


'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				["image_processor", "tokenizer"]
							lowercase__      :       Tuple     						=				"BlipImageProcessor"
							lowercase__      :       List[Any]     						=				"AutoTokenizer"







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Dict:
														lowerCAmelCase__	       =     False
														super().__init__(lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     self.image_processor







							def __call__(		self ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = False ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							BatchEncoding:
														if images is None and text is None:
																					raise ValueError('''You have to specify either images or text.'''       )

														# Get only text
														if images is None:
																					lowerCAmelCase__	       =     self.tokenizer
																					lowerCAmelCase__	       =     self.tokenizer(
																					    text=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   padding=lowerCamelCase_ ,   truncation=lowerCamelCase_ ,   max_length=lowerCamelCase_ ,   stride=lowerCamelCase_ ,   pad_to_multiple_of=lowerCamelCase_ ,   return_attention_mask=lowerCamelCase_ ,   return_overflowing_tokens=lowerCamelCase_ ,   return_special_tokens_mask=lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   return_token_type_ids=lowerCamelCase_ ,   return_length=lowerCamelCase_ ,   verbose=lowerCamelCase_ ,   return_tensors=lowerCamelCase_ ,   **lowerCamelCase_ ,   )
																					return text_encoding

														# add pixel_values
														lowerCAmelCase__	       =     self.image_processor(lowerCamelCase_ ,   return_tensors=lowerCamelCase_       )

														if text is not None:
																					lowerCAmelCase__	       =     self.tokenizer(
																					    text=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   padding=lowerCamelCase_ ,   truncation=lowerCamelCase_ ,   max_length=lowerCamelCase_ ,   stride=lowerCamelCase_ ,   pad_to_multiple_of=lowerCamelCase_ ,   return_attention_mask=lowerCamelCase_ ,   return_overflowing_tokens=lowerCamelCase_ ,   return_special_tokens_mask=lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   return_token_type_ids=lowerCamelCase_ ,   return_length=lowerCamelCase_ ,   verbose=lowerCamelCase_ ,   return_tensors=lowerCamelCase_ ,   **lowerCamelCase_ ,   )
														else:
																					lowerCAmelCase__	       =     None

														if text_encoding is not None:
																					encoding_image_processor.update(lowerCamelCase_       )

														return encoding_image_processor







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_ ,   **lowerCamelCase_       )	->							Any:
														return self.tokenizer.batch_decode(*lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_ ,   **lowerCamelCase_       )	->							Any:
														return self.tokenizer.decode(*lowerCamelCase_ ,   **lowerCamelCase_       )







							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     self.tokenizer.model_input_names
														lowerCAmelCase__	       =     self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



'''simple docstring'''






import argparse
import json
import subprocess


def  _snake_case       (					A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     []

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

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


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   Optional[Any]:
									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)