

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


_SCREAMING_SNAKE_CASE					:  List[Any]             =      transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if isinstance(_A				,		torch.Tensor    ):
          return image
     elif isinstance(_A				,		PIL.Image.Image    ):
          SCREAMING_SNAKE_CASE__   =   [image]

     SCREAMING_SNAKE_CASE__   =   [trans(img.convert('''RGB'''    )    ) for img in image]
     SCREAMING_SNAKE_CASE__   =   torch.stack(_A    )
     return image







class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def __init__(							self						:		Any     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Optional[int] )		->							Any:
       super().__init__()

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

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






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






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Any )		->							List[str]:
       # get the original timestep using init_timestep
       SCREAMING_SNAKE_CASE__   =   min(int(num_inference_steps * strength )     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   max(num_inference_steps - init_timestep     ,       0 )
       SCREAMING_SNAKE_CASE__   =   self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		str=None )		->							List[Any]:
       if not isinstance(__lowerCamelCase     ,       (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(__lowerCamelCase )}''' )

       SCREAMING_SNAKE_CASE__   =   image.to(device=__lowerCamelCase     ,       dtype=__lowerCamelCase )

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

       SCREAMING_SNAKE_CASE__   =   init_latents.shape
       SCREAMING_SNAKE_CASE__   =   randn_tensor(__lowerCamelCase     ,       generator=__lowerCamelCase     ,       device=__lowerCamelCase     ,       dtype=__lowerCamelCase )

       # get latents
       print('''add noise to latents at timestep'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   self.scheduler.add_noise(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   init_latents

       return latents







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

       # 2. Preprocess image
       SCREAMING_SNAKE_CASE__   =   preprocess(__lowerCamelCase )

       # 3. set timesteps
       self.scheduler.set_timesteps(__lowerCamelCase     ,       device=self.device )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.get_timesteps(__lowerCamelCase     ,       __lowerCamelCase     ,       self.device )
       SCREAMING_SNAKE_CASE__   =   timesteps[:1].repeat(__lowerCamelCase )

       # 4. Prepare latent variables
       SCREAMING_SNAKE_CASE__   =   self.prepare_latents(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       self.unet.dtype     ,       self.device     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   latents

       # 5. Denoising loop
       for t in self.progress_bar(__lowerCamelCase ):
            # 1. predict noise model_output
            SCREAMING_SNAKE_CASE__   =   self.unet(__lowerCamelCase     ,       __lowerCamelCase ).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__   =   self.scheduler.step(
                __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       eta=__lowerCamelCase     ,       use_clipped_model_output=__lowerCamelCase     ,       generator=__lowerCamelCase     ,       ).prev_sample

       SCREAMING_SNAKE_CASE__   =   (image / 2 + 0.5).clamp(0     ,       1 )
       SCREAMING_SNAKE_CASE__   =   image.cpu().permute(0     ,       2     ,       3     ,       1 ).numpy()
       if output_type == "pil":
            SCREAMING_SNAKE_CASE__   =   self.numpy_to_pil(__lowerCamelCase )

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

       return ImagePipelineOutput(images=__lowerCamelCase )





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


