ,    ):
    __a			:  str		    =  dtype
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[CommonSchedulerState] = None       ):
    if common is None:
     __a			:  Optional[Any]		    =  CommonSchedulerState.create(self       )

    # standard deviation of the initial noise distribution
    __a			:  int		    =  jnp.array(1.0 ,    dtype=self.dtype       )

    __a			:  List[Any]		    =  jnp.arange(0 ,    self.config.num_train_timesteps       ).round()[::-1]

    return DDPMSchedulerState.create(
        common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[int] = None       ):
    return sample
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: int ,    snake_case_			: Tuple = ()       ):
    __a			:  Tuple		    =  self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    __a			:  Any		    =  (jnp.arange(0 ,    snake_case_       ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str]=None ,    snake_case_			: Union[str, Any]=None       ):
    __a			:  Optional[Any]		    =  state.common.alphas_cumprod[t]
    __a			:  List[str]		    =  jnp.where(t > 0 ,    state.common.alphas_cumprod[t - 1] ,    jnp.array(1.0 ,    dtype=self.dtype       )       )

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    __a			:  Optional[int]		    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

    if variance_type is None:
     __a			:  List[str]		    =  self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small":
     __a			:  Optional[Any]		    =  jnp.clip(snake_case_ ,    a_min=1E-20       )
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
     __a			:  int		    =  jnp.log(jnp.clip(snake_case_ ,    a_min=1E-20       )       )
    elif variance_type == "fixed_large":
     __a			:  List[str]		    =  state.common.betas[t]
    elif variance_type == "fixed_large_log":
     # Glide max_log
     __a			:  Union[str, Any]		    =  jnp.log(state.common.betas[t]       )
    elif variance_type == "learned":
     return predicted_variance
    elif variance_type == "learned_range":
     __a			:  Any		    =  variance
     __a			:  Dict		    =  state.common.betas[t]
     __a			:  Any		    =  (predicted_variance + 1) / 2
     __a			:  Optional[Any]		    =  frac * max_log + (1 - frac) * min_log

    return variance
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: int ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[jax.random.KeyArray] = None ,    snake_case_			: bool = True ,    ):
    __a			:  int		    =  timestep

    if key is None:
     __a			:  Any		    =  jax.random.PRNGKey(0       )

    if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
     __a    ,	__a			:  List[str]		    =  jnp.split(snake_case_ ,    sample.shape[1] ,    axis=1       )
    else:
     __a			:  int		    =  None

    # 1. compute alphas, betas
    __a			:  Optional[int]		    =  state.common.alphas_cumprod[t]
    __a			:  Tuple		    =  jnp.where(t > 0 ,    state.common.alphas_cumprod[t - 1] ,    jnp.array(1.0 ,    dtype=self.dtype       )       )
    __a			:  Optional[int]		    =  1 - alpha_prod_t
    __a			:  Union[str, Any]		    =  1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
     __a			:  int		    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
     __a			:  Union[str, Any]		    =  model_output
    elif self.config.prediction_type == "v_prediction":
     __a			:  Tuple		    =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
         ''' for the FlaxDDPMScheduler.'''       )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
     __a			:  Dict		    =  jnp.clip(snake_case_ ,    -1 ,    1       )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __a			:  str		    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
    __a			:  str		    =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __a			:  Tuple		    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    def random_variance():
     __a			:  Optional[int]		    =  jax.random.split(snake_case_ ,    num=1       )
     __a			:  Union[str, Any]		    =  jax.random.normal(snake_case_ ,    shape=model_output.shape ,    dtype=self.dtype       )
     return (self._get_variance(snake_case_ ,    snake_case_ ,    predicted_variance=snake_case_       ) ** 0.5) * noise

    __a			:  Dict		    =  jnp.where(t > 0 ,    random_variance() ,    jnp.zeros(model_output.shape ,    dtype=self.dtype       )       )

    __a			:  Tuple		    =  pred_prev_sample + variance

    if not return_dict:
     return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=snake_case_ ,    state=snake_case_       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return add_noise_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return get_velocity_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )







   def __len__(self			: List[str]       ):
    return self.config.num_train_timesteps
