'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:UNetaDModel
  lowerCamelCase_		:KarrasVeScheduler


  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       super().__init__()
       self.register_modules(unet=snake_case_				,		scheduler=snake_case_						)






  @torch.no_grad()
  def __call__(			self				,		snake_case_ = 1				,		snake_case_ = 5_0				,		snake_case_ = None				,		snake_case_ = "pil"				,		snake_case_ = True				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.unet.config.sample_size
       UpperCAmelCase_							:       Tuple       =				(batch_size, 3, img_size, img_size)

       UpperCAmelCase_							:       Optional[int]       =				self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       UpperCAmelCase_							:       List[str]       =				randn_tensor(snake_case_				,		generator=snake_case_				,		device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(snake_case_						)

       for t in self.progress_bar(self.scheduler.timesteps						):
            # here sigma_t == t_i from the paper
            UpperCAmelCase_							:       int       =				self.scheduler.schedule[t]
            UpperCAmelCase_							:       Optional[int]       =				self.scheduler.schedule[t - 1] if t > 0 else 0

            # 1. Select temporarily increased noise level sigma_hat
            # 2. Add new noise to move from sample_i to sample_hat
            UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				self.scheduler.add_noise_to_input(snake_case_				,		snake_case_				,		generator=snake_case_						)

            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
            # The model inputs and output are adjusted by following eq. (213) in [1].
            UpperCAmelCase_							:       Union[str, Any]       =				(sigma_hat / 2) * model((sample_hat + 1) / 2				,		sigma_hat / 2						).sample

            # 4. Evaluate dx/dt at sigma_hat
            # 5. Take Euler step from sigma to sigma_prev
            UpperCAmelCase_							:       List[str]       =				self.scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

            if sigma_prev != 0:
                 # 6. Apply 2nd order correction
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 UpperCAmelCase_							:       Dict       =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,		sigma_prev / 2						).sample
                 UpperCAmelCase_							:       Dict       =				self.scheduler.step_correct(
                     snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		step_output.prev_sample				,		step_output['derivative']				,		)
            UpperCAmelCase_							:       str       =				step_output.prev_sample

       UpperCAmelCase_							:       Optional[Any]       =				(sample / 2 + 0.5).clamp(0				,		1						)
       UpperCAmelCase_							:       Dict       =				sample.cpu().permute(0				,		2				,		3				,		1						).numpy()
       if output_type == "pil":
            UpperCAmelCase_							:       Optional[Any]       =				self.numpy_to_pil(snake_case_						)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=snake_case_						)







'''simple docstring'''


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  @register_to_config
  def __init__(			self				,		snake_case_ = 7_6_8				,		):




       '''simple docstring'''

       super().__init__()

       UpperCAmelCase_							:       int       =				nn.Parameter(torch.zeros(1				,		snake_case_						)						)
       UpperCAmelCase_							:       str       =				nn.Parameter(torch.ones(1				,		snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_ = None				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				nn.Parameter(self.mean.to(snake_case_						).to(snake_case_						)						)
       UpperCAmelCase_							:       Tuple       =				nn.Parameter(self.std.to(snake_case_						).to(snake_case_						)						)
       return self


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				(embeds - self.mean) * 1.0 / self.std
       return embeds






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				(embeds * self.std) + self.mean
       return embeds






