






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"transfo-xl"
			snake_case__  =				["mems"]
			snake_case__  =				{
			    "n_token": "vocab_size",
			    "hidden_size": "d_model",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=26_7735  ,						__SCREAMING_SNAKE_CASE      :			List[str]=[2_0000, 4_0000, 20_0000]  ,						__SCREAMING_SNAKE_CASE      :			Any=1024  ,						__SCREAMING_SNAKE_CASE      :			int=1024  ,						__SCREAMING_SNAKE_CASE      :			str=16  ,						__SCREAMING_SNAKE_CASE      :			Tuple=64  ,						__SCREAMING_SNAKE_CASE      :			Any=4096  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=4  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Any=18  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=1600  ,						__SCREAMING_SNAKE_CASE      :			Any=1000  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=True  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=0  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=-1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=0.1  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.0  ,						__SCREAMING_SNAKE_CASE      :			List[str]=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="normal"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.01  ,						__SCREAMING_SNAKE_CASE      :			Dict=0.01  ,						__SCREAMING_SNAKE_CASE      :			str=0.02  ,						__SCREAMING_SNAKE_CASE      :			Dict=1e-5  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0  ,						**__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						)			->			Any:
							a_		:  Union[str, Any]											=      vocab_size
							a_		:  Dict											=      []
							self.cutoffs.extend(__SCREAMING_SNAKE_CASE					)
							if proj_share_all_but_first:
											a_		:  Tuple											=      [False] + [True] * len(self.cutoffs					)
							else:
											a_		:  int											=      [False] + [False] * len(self.cutoffs					)
							a_		:  Dict											=      d_model
							a_		:  Tuple											=      d_embed
							a_		:  Union[str, Any]											=      d_head
							a_		:  Optional[int]											=      d_inner
							a_		:  Union[str, Any]											=      div_val
							a_		:  int											=      pre_lnorm
							a_		:  List[Any]											=      n_layer
							a_		:  List[Any]											=      n_head
							a_		:  Dict											=      mem_len
							a_		:  List[Any]											=      same_length
							a_		:  Dict											=      attn_type
							a_		:  Dict											=      clamp_len
							a_		:  Union[str, Any]											=      sample_softmax
							a_		:  Dict											=      adaptive
							a_		:  Optional[Any]											=      dropout
							a_		:  str											=      dropatt
							a_		:  Optional[Any]											=      untie_r
							a_		:  str											=      init
							a_		:  Optional[Any]											=      init_range
							a_		:  int											=      proj_init_std
							a_		:  Union[str, Any]											=      init_std
							a_		:  List[str]											=      layer_norm_epsilon
							super().__init__(eos_token_id=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			int:
							# Message copied from Transformer-XL documentation
							logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'					)
							return -1







			@max_position_embeddings.setter
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[Any]:
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    f'The model {self.model_type} is one of the few models that has no sequence length limit.'					)












'''simple docstring'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




