


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase        =     logging.get_logger(__name__)


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):
       lowerCamelCase__:     Optional[Any]							    =     """maskformer-swin"""

       lowerCamelCase__:     List[Any]							    =     {
           """num_attention_heads""": """num_heads""",
           """num_hidden_layers""": """num_layers""",
       }

       def __init__(   self      :     List[Any]      ,							lowerCamelCase_      :     List[Any]=2_2_4      ,							lowerCamelCase_      :     Optional[int]=4      ,							lowerCamelCase_      :     List[Any]=3      ,							lowerCamelCase_      :     Optional[Any]=9_6      ,							lowerCamelCase_      :     List[str]=[2, 2, 6, 2]      ,							lowerCamelCase_      :     str=[3, 6, 1_2, 2_4]      ,							lowerCamelCase_      :     str=7      ,							lowerCamelCase_      :     List[Any]=4.0      ,							lowerCamelCase_      :     Optional[Any]=True      ,							lowerCamelCase_      :     Any=0.0      ,							lowerCamelCase_      :     Dict=0.0      ,							lowerCamelCase_      :     Optional[Any]=0.1      ,							lowerCamelCase_      :     Optional[Any]="gelu"      ,							lowerCamelCase_      :     str=False      ,							lowerCamelCase_      :     List[str]=0.0_2      ,							lowerCamelCase_      :     str=1E-5      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Optional[int]=None      ,							**lowerCamelCase_      :     Dict      ,							):
            super().__init__(**lowerCamelCase_    )

            a_    :     Any										=    image_size
            a_    :     Tuple										=    patch_size
            a_    :     Union[str, Any]										=    num_channels
            a_    :     Optional[Any]										=    embed_dim
            a_    :     int										=    depths
            a_    :     Any										=    len(lowerCamelCase_    )
            a_    :     List[Any]										=    num_heads
            a_    :     Dict										=    window_size
            a_    :     Optional[int]										=    mlp_ratio
            a_    :     Any										=    qkv_bias
            a_    :     Optional[Any]										=    hidden_dropout_prob
            a_    :     int										=    attention_probs_dropout_prob
            a_    :     List[Any]										=    drop_path_rate
            a_    :     Tuple										=    hidden_act
            a_    :     str										=    use_absolute_embeddings
            a_    :     Any										=    layer_norm_eps
            a_    :     str										=    initializer_range
            # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            a_    :     int										=    int(embed_dim * 2 ** (len(lowerCamelCase_    ) - 1)    )
            a_    :     str										=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,							len(lowerCamelCase_    ) + 1    )]
            a_    :     Any										=    get_aligned_output_features_output_indices(
                out_features=lowerCamelCase_      ,							out_indices=lowerCamelCase_      ,							stage_names=self.stage_names    )







from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 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 >= 3_0
              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 > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              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()



