"""simple docstring"""







import argparse
import json
import subprocess




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    []

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

					__SCREAMING_SNAKE_CASE						       =    status["runners"]
					for runner in runners:
										if runner["name"] in target_runners:
															if runner["status"] == "offline":
																				offline_runners.append(_a       )

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

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


if __name__ == "__main__":




		def        UpperCAmelCase__	(lowerCAmelCase_       ):




							'''simple docstring'''


							return values.split(","       )

		a__    :							int            =					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.'''
		)
		a__    :							str            =					parser.parse_args()



		get_runner_status(args.target_runners, args.token)




"""simple docstring"""







import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a__    :							Optional[Any]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  int    =      CLIPConfig

	snake_case__    :  str    =      ["CLIPEncoderLayer"]




	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    CLIPConfig       )	->     Dict:
						super().__init__(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    CLIPVisionModelWithProjection(config.vision_config       )

						__SCREAMING_SNAKE_CASE						       =    nn.Linear(config.vision_config.projection_dim      ,      1       )
						__SCREAMING_SNAKE_CASE						       =    nn.Linear(config.vision_config.projection_dim      ,      1       )




	@torch.no_grad()
	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int=0.5      ,      UpperCAmelCase__      :    Optional[int]=0.5       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.vision_model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    self.p_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected > p_threshold
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.tolist()

						if any(UpperCAmelCase__       ):
											logger.warning(
											    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
											    " Try again with a different prompt and/or seed."       )

						for idx, nsfw_detected_ in enumerate(UpperCAmelCase__       ):
											if nsfw_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						__SCREAMING_SNAKE_CASE						       =    self.w_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    watermark_detected > w_threshold
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.tolist()

						if any(UpperCAmelCase__       ):
											logger.warning(
											    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
											    " Try again with a different prompt and/or seed."       )

						for idx, watermark_detected_ in enumerate(UpperCAmelCase__       ):
											if watermark_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						return images, nsfw_detected, watermark_detected
