


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     =			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:

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

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







class   __lowercase				(  _UpperCAmelCase  ):





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






         """simple docstring"""







         super().__init__()

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

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





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					str       )    ->   List[str]:






         """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        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     min(int(num_inference_steps * strength       )					,       lowercase__       )

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

         return timesteps, num_inference_steps - t_start





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Tuple					,       __lowerCamelCase						:					int=None       )    ->   List[str]:






         """simple docstring"""







         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__       )}"""       )

         UpperCAmelCase							=     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."""       )

         UpperCAmelCase							=     init_latents.shape
         UpperCAmelCase							=     randn_tensor(lowercase__					,       generator=lowercase__					,       device=lowercase__					,       dtype=lowercase__       )

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

         return latents





      @torch.no_grad()
      def __call__(		self						:					Optional[int]					,       __lowerCamelCase						:					List[str] = None					,       __lowerCamelCase						:					Any = 0.8					,       __lowerCamelCase						:					int = 1					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					Optional[Any] = 0.0					,       __lowerCamelCase						:					Optional[Any] = 5_0					,       __lowerCamelCase						:					List[Any] = None					,       __lowerCamelCase						:					Tuple = "pil"					,       __lowerCamelCase						:					List[Any] = True					,       )    ->   Dict:






         """simple docstring"""







         self.check_inputs(lowercase__       )

         # 2. Preprocess image
         UpperCAmelCase							=     preprocess(lowercase__       )

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

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

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

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

         return ImagePipelineOutput(images=lowercase__       )







from __future__ import annotations
def   _UpperCamelCase				(						lowerCAmelCase_   )						->None:
   create_state_space_tree(lowerCAmelCase_  ,						[]  ,						0  ,						[0 for i in range(len(lowerCAmelCase_   )   )]   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->None:

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

   for i in range(len(lowerCAmelCase_   )   ):
      if not index_used[i]:
         current_sequence.append(sequence[i]   )
         UpperCAmelCase							=     True
         create_state_space_tree(lowerCAmelCase_  ,						lowerCAmelCase_  ,						index + 1  ,						lowerCAmelCase_   )
         current_sequence.pop()
         UpperCAmelCase							=     False
__a     =			[3, 1, 2, 4]
generate_all_permutations(sequence)

__a     =			["A", "B", "C"]







generate_all_permutations(sequence_a)



