





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE									=				{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				["FunnelTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FunnelBaseModel",
           "FunnelForMaskedLM",
           "FunnelForMultipleChoice",
           "FunnelForPreTraining",
           "FunnelForQuestionAnswering",
           "FunnelForSequenceClassification",
           "FunnelForTokenClassification",
           "FunnelModel",
           "FunnelPreTrainedModel",
           "load_tf_weights_in_funnel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFFunnelBaseModel",
           "TFFunnelForMaskedLM",
           "TFFunnelForMultipleChoice",
           "TFFunnelForPreTraining",
           "TFFunnelForQuestionAnswering",
           "TFFunnelForSequenceClassification",
           "TFFunnelForTokenClassification",
           "TFFunnelModel",
           "TFFunnelPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
       from .tokenization_funnel import FunnelTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_funnel_fast import FunnelTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_funnel import (
                  FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FunnelBaseModel,
                  FunnelForMaskedLM,
                  FunnelForMultipleChoice,
                  FunnelForPreTraining,
                  FunnelForQuestionAnswering,
                  FunnelForSequenceClassification,
                  FunnelForTokenClassification,
                  FunnelModel,
                  FunnelPreTrainedModel,
                  load_tf_weights_in_funnel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_funnel import (
                  TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFunnelBaseModel,
                  TFFunnelForMaskedLM,
                  TFFunnelForMultipleChoice,
                  TFFunnelForPreTraining,
                  TFFunnelForQuestionAnswering,
                  TFFunnelForSequenceClassification,
                  TFFunnelForTokenClassification,
                  TFFunnelModel,
                  TFFunnelPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''






from datetime import datetime as dt
import os

from github import Github


A__							:  str    =[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				Github(os.environ["""GITHUB_TOKEN"""]    )
      _lowerCAmelCase					=				g.get_repo("""huggingface/transformers"""    )
      _lowerCAmelCase					=				repo.get_issues(state="""open"""    )

      for issue in open_issues:
            _lowerCAmelCase					=				sorted([comment for comment in issue.get_comments()]				,      key=lambda lowerCAmelCase    : i.created_at				,      reverse=lowerCAmelCase    )
            _lowerCAmelCase					=				comments[0] if len(lowerCAmelCase    ) > 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()






