
"""simple docstring"""



import argparse
import json
import subprocess
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					[]

    UpperCAmelCase__	:      Tuple     							=					(
        F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
        ' https://api.github.com/repos/huggingface/transformers/actions/runners'
    )
    UpperCAmelCase__	:      List[Any]     							=					subprocess.run(_snake_case    ,shell=_snake_case    ,stdout=subprocess.PIPE )
    UpperCAmelCase__	:      int     							=					output.stdout.decode('utf-8' )
    UpperCAmelCase__	:      int     							=					json.loads(_snake_case )

    UpperCAmelCase__	:      Optional[int]     							=					status['runners']
    for runner in runners:
        if runner["name"] in target_runners:
            if runner["status"] == "offline":
                offline_runners.append(_snake_case )

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

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


if __name__ == "__main__":
    def 					lowerCamelCase   (     _snake_case ):
        return values.split(',' )

    UpperCamelCase__          =	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.'
    )
    UpperCamelCase__          =	parser.parse_args()

    get_runner_status(args.target_runners, args.token)



"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

