



"""simple docstring"""






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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin



@flax.struct.dataclass
class     UpperCAmelCase    :
   A__		:	Optional[int]           =  None
   A__		:	Optional[jnp.ndarray]           =  None
   A__		:	Optional[jnp.ndarray]           =  None  # sigma(t_i)



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Optional[Any]				):



     """simple docstring"""
     return cls()



@dataclass
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	jnp.ndarray
   A__		:	jnp.ndarray
   A__		:	KarrasVeSchedulerState



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE  ):



   @property
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return True



   @register_to_config
   def __init__(       self    :	Dict							,       __lowerCamelCase    :	float = 0.0_2							,       __lowerCamelCase    :	float = 1_0_0							,       __lowerCamelCase    :	float = 1.0_0_7							,       __lowerCamelCase    :	float = 8_0							,       __lowerCamelCase    :	float = 0.0_5							,       __lowerCamelCase    :	float = 5_0							,       ):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     return KarrasVeSchedulerState.create()



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	KarrasVeSchedulerState							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Tuple = ()				):



     """simple docstring"""
     _snake_case   	=      jnp.arange(0							,       SCREAMING_SNAKE_CASE__				)[::-1].copy()
     _snake_case   	=      [
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in timesteps
     ]

     return state.replace(
         num_inference_steps=SCREAMING_SNAKE_CASE__							,       schedule=jnp.array(SCREAMING_SNAKE_CASE__							,       dtype=jnp.floataa				)							,       timesteps=SCREAMING_SNAKE_CASE__							,       )



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	KarrasVeSchedulerState							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	random.KeyArray							,       ):



     """simple docstring"""



     if self.config.s_min <= sigma <= self.config.s_max:
       _snake_case   	=      min(self.config.s_churn / state.num_inference_steps							,       2**0.5 - 1				)
     else:
       _snake_case   	=      0

     # sample eps ~ N(0, S_noise^2 * I)
     _snake_case   	=      random.split(SCREAMING_SNAKE_CASE__							,       num=1				)
     _snake_case   	=      self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__							,       shape=sample.shape				)
     _snake_case   	=      sigma + gamma * sigma
     _snake_case   	=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	KarrasVeSchedulerState							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	bool = True							,       ):



     """simple docstring"""
     _snake_case   	=      sample_hat + sigma_hat * model_output
     _snake_case   	=      (sample_hat - pred_original_sample) / sigma_hat
     _snake_case   	=      sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
       return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__							,       derivative=SCREAMING_SNAKE_CASE__							,       state=SCREAMING_SNAKE_CASE__				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	KarrasVeSchedulerState							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	jnp.ndarray							,       __lowerCamelCase    :	bool = True							,       ):



     """simple docstring"""
     _snake_case   	=      sample_prev + sigma_prev * model_output
     _snake_case   	=      (sample_prev - pred_original_sample) / sigma_prev
     _snake_case   	=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
       return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__							,       derivative=SCREAMING_SNAKE_CASE__							,       state=SCREAMING_SNAKE_CASE__				)





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	KarrasVeSchedulerState							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     raise NotImplementedError()







def  __lowerCamelCase					( _lowercase			)       ->      list:
     UpperCamelCase						    =					len(_lowercase			)
     for _ in range(_lowercase			):
          for i in range(_ % 2						,     arr_size - 1						,     2			):
               if arr[i + 1] < arr[i]:
                    UpperCamelCase    ,		UpperCamelCase						    =					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _snake_case      = list(range(10, 0, -1))
 print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")


