
"""simple docstring"""

import argparse
import json
import subprocess




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ) ->      Optional[Any]:






				"""simple docstring"""




				UpperCAmelCase									=					[]

				UpperCAmelCase									=					(
				    F"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
				    ' https://api.github.com/repos/huggingface/transformers/actions/runners'
				)
				UpperCAmelCase									=					subprocess.run(lowerCAmelCase ,				shell=lowerCAmelCase ,				stdout=subprocess.PIPE )
				UpperCAmelCase									=					output.stdout.decode('utf-8' )
				UpperCAmelCase									=					json.loads(lowerCAmelCase )

				UpperCAmelCase									=					status['runners']
				for runner in runners:
								if runner["name"] in target_runners:
												if runner["status"] == "offline":
																offline_runners.append(lowerCAmelCase )

    # save the result so we can report them on Slack
				with open('offline_runners.txt' ,				'w' ) as fp:
								fp.write(json.dumps(lowerCAmelCase ) )

				if len(lowerCAmelCase ) > 0:
								UpperCAmelCase									=					'\n'.join([x['name'] for x in offline_runners] )
								raise ValueError(F"The following runners are offline:\n{failed}" )


if __name__ == "__main__":




			def     lowercase__						(					lowerCAmelCase				:					Dict ) ->      Tuple:






							"""simple docstring"""




							return values.split(',' )

			SCREAMING_SNAKE_CASE_    					=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--target_runners''',
			    default=None,
			    type=list_str,
			    required=True,
			    help='''Comma-separated list of runners to check status.''',
			)

			parser.add_argument(
			    '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
			)
			SCREAMING_SNAKE_CASE_    					=						parser.parse_args()







			get_runner_status(args.target_runners, args.token)





"""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((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




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

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







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          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     a_      (							self   ,       lowercase_							)						->					Tuple:
          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   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          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   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							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_							)



