"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_a   :	Any 					=     logging.get_logger(__name__)

_a   :	int 					=     {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[str]	      =	"gptj"
     _UpperCamelCase							:    Union[str, Any]	      =	{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }






     def __init__(	self       ,  a__=50400       ,  a__=2048       ,  a__=4096       ,  a__=28       ,  a__=16       ,  a__=64       ,  a__=None       ,  a__="gelu_new"       ,  a__=0.0       ,  a__=0.0       ,  a__=0.0       ,  a__=1e-5       ,  a__=0.0_2       ,  a__=True       ,  a__=50256       ,  a__=50256       ,  a__=False       ,  **a__       ,  ):
       _lowerCAmelCase		: Optional[int]						    =  vocab_size
       _lowerCAmelCase		: int						    =  n_positions
       _lowerCAmelCase		: List[Any]						    =  n_embd
       _lowerCAmelCase		: int						    =  n_layer
       _lowerCAmelCase		: Union[str, Any]						    =  n_head
       _lowerCAmelCase		: List[Any]						    =  n_inner
       _lowerCAmelCase		: Optional[Any]						    =  rotary_dim
       _lowerCAmelCase		: Optional[int]						    =  activation_function
       _lowerCAmelCase		: Union[str, Any]						    =  resid_pdrop
       _lowerCAmelCase		: List[str]						    =  embd_pdrop
       _lowerCAmelCase		: Optional[Any]						    =  attn_pdrop
       _lowerCAmelCase		: str						    =  layer_norm_epsilon
       _lowerCAmelCase		: str						    =  initializer_range
       _lowerCAmelCase		: Optional[int]						    =  use_cache

       _lowerCAmelCase		: int						    =  bos_token_id
       _lowerCAmelCase		: Dict						    =  eos_token_id

       super().__init__(
           bos_token_id=__A       ,  eos_token_id=__A       ,  tie_word_embeddings=__A       ,  **__A						)







class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__ = "default"       ,  a__ = None       ,  a__ = False       ,  ):
       super().__init__(__A       ,  task=__A       ,  patching_specs=__A       ,  use_past=__A						)
       if not getattr(self._config       ,  """pad_token_id"""       ,  __A						):
         # TODO: how to do that better?
         _lowerCAmelCase		: Dict						    =  0






     @property
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
       if self.use_past:
         self.fill_with_past_key_values_(__A       ,  direction="""inputs"""						)
         _lowerCAmelCase		: Optional[Any]						    =  {0: """batch""", 1: """past_sequence + sequence"""}
       else:
         _lowerCAmelCase		: Any						    =  {0: """batch""", 1: """sequence"""}

       return common_inputs






     @property
     def 					__A						(	self						):
       return self._config.n_layer






     @property
     def 					__A						(	self						):
       return self._config.n_head






     def 					__A						(	self       ,  a__       ,  a__ = -1       ,  a__ = -1       ,  a__ = False       ,  a__ = None       ,  ):
       _lowerCAmelCase		: Tuple						    =  super(__A       ,  self						).generate_dummy_inputs(
           __A       ,  batch_size=__A       ,  seq_length=__A       ,  is_pair=__A       ,  framework=__A						)

       # We need to order the input in the way they appears in the forward()
       _lowerCAmelCase		: List[str]						    =  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}						)

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
         else:
           import torch

           _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           _lowerCAmelCase		: Any						    =  seqlen + 2
           _lowerCAmelCase		: Union[str, Any]						    =  (
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           _lowerCAmelCase		: List[str]						    =  [
               (torch.zeros(__A						), torch.zeros(__A						)) for _ in range(self.num_layers						)
           ]

       _lowerCAmelCase		: str						    =  common_inputs["""attention_mask"""]
       if self.use_past:
         _lowerCAmelCase		: int						    =  ordered_inputs["""attention_mask"""].dtype
         _lowerCAmelCase		: Union[str, Any]						    =  torch.cat(
             [ordered_inputs["""attention_mask"""], torch.ones(__A       ,  __A       ,  dtype=__A						)]       ,  dim=1						)

       return ordered_inputs






     @property
     def 					__A						(	self						):
       return 13


"""simple docstring"""




import argparse
import json
import subprocess





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: Tuple						    =  []

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

  _lowerCAmelCase		: int						    =  status["""runners"""]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase			)

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

  if len(_lowerCamelCase			) > 0:
    _lowerCAmelCase		: int						    =  """\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  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
       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   :	Tuple 					=     parser.parse_args()






     get_runner_status(args.target_runners, args.token)




