'''simple docstring'''

import torch
from torch import nn

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







class __a (   UpperCAmelCase__,   UpperCAmelCase__			):





   @register_to_config
   def __init__(					self				:			Dict				,*,
       lowerCamelCase				:			int = 4				,lowerCamelCase				:			int = 768				,lowerCamelCase				:			int				,lowerCamelCase				:			Optional[int]				,):

      '''simple docstring'''

      super().__init__()

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.zeros(lowerCamelCase    )    )

      # parameters for additional clip time embeddings
      __SCREAMING_SNAKE_CASE      							=       nn.Linear(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.Linear(lowerCamelCase				,lowerCamelCase    )

      # parameters for encoder hidden states
      __SCREAMING_SNAKE_CASE      							=       clip_extra_context_tokens
      __SCREAMING_SNAKE_CASE      							=       nn.Linear(
          lowerCamelCase				,self.clip_extra_context_tokens * cross_attention_dim    )
      __SCREAMING_SNAKE_CASE      							=       nn.Linear(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.LayerNorm(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,*, lowerCamelCase				:			Union[str, Any]				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Dict				,lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      if do_classifier_free_guidance:
         # Add the classifier free guidance embeddings to the image embeddings
         __SCREAMING_SNAKE_CASE      							=       image_embeddings.shape[0]
         __SCREAMING_SNAKE_CASE      							=       self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
         __SCREAMING_SNAKE_CASE      							=       classifier_free_guidance_embeddings.expand(
             lowerCamelCase				,-1    )
         __SCREAMING_SNAKE_CASE      							=       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]

      __SCREAMING_SNAKE_CASE      							=       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, ...
      __SCREAMING_SNAKE_CASE      							=       self.embedding_proj(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       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"
      __SCREAMING_SNAKE_CASE      							=       self.clip_extra_context_tokens_proj(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       clip_extra_context_tokens.reshape(lowerCamelCase				,-1				,self.clip_extra_context_tokens    )
      __SCREAMING_SNAKE_CASE      							=       clip_extra_context_tokens.permute(0				,2				,1    )

      __SCREAMING_SNAKE_CASE      							=       self.encoder_hidden_states_proj(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.text_encoder_hidden_states_norm(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,dim=1    )

      return text_encoder_hidden_states, additive_clip_time_embeddings












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







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


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





