






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       __a  :
 # setable values
 _lowerCAmelCase	:	Optional[int]				=			None
 _lowerCAmelCase	:	Optional[jnp.ndarray]				=			None
 _lowerCAmelCase	:	Optional[jnp.ndarray]				=			None  # sigma(t_i)







 @classmethod
 def  __lowercase (		cls :       Optional[int]						):


      '''simple docstring'''






      return cls()







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	KarrasVeSchedulerState







class       __a  (   A__	,       A__	):







 @property
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Any							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       float = 1_00							,       SCREAMING_SNAKE_CASE :       float = 1.0_0_7							,       SCREAMING_SNAKE_CASE :       float = 80							,       SCREAMING_SNAKE_CASE :       float = 0.0_5							,       SCREAMING_SNAKE_CASE :       float = 50							,       ):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return KarrasVeSchedulerState.create()







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       KarrasVeSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  jnp.arange(0							,       SCREAMING_SNAKE_CASE						)[::-1].copy()
      UpperCamelCase__      :       Any	    =  [
          (
              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  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       KarrasVeSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       float							,       SCREAMING_SNAKE_CASE :       random.KeyArray							,       ):


      '''simple docstring'''






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

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

      return sample_hat, sigma_hat







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       KarrasVeSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       float							,       SCREAMING_SNAKE_CASE :       float							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  sample_hat + sigma_hat * model_output
      UpperCamelCase__      :       Tuple	    =  (sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase__      :       Dict	    =  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  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       KarrasVeSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       float							,       SCREAMING_SNAKE_CASE :       float							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  sample_prev + sigma_prev * model_output
      UpperCamelCase__      :       Optional[int]	    =  (sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase__      :       int	    =  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  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       KarrasVeSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      raise NotImplementedError()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Dict    =logging.get_logger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	Optional[int]				=			'''timm_backbone'''







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=3							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       SCREAMING_SNAKE_CASE :       Dict=True							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  backbone
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       str	    =  features_only
      UpperCamelCase__      :       Dict	    =  use_pretrained_backbone
      UpperCamelCase__      :       Tuple	    =  True
      UpperCamelCase__      :       List[Any]	    =  out_indices if out_indices is not None else (-1,)