






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case     					=		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  if isinstance(lowercase    ,  torch.Tensor					):
    return image
  elif isinstance(lowercase    ,  PIL.Image.Image					):
    SCREAMING_SNAKE_CASE							:   Dict						    =     [image]

  SCREAMING_SNAKE_CASE							:   Tuple						    =     [trans(img.convert("RGB"					)					) for img in image]
  SCREAMING_SNAKE_CASE							:   Dict						    =     torch.stack(lowercase					)
  return image


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def __init__(					self							:						List[str]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Tuple   ):
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       SCREAMING_SNAKE_CASE							:   Dict						    =     DDIMScheduler.from_config(scheduler.config   )

       self.register_modules(unet=UpperCAmelCase_       ,			scheduler=UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Optional[Any]   ):
       if strength < 0 or strength > 1:
         raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Any   ):
       # get the original timestep using init_timestep
       SCREAMING_SNAKE_CASE							:   str						    =     min(int(num_inference_steps * strength   )       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     max(num_inference_steps - init_timestep       ,			0   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]=None   ):
       if not isinstance(UpperCAmelCase_       ,			(torch.Tensor, PIL.Image.Image, list)   ):
         raise ValueError(
             f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_   )}'''   )

       SCREAMING_SNAKE_CASE							:   str						    =     image.to(device=UpperCAmelCase_       ,			dtype=UpperCAmelCase_   )

       if isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ) and len(UpperCAmelCase_   ) != batch_size:
         raise ValueError(
             f'''You have passed a list of generators of length {len(UpperCAmelCase_   )}, but requested an effective batch'''
             f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     init_latents.shape
       SCREAMING_SNAKE_CASE							:   Any						    =     randn_tensor(UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			device=UpperCAmelCase_       ,			dtype=UpperCAmelCase_   )

       # get latents
       print("add noise to latents at timestep"       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.scheduler.add_noise(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     init_latents

       return latents






     @torch.no_grad()
     def __call__(					self							:						List[str]       ,			UpperCAmelCase_							:						Union[torch.FloatTensor, PIL.Image.Image] = None       ,			UpperCAmelCase_							:						float = 0.8       ,			UpperCAmelCase_							:						int = 1       ,			UpperCAmelCase_							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,			UpperCAmelCase_							:						float = 0.0       ,			UpperCAmelCase_							:						int = 50       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			UpperCAmelCase_							:						Optional[str] = "pil"       ,			UpperCAmelCase_							:						bool = True       ,			):
       self.check_inputs(UpperCAmelCase_   )

       # 2. Preprocess image
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     preprocess(UpperCAmelCase_   )

       # 3. set timesteps
       self.scheduler.set_timesteps(UpperCAmelCase_       ,			device=self.device   )
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     self.get_timesteps(UpperCAmelCase_       ,			UpperCAmelCase_       ,			self.device   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     timesteps[:1].repeat(UpperCAmelCase_   )

       # 4. Prepare latent variables
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.prepare_latents(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			self.unet.dtype       ,			self.device       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     latents

       # 5. Denoising loop
       for t in self.progress_bar(UpperCAmelCase_   ):
         # 1. predict noise model_output
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.unet(UpperCAmelCase_       ,			UpperCAmelCase_   ).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         SCREAMING_SNAKE_CASE							:   Dict						    =     self.scheduler.step(
             UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			eta=UpperCAmelCase_       ,			use_clipped_model_output=UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			).prev_sample

       SCREAMING_SNAKE_CASE							:   Tuple						    =     (image / 2 + 0.5).clamp(0       ,			1   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     image.cpu().permute(0       ,			2       ,			3       ,			1   ).numpy()
       if output_type == "pil":
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.numpy_to_pil(UpperCAmelCase_   )

       if not return_dict:
         return (image, latent_timestep.item())

       return ImagePipelineOutput(images=UpperCAmelCase_   )







'''simple docstring'''


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


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import (
					    ImageTextPipelineOutput,
					    UniDiffuserPipeline,
					)
else:
					from .modeling_text_decoder import UniDiffuserTextDecoder
					from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
					from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
