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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					torch.FloatTensor
 SCREAMING_SNAKE_CASE_				:					torch.FloatTensor
 SCREAMING_SNAKE_CASE_				:					Optional[torch.FloatTensor]       						=None




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[str]       						=2

 @register_to_config
 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      UpperCamelCase						    =					sigma_max

      # setable values
      UpperCamelCase						    =					None
      UpperCamelCase						    =					None
      UpperCamelCase						    =					None  # sigma(t_i)

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None   ):






      """simple docstring"""



      return sample

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Union[str, torch.device] = None   ):






      """simple docstring"""



      UpperCamelCase						    =					num_inference_steps
      UpperCamelCase						    =					np.arange(0					, self.num_inference_steps   )[::-1].copy()
      UpperCamelCase						    =					torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in self.timesteps
      ]
      UpperCamelCase						    =					torch.tensor(SCREAMING_SNAKE_CASE__					, dtype=torch.floataa					, device=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			Optional[torch.Generator] = None   ):






      """simple docstring"""



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

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					self.config.s_noise * randn_tensor(sample.shape					, generator=SCREAMING_SNAKE_CASE__   ).to(sample.device   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative)

      return KarrasVeOutput(
          prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, pred_original_sample=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative)

      return KarrasVeOutput(
          prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, pred_original_sample=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      raise NotImplementedError()




from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


