
"""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


a					:	Any							=		transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

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

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







class 		a_						( _UpperCAmelCase	):







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Any     )		->Optional[Any]:






        '''simple docstring'''






        super().__init__()

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

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







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       Union[str, Any]     )		->Any:






        '''simple docstring'''






        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			:       Dict     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       int     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   min(int(num_inference_steps * strength     )     ,					__UpperCamelCase     )

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

        return timesteps, num_inference_steps - t_start







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       str=None     )		->Tuple:






        '''simple docstring'''






        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     )}"""     )

        _UpperCAmelCase          =   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."""     )

        _UpperCAmelCase          =   init_latents.shape
        _UpperCAmelCase          =   randn_tensor(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					device=__UpperCamelCase     ,					dtype=__UpperCamelCase     )

        # get latents
        print("""add noise to latents at timestep"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.scheduler.add_noise(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   init_latents

        return latents







    @torch.no_grad()
    def __call__(			self			:       int     ,					__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     ,					)		->Union[ImagePipelineOutput, Tuple]:






        '''simple docstring'''






        self.check_inputs(__UpperCamelCase     )

        # 2. Preprocess image
        _UpperCAmelCase          =   preprocess(__UpperCamelCase     )

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

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

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

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

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

        return ImagePipelineOutput(images=__UpperCamelCase     )

"""simple docstring"""



from __future__ import annotations


def  _UpperCamelCase	(						_A      )    ->  None:





    """simple docstring"""

    create_state_space_tree(_A					,      []					,      0					,      [0 for i in range(len(_A      )      )]      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  None:





    """simple docstring"""

    if index == len(_A      ):
        print(_A      )
        return

    for i in range(len(_A      )      ):
        if not index_used[i]:
            current_sequence.append(sequence[i]      )
            _UpperCAmelCase          =   True
            create_state_space_tree(_A					,      _A					,      index + 1					,      _A      )
            current_sequence.pop()
            _UpperCAmelCase          =   False
a					:	list[int | str]							=		[3, 1, 2, 4]
generate_all_permutations(sequence)

a					:	list[int | str]							=		["A", "B", "C"]







generate_all_permutations(sequence_a)