






'''simple docstring'''







from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]






			@register_to_config
			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			int = 5_0257  ,						__SCREAMING_SNAKE_CASE      :			int = 1024  ,						__SCREAMING_SNAKE_CASE      :			int = 768  ,						__SCREAMING_SNAKE_CASE      :			int = 12  ,						__SCREAMING_SNAKE_CASE      :			int = 12  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			str = "gelu_new"  ,						__SCREAMING_SNAKE_CASE      :			float = 0.1  ,						__SCREAMING_SNAKE_CASE      :			float = 0.1  ,						__SCREAMING_SNAKE_CASE      :			float = 0.1  ,						__SCREAMING_SNAKE_CASE      :			float = 1e-5  ,						__SCREAMING_SNAKE_CASE      :			float = 0.02  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						)			->			List[Any]:
							super().__init__()

							a_		:  Optional[Any]											=      prefix_length

							if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
											raise ValueError(
											    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
											    f' `n_embd`: {n_embd} are not equal.'					)

							a_		:  str											=      prefix_inner_dim
							a_		:  Optional[Any]											=      prefix_hidden_dim

							a_		:  List[str]											=      (
							    nn.Linear(self.prefix_inner_dim  ,						self.prefix_hidden_dim					)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							a_		:  List[Any]											=      (
							    nn.Linear(self.prefix_hidden_dim  ,						__SCREAMING_SNAKE_CASE					) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							a_		:  Union[str, Any]											=      GPTaConfig(
							    vocab_size=__SCREAMING_SNAKE_CASE  ,						n_positions=__SCREAMING_SNAKE_CASE  ,						n_embd=__SCREAMING_SNAKE_CASE  ,						n_layer=__SCREAMING_SNAKE_CASE  ,						n_head=__SCREAMING_SNAKE_CASE  ,						n_inner=__SCREAMING_SNAKE_CASE  ,						activation_function=__SCREAMING_SNAKE_CASE  ,						resid_pdrop=__SCREAMING_SNAKE_CASE  ,						embd_pdrop=__SCREAMING_SNAKE_CASE  ,						attn_pdrop=__SCREAMING_SNAKE_CASE  ,						layer_norm_epsilon=__SCREAMING_SNAKE_CASE  ,						initializer_range=__SCREAMING_SNAKE_CASE  ,						scale_attn_weights=__SCREAMING_SNAKE_CASE  ,						use_cache=__SCREAMING_SNAKE_CASE  ,						scale_attn_by_inverse_layer_idx=__SCREAMING_SNAKE_CASE  ,						reorder_and_upcast_attn=__SCREAMING_SNAKE_CASE  ,						)
							a_		:  Tuple											=      GPTaLMHeadModel(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			torch.Tensor  ,						__SCREAMING_SNAKE_CASE      :			torch.Tensor  ,						__SCREAMING_SNAKE_CASE      :			Optional[torch.Tensor] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[torch.Tensor] = None  ,						)			->			List[Any]:
							a_		:  Any											=      self.transformer.transformer.wte(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      self.encode_prefix(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      self.decode_prefix(__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      torch.cat((prefix_embeds, embedding_text)  ,						dim=1					)

							if labels is not None:
											a_		:  List[str]											=      self.get_dummy_token(input_ids.shape[0]  ,						input_ids.device					)
											a_		:  int											=      torch.cat((dummy_token, input_ids)  ,						dim=1					)
							a_		:  Dict											=      self.transformer(inputs_embeds=__SCREAMING_SNAKE_CASE  ,						labels=__SCREAMING_SNAKE_CASE  ,						attention_mask=__SCREAMING_SNAKE_CASE					)
							if self.prefix_hidden_dim is not None:
											return out, hidden
							else:
											return out






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			torch.device					)			->			torch.Tensor:
							return torch.zeros(__SCREAMING_SNAKE_CASE  ,						self.prefix_length  ,						dtype=torch.intaa  ,						device=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Any:
							return self.encode_prefix(__SCREAMING_SNAKE_CASE					)






