




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     a__					(      snake_case__				,       snake_case__				,       snake_case__   ):
						_a							:					List[str]      =					[R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]






						@register_to_config
						def __init__(     self	,    _A	,    _A	,    _A = None	,    _A = 5_0_2_5_7	,    _A = 1_0_2_4	,    _A = 7_6_8	,    _A = 1_2	,    _A = 1_2	,    _A = None	,    _A = "gelu_new"	,    _A = 0.1	,    _A = 0.1	,    _A = 0.1	,    _A = 1E-5	,    _A = 0.02	,    _A = True	,    _A = True	,    _A = False	,    _A = False	,    ):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase            = 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."""    )

											__lowerCAmelCase            = prefix_inner_dim
											__lowerCAmelCase            = prefix_hidden_dim

											__lowerCAmelCase            = (
											    nn.Linear(self.prefix_inner_dim	,    self.prefix_hidden_dim    )
											    if self.prefix_hidden_dim is not None
											    else nn.Identity()
											)
											__lowerCAmelCase            = (
											    nn.Linear(self.prefix_hidden_dim	,    _A    ) if self.prefix_hidden_dim is not None else nn.Identity()
											)

											__lowerCAmelCase            = GPTaConfig(
											    vocab_size=_A	,    n_positions=_A	,    n_embd=_A	,    n_layer=_A	,    n_head=_A	,    n_inner=_A	,    activation_function=_A	,    resid_pdrop=_A	,    embd_pdrop=_A	,    attn_pdrop=_A	,    layer_norm_epsilon=_A	,    initializer_range=_A	,    scale_attn_weights=_A	,    use_cache=_A	,    scale_attn_by_inverse_layer_idx=_A	,    reorder_and_upcast_attn=_A	,    )
											__lowerCAmelCase            = GPTaLMHeadModel(_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A = None	,    _A = None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.transformer.transformer.wte(_A    )
											__lowerCAmelCase            = self.encode_prefix(_A    )
											__lowerCAmelCase            = self.decode_prefix(_A    )
											__lowerCAmelCase            = torch.cat((prefix_embeds, embedding_text)	,    dim=1    )

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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											return torch.zeros(_A	,    self.prefix_length	,    dtype=torch.intaa	,    device=_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											return self.encode_prefix(_A    )






						@torch.no_grad()
						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = torch.split(_A	,    1	,    dim=0    )
											__lowerCAmelCase            = []
											__lowerCAmelCase            = []
											for feature in features:
																__lowerCAmelCase            = self.decode_prefix(feature.to(_A    )    )  # back to the clip feature
																# Only support beam search for now
																__lowerCAmelCase     ,							__lowerCAmelCase            = self.generate_beam(
																    input_embeds=_A	,    device=_A	,    eos_token_id=_A    )
																generated_tokens.append(output_tokens[0]    )
																generated_seq_lengths.append(seq_lengths[0]    )
											__lowerCAmelCase            = torch.stack(_A    )
											__lowerCAmelCase            = torch.stack(_A    )
											return generated_tokens, generated_seq_lengths






						@torch.no_grad()
						def        __SCREAMING_SNAKE_CASE(     self	,    _A=None	,    _A=None	,    _A=None	,    _A = 5	,    _A = 6_7	,    _A = 1.0	,    _A = None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = eos_token_id
											__lowerCAmelCase            = None
											__lowerCAmelCase            = None
											__lowerCAmelCase            = torch.ones(_A	,    device=_A	,    dtype=torch.int    )
											__lowerCAmelCase            = torch.zeros(_A	,    device=_A	,    dtype=torch.bool    )

											if input_embeds is not None:
																__lowerCAmelCase            = input_embeds
											else:
																__lowerCAmelCase            = self.transformer.transformer.wte(_A    )

											for i in range(_A    ):
																__lowerCAmelCase            = self.transformer(inputs_embeds=_A    )
																__lowerCAmelCase            = outputs.logits
																__lowerCAmelCase            = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																__lowerCAmelCase            = logits.softmax(-1    ).log()

																if scores is None:
																					__lowerCAmelCase     ,							__lowerCAmelCase            = logits.topk(_A	,    -1    )
																					__lowerCAmelCase            = generated.expand(_A	,    *generated.shape[1:]    )
																					__lowerCAmelCase     ,							__lowerCAmelCase            = next_tokens.permute(1	,    0    ), scores.squeeze(0    )
																					if tokens is None:
																										__lowerCAmelCase            = next_tokens
																					else:
																										__lowerCAmelCase            = tokens.expand(_A	,    *tokens.shape[1:]    )
																										__lowerCAmelCase            = torch.cat((tokens, next_tokens)	,    dim=1    )
																else:
																					__lowerCAmelCase            = -float(np.inf    )
																					__lowerCAmelCase            = 0
																					__lowerCAmelCase            = scores[:, None] + logits
																					seq_lengths[~is_stopped] += 1
																					__lowerCAmelCase            = scores_sum / seq_lengths[:, None]
																					__lowerCAmelCase     ,							__lowerCAmelCase            = scores_sum_average.view(-1    ).topk(_A	,    -1    )
																					__lowerCAmelCase            = next_tokens // scores_sum.shape[1]
																					__lowerCAmelCase            = seq_lengths[next_tokens_source]
																					__lowerCAmelCase            = next_tokens % scores_sum.shape[1]
																					__lowerCAmelCase            = next_tokens.unsqueeze(1    )
																					__lowerCAmelCase            = tokens[next_tokens_source]
																					__lowerCAmelCase            = torch.cat((tokens, next_tokens)	,    dim=1    )
																					__lowerCAmelCase            = generated[next_tokens_source]
																					__lowerCAmelCase            = scores_sum_average * seq_lengths
																					__lowerCAmelCase            = is_stopped[next_tokens_source]

																__lowerCAmelCase            = self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]	,    1	,    -1    )
																__lowerCAmelCase            = torch.cat((generated, next_token_embed)	,    dim=1    )
																__lowerCAmelCase            = is_stopped + next_tokens.eq(_A    ).squeeze()
																if is_stopped.all():
																					break

											__lowerCAmelCase            = scores / seq_lengths
											__lowerCAmelCase            = scores.argsort(descending=_A    )
											# tokens tensors are already padded to max_seq_length
											__lowerCAmelCase            = [tokens[i] for i in order]
											__lowerCAmelCase            = torch.stack(_A	,    dim=0    )
											__lowerCAmelCase            = torch.tensor([seq_lengths[i] for i in order]	,    dtype=seq_lengths.dtype    )
											return output_texts, seq_lengths










import requests
from bsa import BeautifulSoup







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


if __name__ == "__main__":
					UpperCamelCase__					    =    {
					    """title""": (
					        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
					        """capacitance, volumetric capacitance, and energy density"""
					    ),
					    """journal""": """Chem. Mater.""",
					    """volume""": 30,
					    """pages""": """3979-3990""",
					    """year""": 2018,
					    """hl""": """en""",
					}
					print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))




