





'''simple docstring'''




import copy

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


__SCREAMING_SNAKE_CASE     :List[Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Dict       =				{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : str           =							"""deta"""
 _lowerCamelCase  : List[str]           =							{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Dict=None	,			snake_case_       :    int=9_0_0	,			snake_case_       :    Tuple=2_0_4_8	,			snake_case_       :    Union[str, Any]=6	,			snake_case_       :    Tuple=2_0_4_8	,			snake_case_       :    int=8	,			snake_case_       :    str=6	,			snake_case_       :    int=1_0_2_4	,			snake_case_       :    str=8	,			snake_case_       :    Tuple=0.0	,			snake_case_       :    int=True	,			snake_case_       :    str="relu"	,			snake_case_       :    Optional[int]=2_5_6	,			snake_case_       :    int=0.1	,			snake_case_       :    int=0.0	,			snake_case_       :    Dict=0.0	,			snake_case_       :    List[Any]=0.0_2	,			snake_case_       :    Optional[int]=1.0	,			snake_case_       :    Tuple=True	,			snake_case_       :    Dict=False	,			snake_case_       :    Any="sine"	,			snake_case_       :    int=5	,			snake_case_       :    Union[str, Any]=4	,			snake_case_       :    List[Any]=4	,			snake_case_       :    Optional[int]=True	,			snake_case_       :    Optional[Any]=3_0_0	,			snake_case_       :    str=True	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    Dict=1	,			snake_case_       :    Optional[int]=5	,			snake_case_       :    str=2	,			snake_case_       :    int=1	,			snake_case_       :    Union[str, Any]=1	,			snake_case_       :    Dict=5	,			snake_case_       :    Union[str, Any]=2	,			snake_case_       :    Optional[Any]=0.1	,			snake_case_       :    Optional[int]=0.2_5	,			**snake_case_       :    Optional[Any]	,			):
      if backbone_config is None:
           logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."    )
           _UpperCAmelCase     							=   CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]    )
      else:
           if isinstance(snake_case_	,			snake_case_    ):
                _UpperCAmelCase     							=   backbone_config.pop("model_type"    )
                _UpperCAmelCase     							=   CONFIG_MAPPING[backbone_model_type]
                _UpperCAmelCase     							=   config_class.from_dict(snake_case_    )

      _UpperCAmelCase     							=   backbone_config
      _UpperCAmelCase     							=   num_queries
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   encoder_ffn_dim
      _UpperCAmelCase     							=   encoder_layers
      _UpperCAmelCase     							=   encoder_attention_heads
      _UpperCAmelCase     							=   decoder_ffn_dim
      _UpperCAmelCase     							=   decoder_layers
      _UpperCAmelCase     							=   decoder_attention_heads
      _UpperCAmelCase     							=   dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   activation_function
      _UpperCAmelCase     							=   init_std
      _UpperCAmelCase     							=   init_xavier_std
      _UpperCAmelCase     							=   encoder_layerdrop
      _UpperCAmelCase     							=   auxiliary_loss
      _UpperCAmelCase     							=   position_embedding_type
      # deformable attributes
      _UpperCAmelCase     							=   num_feature_levels
      _UpperCAmelCase     							=   encoder_n_points
      _UpperCAmelCase     							=   decoder_n_points
      _UpperCAmelCase     							=   two_stage
      _UpperCAmelCase     							=   two_stage_num_proposals
      _UpperCAmelCase     							=   with_box_refine
      _UpperCAmelCase     							=   assign_first_stage
      if two_stage is True and with_box_refine is False:
           raise ValueError("If two_stage is True, with_box_refine must be True."    )
      # Hungarian matcher
      _UpperCAmelCase     							=   class_cost
      _UpperCAmelCase     							=   bbox_cost
      _UpperCAmelCase     							=   giou_cost
      # Loss coefficients
      _UpperCAmelCase     							=   mask_loss_coefficient
      _UpperCAmelCase     							=   dice_loss_coefficient
      _UpperCAmelCase     							=   bbox_loss_coefficient
      _UpperCAmelCase     							=   giou_loss_coefficient
      _UpperCAmelCase     							=   eos_coefficient
      _UpperCAmelCase     							=   focal_alpha
      super().__init__(is_encoder_decoder=snake_case_	,			**snake_case_    )




 @property
 def      lowercase	(					self       :    List[Any]    ):
      return self.encoder_attention_heads




 @property
 def      lowercase	(					self       :    Dict    ):
      return self.d_model




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   copy.deepcopy(self.__dict__    )
      _UpperCAmelCase     							=   self.backbone_config.to_dict()
      _UpperCAmelCase     							=   self.__class__.model_type
      return output






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




