


'''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


SCREAMING_SNAKE_CASE_      =			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Optional[Any]:
     """simple docstring"""




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

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



class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       super().__init__()

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

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

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''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 __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       __a       =   min(int(num_inference_steps * strength						)				,							SCREAMING_SNAKE_CASE__						)

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

       return timesteps, num_inference_steps - t_start

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None						):





       '''simple docstring'''

       if not isinstance(SCREAMING_SNAKE_CASE__				,							(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(SCREAMING_SNAKE_CASE__						)}'''						)

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

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

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

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

       return latents


  @torch.no_grad()
  def __call__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[torch.FloatTensor, PIL.Image.Image] = None				,							SCREAMING_SNAKE_CASE__     :							float = 0.8				,							SCREAMING_SNAKE_CASE__     :							int = 1				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE__     :							float = 0.0				,							SCREAMING_SNAKE_CASE__     :							int = 5_0				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       self.check_inputs(SCREAMING_SNAKE_CASE__						)

       # 2. Preprocess image
       __a       =   preprocess(SCREAMING_SNAKE_CASE__						)

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

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

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

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

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

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







from __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE_      =			'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE_      =			BASE_URL + '/user'

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE_      =			os.environ.get('USER_TOKEN', '')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					dict[Any, Any]:
     """simple docstring"""




     __a       =   {
         """Authorization""": F'''token {auth_token}''',
         """Accept""": """application/vnd.github.v3+json""",
     }
     return requests.get(__SCREAMING_SNAKE_CASE				,  headers=__SCREAMING_SNAKE_CASE		).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(f"""{key}: {value}""")
 else:
  raise ValueError('\'USER_TOKEN\' field cannot be empty.')






