





import copy

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


_lowerCamelCase :    str  							= logging.get_logger(__name__)

_lowerCamelCase :    Tuple  							= {
    '''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       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "dpt"






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





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

														SCREAMING_SNAKE_CASE__       :      Any     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      str     =						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__       :      List[str]     =						BitConfig(**_UpperCAmelCase						)
																					elif isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												logger.info("Initializing the config with a `BiT` backbone."						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						BitConfig(**_UpperCAmelCase						)
																					elif isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						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__       :      str     =						neck_ignore_stages

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

														SCREAMING_SNAKE_CASE__       :      Dict     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      Tuple     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      int     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      int     =						layer_norm_eps
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						image_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						patch_size
														SCREAMING_SNAKE_CASE__       :      int     =						num_channels
														SCREAMING_SNAKE_CASE__       :      Any     =						qkv_bias
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      int     =						readout_type
														SCREAMING_SNAKE_CASE__       :      List[str]     =						reassemble_factors
														SCREAMING_SNAKE_CASE__       :      str     =						neck_hidden_sizes
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						fusion_hidden_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						head_in_index
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						use_batch_norm_in_fusion_residual
														# auxiliary head attributes (semantic segmentation)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						use_auxiliary_head
														SCREAMING_SNAKE_CASE__       :      str     =						auxiliary_loss_weight
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						semantic_loss_ignore_index
														SCREAMING_SNAKE_CASE__       :      Dict     =						semantic_classifier_dropout






							def  A_     ( self     :					Union[str, Any]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						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__       :      Optional[Any]     =						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))






