


import argparse
import json
import subprocess
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Optional[Any]										=	[]

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

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

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

      if len(UpperCAmelCase_						) > 0:
            a						: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						(			UpperCAmelCase_						:		int						):




           """simple docstring"""





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

     snake_case    :   Dict	    =					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.'''
     )
     snake_case    :   int	    =					parser.parse_args()






     get_runner_status(args.target_runners, args.token)









from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

snake_case    :   str	    =					logging.get_logger(__name__)







@add_end_docstrings(_snake_case							)
class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       super().__init__(*_lowerCamelCase ,  **_lowerCamelCase   )
       requires_backends(self ,  '''vision'''   )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None   ):
       a						:Tuple										=	{}
       if top_k is not None:
             a						:int										=	top_k
       return {}, {}, postprocess_params




 def __call__(		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       return super().__call__(_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	load_image(_lowerCamelCase   )
       a						:Any										=	self.image_processor(images=_lowerCamelCase ,  return_tensors=self.framework   )
       return model_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.model(**_lowerCamelCase   )
       return model_outputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=5   ):
       if top_k > self.model.config.num_labels:
             a						:List[Any]										=	self.model.config.num_labels

       if self.framework == "pt":
             a						:int										=	model_outputs.logits.softmax(-1   )[0]
             a							,    a						:Union[str, Any]										=	probs.topk(_lowerCamelCase   )
       elif self.framework == "tf":
             a						:Optional[Any]										=	stable_softmax(model_outputs.logits ,  axis=-1   )[0]
             a						:Union[str, Any]										=	tf.math.top_k(_lowerCamelCase ,  k=_lowerCamelCase   )
             a							,    a						:Optional[int]										=	topk.values.numpy(), topk.indices.numpy()
       else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''   )

       a						:Optional[int]										=	scores.tolist()
       a						:str										=	ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase ,  _lowerCamelCase   )]





