




import argparse
import json
import subprocess







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

       A__ 					=       []

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

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

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

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


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						Any							=  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."""
  )
  _lowerCamelCase      :						Optional[Any]							=  parser.parse_args()
  get_runner_status(args.target_runners, args.token)






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

from ...utils import logging


snake_case__				:	int					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							CLIPConfig

       _a   							=							["CLIPEncoderLayer"]



       def __init__(						self				:				Tuple		,				__a				:				CLIPConfig							)					->Union[str, Any]:
          super().__init__(__a							)

          lowerCamelCase_						:   List[Any]		= CLIPVisionModelWithProjection(config.vision_config							)

          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)
          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)




       @torch.no_grad()
       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				List[Any]		,				__a				:				Tuple		,				__a				:				int=0.5		,				__a				:				Optional[Any]=0.5							)					->Union[str, Any]:
          lowerCamelCase_						:   Dict		= self.vision_model(__a							)[0]

          lowerCamelCase_						:   str		= self.p_head(__a							)
          lowerCamelCase_						:   Union[str, Any]		= nsfw_detected.flatten()
          lowerCamelCase_						:   Tuple		= nsfw_detected > p_threshold
          lowerCamelCase_						:   Dict		= nsfw_detected.tolist()

          if any(__a							):
             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(__a							):
             if nsfw_detected_:
                lowerCamelCase_						:   int		= np.zeros(images[idx].shape							)

          lowerCamelCase_						:   Union[str, Any]		= self.w_head(__a							)
          lowerCamelCase_						:   List[str]		= watermark_detected.flatten()
          lowerCamelCase_						:   Dict		= watermark_detected > w_threshold
          lowerCamelCase_						:   Optional[int]		= watermark_detected.tolist()

          if any(__a							):
             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(__a							):
             if watermark_detected_:
                lowerCamelCase_						:   Tuple		= np.zeros(images[idx].shape							)

          return images, nsfw_detected, watermark_detected





