



"""simple docstring"""

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


lowercase__					:						Optional[int]												=			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def __lowercase    (							_a					):
    if isinstance(_a	,    torch.Tensor					):
        return image
    elif isinstance(_a	,    PIL.Image.Image					):
        snake_case_				:  int       =			[image]

    snake_case_				:  Union[str, Any]       =			[trans(img.convert('''RGB'''					)					) for img in image]
    snake_case_				:  int       =			torch.stack(_a					)
    return image






class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: Tuple						,  lowercase_	: int						,  lowercase_	: Union[str, Any]       ):
     super().__init__()

     # make sure scheduler can always be converted to DDIM
     snake_case_				:  List[Any]       =			DDIMScheduler.from_config(scheduler.config       )

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

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Optional[int]       ):
     if strength < 0 or strength > 1:
         raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"       )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Optional[int]						,  lowercase_	: Any						,  lowercase_	: Any       ):
     # get the original timestep using init_timestep
     snake_case_				:  List[Any]       =			min(int(num_inference_steps * strength       )						,  lowercase_       )

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

     return timesteps, num_inference_steps - t_start

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: List[Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Dict						,  lowercase_	: Optional[Any]						,  lowercase_	: Optional[Any]						,  lowercase_	: Dict=None       ):
     if not isinstance(lowercase_						,  (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(lowercase_       )}"       )

     snake_case_				:  Optional[Any]       =			image.to(device=lowercase_						,  dtype=lowercase_       )

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

     snake_case_				:  Dict       =			init_latents.shape
     snake_case_				:  List[Any]       =			randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_						,  dtype=lowercase_       )

     # get latents
     print('''add noise to latents at timestep'''						,  lowercase_       )
     snake_case_				:  Optional[Any]       =			self.scheduler.add_noise(lowercase_						,  lowercase_						,  lowercase_       )
     snake_case_				:  str       =			init_latents

     return latents






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

     # 2. Preprocess image
     snake_case_				:  str       =			preprocess(lowercase_       )

     # 3. set timesteps
     self.scheduler.set_timesteps(lowercase_						,  device=self.device       )
     snake_case_,			snake_case_				:  Any       =			self.get_timesteps(lowercase_						,  lowercase_						,  self.device       )
     snake_case_				:  Optional[Any]       =			timesteps[:1].repeat(lowercase_       )

     # 4. Prepare latent variables
     snake_case_				:  Any       =			self.prepare_latents(lowercase_						,  lowercase_						,  lowercase_						,  self.unet.dtype						,  self.device						,  lowercase_       )
     snake_case_				:  Union[str, Any]       =			latents

     # 5. Denoising loop
     for t in self.progress_bar(lowercase_       ):
         # 1. predict noise model_output
         snake_case_				:  str       =			self.unet(lowercase_						,  lowercase_       ).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
         snake_case_				:  Dict       =			self.scheduler.step(
             lowercase_						,  lowercase_						,  lowercase_						,  eta=lowercase_						,  use_clipped_model_output=lowercase_						,  generator=lowercase_						,  ).prev_sample

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

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

     return ImagePipelineOutput(images=lowercase_       )










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





