import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=768) ->			Any:
        super().__init__(__lowercase)
        __UpperCamelCase  :Tuple    						=	proj_size

        __UpperCamelCase  :int    						=	CLIPVisionModel(__lowercase)
        __UpperCamelCase  :List[str]    						=	PaintByExampleMapper(__lowercase)
        __UpperCamelCase  :int    						=	nn.LayerNorm(config.hidden_size)
        __UpperCamelCase  :List[str]    						=	nn.Linear(config.hidden_size				,				self.proj_size)

        # uncondition for scaling
        __UpperCamelCase  :Union[str, Any]    						=	nn.Parameter(torch.randn((1, 1, self.proj_size)))







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=False) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	self.model(pixel_values=__lowercase)
        __UpperCamelCase  :int    						=	clip_output.pooler_output
        __UpperCamelCase  :int    						=	self.mapper(latent_states[:, None])
        __UpperCamelCase  :str    						=	self.final_layer_norm(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	self.proj_out(__lowercase)
        if return_uncond_vector:
            return latent_states, self.uncond_vector

        return latent_states


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase) ->			Optional[int]:
        super().__init__()
        __UpperCamelCase  :List[str]    						=	(config.num_hidden_layers + 1) // 5
        __UpperCamelCase  :str    						=	config.hidden_size
        __UpperCamelCase  :Union[str, Any]    						=	1
        __UpperCamelCase  :Optional[int]    						=	nn.ModuleList(
            [
                BasicTransformerBlock(__lowercase				,				__lowercase				,				__lowercase				,				activation_fn='''gelu'''				,				attention_bias=__lowercase)
                for _ in range(__lowercase)
            ])







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Tuple:
        for block in self.blocks:
            __UpperCamelCase  :List[str]    						=	block(__lowercase)

        return hidden_states






import requests
from bsa import BeautifulSoup



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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


if __name__ == "__main__":
    __lowercase											=      {
        '''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))





