
"""simple docstring"""




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						image.size
     UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     UpperCAmelCase__							: str 							=						image.resize((w, h)       ,    resample=PIL_INTERPOLATION["lanczos"]		)
     UpperCAmelCase__							: Union[str, Any] 							=						np.array(lowerCAmelCase		).astype(np.floataa		) / 255.0
     UpperCAmelCase__							: List[str] 							=						image[None].transpose(0       ,    3       ,    1       ,    2		)
     UpperCAmelCase__							: List[Any] 							=						torch.from_numpy(lowerCAmelCase		)
     return 2.0 * image - 1.0







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					Optional[int]:
          super().__init__()
          self.register_modules(vqvae=__UpperCamelCase ,			unet=__UpperCamelCase ,			scheduler=__UpperCamelCase  )



     @torch.no_grad()
     def __call__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = 1 ,			__UpperCamelCase = 1_00 ,			__UpperCamelCase = 0.0 ,			__UpperCamelCase = None ,			__UpperCamelCase = "pil" ,			__UpperCamelCase = True ,			)->					Union[Tuple, ImagePipelineOutput]:
          if isinstance(__UpperCamelCase ,			PIL.Image.Image  ):
               UpperCAmelCase__							: Dict 							=						1
          elif isinstance(__UpperCamelCase ,			torch.Tensor  ):
               UpperCAmelCase__							: Optional[int] 							=						image.shape[0]
          else:
               raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__UpperCamelCase  )}"  )

          if isinstance(__UpperCamelCase ,			PIL.Image.Image  ):
               UpperCAmelCase__							: str 							=						preprocess(__UpperCamelCase  )

          UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						image.shape[-2:]

          # in_channels should be 6: 3 for latents, 3 for low resolution image
          UpperCAmelCase__							: int 							=						(batch_size, self.unet.config.in_channels // 2, height, width)
          UpperCAmelCase__							: Union[str, Any] 							=						next(self.unet.parameters()  ).dtype

          UpperCAmelCase__							: List[Any] 							=						randn_tensor(__UpperCamelCase ,			generator=__UpperCamelCase ,			device=self.device ,			dtype=__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						image.to(device=self.device ,			dtype=__UpperCamelCase  )

          # set timesteps and move to the correct device
          self.scheduler.set_timesteps(__UpperCamelCase ,			device=self.device  )
          UpperCAmelCase__							: List[Any] 							=						self.scheduler.timesteps

          # scale the initial noise by the standard deviation required by the scheduler
          UpperCAmelCase__							: Optional[Any] 							=						latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          UpperCAmelCase__							: Optional[Any] 							=						"eta" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
          UpperCAmelCase__							: Optional[int] 							=						{}
          if accepts_eta:
               UpperCAmelCase__							: List[str] 							=						eta

          for t in self.progress_bar(__UpperCamelCase  ):
               # concat latents and low resolution image in the channel dimension.
               UpperCAmelCase__							: Any 							=						torch.cat([latents, image] ,			dim=1  )
               UpperCAmelCase__							: Optional[Any] 							=						self.scheduler.scale_model_input(__UpperCamelCase ,			__UpperCamelCase  )
               # predict the noise residual
               UpperCAmelCase__							: Union[str, Any] 							=						self.unet(__UpperCamelCase ,			__UpperCamelCase  ).sample
               # compute the previous noisy sample x_t -> x_t-1
               UpperCAmelCase__							: List[Any] 							=						self.scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			**__UpperCamelCase  ).prev_sample

          # decode the image latents with the VQVAE
          UpperCAmelCase__							: List[Any] 							=						self.vqvae.decode(__UpperCamelCase  ).sample
          UpperCAmelCase__							: List[Any] 							=						torch.clamp(__UpperCamelCase ,			-1.0 ,			1.0  )
          UpperCAmelCase__							: Any 							=						image / 2 + 0.5
          UpperCAmelCase__							: Optional[int] 							=						image.cpu().permute(0 ,			2 ,			3 ,			1  ).numpy()

          if output_type == "pil":
               UpperCAmelCase__							: Any 							=						self.numpy_to_pil(__UpperCamelCase  )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=__UpperCamelCase  )





"""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   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



