





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


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

_lowerCamelCase :    int  							= {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 						lowerCamelCase       (SCREAMING_SNAKE_CASE__      ):







       """simple docstring"""




       UpperCAmelCase_      						=   "dpt"






       def __init__( self     :					Any,			_UpperCAmelCase     :					int=7_6_8,			_UpperCAmelCase     :					Union[str, Any]=1_2,			_UpperCAmelCase     :					Union[str, Any]=1_2,			_UpperCAmelCase     :					Union[str, Any]=3_0_7_2,			_UpperCAmelCase     :					int="gelu",			_UpperCAmelCase     :					Optional[Any]=0.0,			_UpperCAmelCase     :					Tuple=0.0,			_UpperCAmelCase     :					List[str]=0.02,			_UpperCAmelCase     :					List[str]=1E-12,			_UpperCAmelCase     :					Union[str, Any]=3_8_4,			_UpperCAmelCase     :					List[str]=1_6,			_UpperCAmelCase     :					Tuple=3,			_UpperCAmelCase     :					Union[str, Any]=False,			_UpperCAmelCase     :					Dict=True,			_UpperCAmelCase     :					Optional[int]=[2, 5, 8, 1_1],			_UpperCAmelCase     :					Any="project",			_UpperCAmelCase     :					List[str]=[4, 2, 1, 0.5],			_UpperCAmelCase     :					Tuple=[9_6, 1_9_2, 3_8_4, 7_6_8],			_UpperCAmelCase     :					Union[str, Any]=2_5_6,			_UpperCAmelCase     :					List[Any]=-1,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					Union[str, Any]=0.4,			_UpperCAmelCase     :					Any=2_5_5,			_UpperCAmelCase     :					Union[str, Any]=0.1,			_UpperCAmelCase     :					Optional[int]=[1, 1_0_2_4, 2_4, 2_4],			_UpperCAmelCase     :					List[Any]=[0, 1],			_UpperCAmelCase     :					Tuple=None,			**_UpperCAmelCase     :					Union[str, Any],			)				->			Any:





              """simple docstring"""
              super().__init__(**_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						is_hybrid

              if self.is_hybrid:
                     if backbone_config is None:
                            logger.info("Initializing the config with a `BiT` backbone."						)
                            SCREAMING_SNAKE_CASE__       :      List[Any]     =						{
                                'global_padding': 'same',
                                'layer_type': 'bottleneck',
                                'depths': [3, 4, 9],
                                'out_features': ['stage1', 'stage2', 'stage3'],
                                'embedding_dynamic_padding': True,
                            }
                            SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						BitConfig(**_UpperCAmelCase						)
                     elif isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
                            logger.info("Initializing the config with a `BiT` backbone."						)
                            SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BitConfig(**_UpperCAmelCase						)
                     elif isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
                            SCREAMING_SNAKE_CASE__       :      Dict     =						backbone_config
                     else:
                            raise ValueError(
                                F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						backbone_featmap_shape
                     SCREAMING_SNAKE_CASE__       :      Any     =						neck_ignore_stages

                     if readout_type != "project":
                            raise ValueError("Readout type must be \'project\' when using `DPT-hybrid` mode."						)
              else:
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						None
                     SCREAMING_SNAKE_CASE__       :      int     =						None
                     SCREAMING_SNAKE_CASE__       :      Any     =						[]

              SCREAMING_SNAKE_CASE__       :      List[str]     =						num_hidden_layers
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						num_attention_heads
              SCREAMING_SNAKE_CASE__       :      int     =						intermediate_size
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						hidden_act
              SCREAMING_SNAKE_CASE__       :      Dict     =						hidden_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						initializer_range
              SCREAMING_SNAKE_CASE__       :      int     =						layer_norm_eps
              SCREAMING_SNAKE_CASE__       :      str     =						image_size
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
              SCREAMING_SNAKE_CASE__       :      str     =						num_channels
              SCREAMING_SNAKE_CASE__       :      int     =						qkv_bias
              SCREAMING_SNAKE_CASE__       :      str     =						backbone_out_indices
              if readout_type not in ["ignore", "add", "project"]:
                     raise ValueError("Readout_type must be one of [\'ignore\', \'add\', \'project\']"						)
              SCREAMING_SNAKE_CASE__       :      List[str]     =						readout_type
              SCREAMING_SNAKE_CASE__       :      List[str]     =						reassemble_factors
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						neck_hidden_sizes
              SCREAMING_SNAKE_CASE__       :      Dict     =						fusion_hidden_size
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						head_in_index
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						use_batch_norm_in_fusion_residual
              # auxiliary head attributes (semantic segmentation)
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						use_auxiliary_head
              SCREAMING_SNAKE_CASE__       :      List[str]     =						auxiliary_loss_weight
              SCREAMING_SNAKE_CASE__       :      Dict     =						semantic_loss_ignore_index
              SCREAMING_SNAKE_CASE__       :      int     =						semantic_classifier_dropout






       def  A_     ( self     :					Optional[int]						)				->			List[str]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						copy.deepcopy(self.__dict__						)

              if output["backbone_config"] is not None:
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.backbone_config.to_dict()

              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.__class__.model_type
              return output













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






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


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