			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			Dict:
							a_		:  Dict											=      torch.split(__SCREAMING_SNAKE_CASE  ,						1  ,						dim=0					)
							a_		:  str											=      []
							a_		:  Optional[int]											=      []
							for feature in features:
											a_		:  str											=      self.decode_prefix(feature.to(__SCREAMING_SNAKE_CASE					)					)  # back to the clip feature
											# Only support beam search for now
											a_				,			a_		:  List[Any]											=      self.generate_beam(
											    input_embeds=__SCREAMING_SNAKE_CASE  ,						device=__SCREAMING_SNAKE_CASE  ,						eos_token_id=__SCREAMING_SNAKE_CASE					)
											generated_tokens.append(output_tokens[0]					)
											generated_seq_lengths.append(seq_lengths[0]					)
							a_		:  Dict											=      torch.stack(__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      torch.stack(__SCREAMING_SNAKE_CASE					)
							return generated_tokens, generated_seq_lengths







			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			str=None  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			int = 5  ,						__SCREAMING_SNAKE_CASE      :			int = 67  ,						__SCREAMING_SNAKE_CASE      :			float = 1.0  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						)			->			Dict:
							a_		:  int											=      eos_token_id
							a_		:  Dict											=      None
							a_		:  Dict											=      None
							a_		:  List[Any]											=      torch.ones(__SCREAMING_SNAKE_CASE  ,						device=__SCREAMING_SNAKE_CASE  ,						dtype=torch.int					)
							a_		:  Any											=      torch.zeros(__SCREAMING_SNAKE_CASE  ,						device=__SCREAMING_SNAKE_CASE  ,						dtype=torch.bool					)

							if input_embeds is not None:
											a_		:  Tuple											=      input_embeds
							else:
											a_		:  Dict											=      self.transformer.transformer.wte(__SCREAMING_SNAKE_CASE					)

							for i in range(__SCREAMING_SNAKE_CASE					):
											a_		:  Tuple											=      self.transformer(inputs_embeds=__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      outputs.logits
											a_		:  Union[str, Any]											=      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
											a_		:  Optional[Any]											=      logits.softmax(-1					).log()

											if scores is None:
															a_				,			a_		:  Optional[Any]											=      logits.topk(__SCREAMING_SNAKE_CASE  ,						-1					)
															a_		:  Dict											=      generated.expand(__SCREAMING_SNAKE_CASE  ,						*generated.shape[1:]					)
															a_				,			a_		:  List[str]											=      next_tokens.permute(1  ,						0					), scores.squeeze(0					)
															if tokens is None:
																			a_		:  Optional[int]											=      next_tokens
															else:
																			a_		:  int											=      tokens.expand(__SCREAMING_SNAKE_CASE  ,						*tokens.shape[1:]					)
																			a_		:  Tuple											=      torch.cat((tokens, next_tokens)  ,						dim=1					)
											else:
															a_		:  List[Any]											=      -float(np.inf					)
															a_		:  Tuple											=      0
															a_		:  List[Any]											=      scores[:, None] + logits
															seq_lengths[~is_stopped] += 1
															a_		:  str											=      scores_sum / seq_lengths[:, None]
															a_				,			a_		:  List[str]											=      scores_sum_average.view(-1					).topk(__SCREAMING_SNAKE_CASE  ,						-1					)
															a_		:  str											=      next_tokens // scores_sum.shape[1]
															a_		:  int											=      seq_lengths[next_tokens_source]
															a_		:  Tuple											=      next_tokens % scores_sum.shape[1]
															a_		:  List[str]											=      next_tokens.unsqueeze(1					)
															a_		:  Tuple											=      tokens[next_tokens_source]
															a_		:  Dict											=      torch.cat((tokens, next_tokens)  ,						dim=1					)
															a_		:  Tuple											=      generated[next_tokens_source]
															a_		:  int											=      scores_sum_average * seq_lengths
															a_		:  int											=      is_stopped[next_tokens_source]

											a_		:  Optional[int]											=      self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]  ,						1  ,						-1					)
											a_		:  Optional[Any]											=      torch.cat((generated, next_token_embed)  ,						dim=1					)
											a_		:  List[str]											=      is_stopped + next_tokens.eq(__SCREAMING_SNAKE_CASE					).squeeze()
											if is_stopped.all():
															break

							a_		:  Optional[Any]											=      scores / seq_lengths
							a_		:  str											=      scores.argsort(descending=__SCREAMING_SNAKE_CASE					)
							# tokens tensors are already padded to max_seq_length
							a_		:  List[str]											=      [tokens[i] for i in order]
							a_		:  str											=      torch.stack(__SCREAMING_SNAKE_CASE  ,						dim=0					)
							a_		:  List[Any]											=      torch.tensor([seq_lengths[i] for i in order]  ,						dtype=seq_lengths.dtype					)
							return output_texts, seq_lengths












'''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))




