



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}







class 		lowercase							(     UpperCamelCase_	):
     _a											=		"deberta-v2"

     def __init__(						self	,  _a=12_8100	,  _a=1536	,  _a=24	,  _a=24	,  _a=6144	,  _a="gelu"	,  _a=0.1	,  _a=0.1	,  _a=512	,  _a=0	,  _a=0.02	,  _a=1e-7	,  _a=False	,  _a=-1	,  _a=0	,  _a=True	,  _a=None	,  _a=0	,  _a="gelu"	,  **_a	,  )		->							List[str]:
           super().__init__(**__A       )

           _A							:  Optional[int]    =		hidden_size
           _A							:  Union[str, Any]    =		num_hidden_layers
           _A							:  Optional[int]    =		num_attention_heads
           _A							:  Tuple    =		intermediate_size
           _A							:  Union[str, Any]    =		hidden_act
           _A							:  Any    =		hidden_dropout_prob
           _A							:  Tuple    =		attention_probs_dropout_prob
           _A							:  Tuple    =		max_position_embeddings
           _A							:  List[Any]    =		type_vocab_size
           _A							:  int    =		initializer_range
           _A							:  List[Any]    =		relative_attention
           _A							:  Dict    =		max_relative_positions
           _A							:  Any    =		pad_token_id
           _A							:  Any    =		position_biased_input

           # Backwards compatibility
           if type(__A       ) == str:
                 _A							:  List[Any]    =		[x.strip() for x in pos_att_type.lower().split("""|"""       )]

           _A							:  List[str]    =		pos_att_type
           _A							:  Tuple    =		vocab_size
           _A							:  List[str]    =		layer_norm_eps

           _A							:  List[str]    =		kwargs.get("""pooler_hidden_size"""	,  __A       )
           _A							:  Tuple    =		pooler_dropout
           _A							:  str    =		pooler_hidden_act







class 		lowercase							(     UpperCamelCase_	):

     @property
     def       a__			(						self       )		->							Optional[Any]:
           if self.task == "multiple-choice":
                 _A							:  int    =		{0: "batch", 1: "choice", 2: "sequence"}
           else:
                 _A							:  List[Any]    =		{0: "batch", 1: "sequence"}
           if self._config.type_vocab_size > 0:
                 return OrderedDict(
                     [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]       )
           else:
                 return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]       )

     @property
     def       a__			(						self       )		->							List[Any]:
           return 12



     def       a__			(						self	,  _a	,  _a = -1	,  _a = -1	,  _a = -1	,  _a = False	,  _a = None	,  _a = 3	,  _a = 40	,  _a = 40	,  _a = None	,  )		->							str:
           _A							:  int    =		super().generate_dummy_inputs(preprocessor=__A	,  framework=__A       )
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                 del dummy_inputs["token_type_ids"]
           return dummy_inputs







from datetime import datetime as dt
import os

from github import Github


_snake_case            = [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def        lowerCAmelCase_			(  ):
      _A							:  int    =		Github(os.environ["""GITHUB_TOKEN"""]  )
      _A							:  Tuple    =		g.get_repo("""huggingface/transformers"""  )
      _A							:  Dict    =		repo.get_issues(state="""open"""  )

      for issue in open_issues:
            _A							:  Optional[Any]    =		sorted([comment for comment in issue.get_comments()],key=lambda snake_case_  : i.created_at,reverse=snake_case_  )
            _A							:  Dict    =		comments[0] if len(snake_case_  ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""  )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""  )


if __name__ == "__main__":
  main()

