from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		str      			=   field(
        metadata={"""help""": """The output directory where the model will be written."""}		,			)
    a__			:		str      			=   field(
        metadata={
            """help""": (
                """The encoder model checkpoint for weights initialization."""
                """Don't set if you want to train an encoder model from scratch."""
            )
        }		,			)
    a__			:		str      			=   field(
        metadata={
            """help""": (
                """The decoder model checkpoint for weights initialization."""
                """Don't set if you want to train a decoder model from scratch."""
            )
        }		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}				)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}				)



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







    __UpperCamelCase  :Dict    						=	HfArgumentParser((ModelArguments,)			)
    ((__UpperCamelCase)						, )  :Optional[int]    						=	parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

    # Use explicit specified encoder config
    if model_args.encoder_config_name:
        __UpperCamelCase  :Tuple    						=	AutoConfig.from_pretrained(model_args.encoder_config_name			)
    # Use pretrained encoder model's config
    else:
        __UpperCamelCase  :List[str]    						=	AutoConfig.from_pretrained(model_args.encoder_model_name_or_path			)

    # Use explicit specified decoder config
    if model_args.decoder_config_name:
        __UpperCamelCase  :List[str]    						=	AutoConfig.from_pretrained(model_args.decoder_config_name			)
    # Use pretrained decoder model's config
    else:
        __UpperCamelCase  :Optional[int]    						=	AutoConfig.from_pretrained(model_args.decoder_model_name_or_path			)

    # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
    __UpperCamelCase  :Dict    						=	True
    __UpperCamelCase  :List[str]    						=	True

    __UpperCamelCase  :int    						=	FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
        encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,    decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,    encoder_config=SCREAMING_SNAKE_CASE						,    decoder_config=SCREAMING_SNAKE_CASE						,    )

    # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
    __UpperCamelCase  :str    						=	decoder_config.decoder_start_token_id
    __UpperCamelCase  :str    						=	decoder_config.pad_token_id
    if decoder_start_token_id is None:
        __UpperCamelCase  :List[Any]    						=	decoder_config.bos_token_id
    if pad_token_id is None:
        __UpperCamelCase  :int    						=	decoder_config.eos_token_id

    # This is necessary to make Flax's generate() work
    __UpperCamelCase  :str    						=	decoder_config.eos_token_id
    __UpperCamelCase  :Tuple    						=	decoder_start_token_id
    __UpperCamelCase  :Union[str, Any]    						=	pad_token_id

    __UpperCamelCase  :Tuple    						=	AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path			)

    __UpperCamelCase  :Optional[int]    						=	AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path			)
    __UpperCamelCase  :List[str]    						=	tokenizer.convert_ids_to_tokens(model.config.pad_token_id			)

    model.save_pretrained(model_args.output_dir			)
    image_processor.save_pretrained(model_args.output_dir			)
    tokenizer.save_pretrained(model_args.output_dir			)


if __name__ == "__main__":
    main()






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))





