



import torch
from torch import nn

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



class 						UpperCAmelCase_					( a     ,      a):







							@register_to_config
							def __init__(     self,    *,
							    __a = 4,    __a = 768,    __a,    __a,    ):

														'''simple docstring'''

														super().__init__()

														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.zeros(__a))

														# parameters for additional clip time embeddings
														_lowerCAmelCase  :							Dict				     =					nn.Linear(__a,    __a)
														_lowerCAmelCase  :							int				     =					nn.Linear(__a,    __a)

														# parameters for encoder hidden states
														_lowerCAmelCase  :							List[Any]				     =					clip_extra_context_tokens
														_lowerCAmelCase  :							int				     =					nn.Linear(
														    __a,    self.clip_extra_context_tokens * cross_attention_dim)
														_lowerCAmelCase  :							List[str]				     =					nn.Linear(__a,    __a)
														_lowerCAmelCase  :							Dict				     =					nn.LayerNorm(__a)







							def      snake_case__   (     self,    *, __a,    __a,    __a,    __a):

														'''simple docstring'''

														if do_classifier_free_guidance:
																					# Add the classifier free guidance embeddings to the image embeddings
																					_lowerCAmelCase  :							List[Any]				     =					image_embeddings.shape[0]
																					_lowerCAmelCase  :							Optional[Any]				     =					self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
																					_lowerCAmelCase  :							Optional[int]				     =					classifier_free_guidance_embeddings.expand(
																					    __a,    -1)
																					_lowerCAmelCase  :							List[str]				     =					torch.cat([classifier_free_guidance_embeddings, image_embeddings],    dim=0)

														# The image embeddings batch size and the text embeddings batch size are equal
														assert image_embeddings.shape[0] == prompt_embeds.shape[0]

														_lowerCAmelCase  :							Dict				     =					prompt_embeds.shape[0]

														# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
														# adding CLIP embeddings to the existing timestep embedding, ...
														_lowerCAmelCase  :							List[str]				     =					self.embedding_proj(__a)
														_lowerCAmelCase  :							List[str]				     =					self.clip_image_embeddings_project_to_time_embeddings(__a)
														_lowerCAmelCase  :							List[Any]				     =					time_projected_image_embeddings + time_projected_prompt_embeds

														# ... and by projecting CLIP embeddings into four
														# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
														_lowerCAmelCase  :							Any				     =					self.clip_extra_context_tokens_proj(__a)
														_lowerCAmelCase  :							List[Any]				     =					clip_extra_context_tokens.reshape(__a,    -1,    self.clip_extra_context_tokens)
														_lowerCAmelCase  :							Optional[int]				     =					clip_extra_context_tokens.permute(0,    2,    1)

														_lowerCAmelCase  :							str				     =					self.encoder_hidden_states_proj(__a)
														_lowerCAmelCase  :							Any				     =					self.text_encoder_hidden_states_norm(__a)
														_lowerCAmelCase  :							Optional[int]				     =					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states],    dim=1)

														return text_encoder_hidden_states, additive_clip_time_embeddings











import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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


if __name__ == "__main__":
						_snake_case	=						{
						    "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))
