



'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class       lowerCamelCase_							:






   '''simple docstring'''

   A :     Optional[jnp.ndarray]				=	None
   A :     Optional[jnp.ndarray]				=	None
   A :     Optional[jnp.ndarray]				=	None


   @classmethod
   def 	_UpperCamelCase       (  cls ):
       return cls()

@dataclass
class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     ScoreSdeVeSchedulerState
   A :     jnp.ndarray
   A :     Optional[jnp.ndarray]				=	None

class       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   @property
   def 	_UpperCamelCase       (  self ):
       return True


   @register_to_config
   def __init__(  self     ,						__UpperCamelCase = 20_00     ,						__UpperCamelCase = 0.15     ,						__UpperCamelCase = 0.01     ,						__UpperCamelCase = 1_348.0     ,						__UpperCamelCase = 1e-5     ,						__UpperCamelCase = 1     ,						):
       pass


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			ScoreSdeVeSchedulerState.create()
       return self.set_sigmas(
           __UpperCamelCase     ,						self.config.num_train_timesteps     ,						self.config.sigma_min     ,						self.config.sigma_max     ,						self.config.sampling_eps     ,						)


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = ()     ,						__UpperCamelCase = None ):
       UpperCamelCase__      =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__      =			jnp.linspace(1     ,						__UpperCamelCase     ,						__UpperCamelCase )
       return state.replace(timesteps=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						):
       UpperCamelCase__      =			sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__      =			sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__      =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
           UpperCamelCase__      =			self.set_timesteps(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase )

       UpperCamelCase__      =			jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase )     ,						jnp.log(__UpperCamelCase )     ,						__UpperCamelCase ) )
       UpperCamelCase__      =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps] )

       return state.replace(discrete_sigmas=__UpperCamelCase     ,						sigmas=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       return jnp.where(timesteps == 0     ,						jnp.zeros_like(__UpperCamelCase )     ,						state.discrete_sigmas[timesteps - 1] )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = True     ,						):
       if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )

       UpperCamelCase__      =			timestep * jnp.ones(
           sample.shape[0]     ,						)
       UpperCamelCase__      =			(timestep * (len(state.timesteps ) - 1)).long()

       UpperCamelCase__      =			state.discrete_sigmas[timesteps]
       UpperCamelCase__      =			self.get_adjacent_sigma(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			jnp.zeros_like(__UpperCamelCase )
       UpperCamelCase__      =			(sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__      =			diffusion.flatten()
       UpperCamelCase__      =			broadcast_to_shape_from_left(__UpperCamelCase     ,						sample.shape )
       UpperCamelCase__      =			drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__      =			random.split(__UpperCamelCase     ,						num=1 )
       UpperCamelCase__      =			random.normal(key=__UpperCamelCase     ,						shape=sample.shape )
       UpperCamelCase__      =			sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__      =			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean, state)

       return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,						prev_sample_mean=__UpperCamelCase     ,						state=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = True     ,						):
       if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__      =			random.split(__UpperCamelCase     ,						num=1 )
       UpperCamelCase__      =			random.normal(key=__UpperCamelCase     ,						shape=sample.shape )

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__      =			jnp.linalg.norm(__UpperCamelCase )
       UpperCamelCase__      =			jnp.linalg.norm(__UpperCamelCase )
       UpperCamelCase__      =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__      =			step_size * jnp.ones(sample.shape[0] )

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__      =			step_size.flatten()
       UpperCamelCase__      =			broadcast_to_shape_from_left(__UpperCamelCase     ,						sample.shape )
       UpperCamelCase__      =			sample + step_size * model_output
       UpperCamelCase__      =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample, state)

       return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,						state=__UpperCamelCase )







   def __len__(  self ):
       return self.config.num_train_timesteps









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
      print('Enter the array elements:')
      lowercase__     :  Optional[int]    					=			list(map(int, input().split()))
      print('The unsorted array is:')
      print(array)
      print('Array after Wiggle sort:')
      print(wiggle_sort(array))




