
from datetime import datetime as dt
import os

from github import Github


__lowerCAmelCase    :	List[str]						=						[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			UpperCAmelCase_     (  )       ->  Any:
    __lowercase						:	Union[str, Any]					=				Github(os.environ['''GITHUB_TOKEN''']		)
    __lowercase						:	Tuple					=				g.get_repo('''huggingface/transformers'''		)
    __lowercase						:	int					=				repo.get_issues(state='''open'''		)

    for issue in open_issues:
        __lowercase						:	Optional[Any]					=				sorted([comment for comment in issue.get_comments()]		,    key=lambda __lowerCAmelCase		: i.created_at		,    reverse=__UpperCamelCase		)
        __lowercase						:	Tuple					=				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 >= 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()







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





