


'''simple docstring'''


import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __init__(							self :       Any	,_a :       Dict	,_a :       str ):

        '''simple docstring'''




        A_						:  List[Any]          =				question_encoder
        A_						:  Dict          =				generator
        A_						:  Any          =				self.question_encoder






  def        _a							(							self :       List[Any]	,_a :       int ):

        '''simple docstring'''




        if os.path.isfile(_a ):
              raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
        os.makedirs(_a	,exist_ok=_a )
        A_						:  Any          =				os.path.join(_a	,"""question_encoder_tokenizer""" )
        A_						:  List[Any]          =				os.path.join(_a	,"""generator_tokenizer""" )
        self.question_encoder.save_pretrained(_a )
        self.generator.save_pretrained(_a )






  @classmethod
  def        _a							(							cls :       int	,_a :       List[str]	,**_a :       List[Any] ):

        '''simple docstring'''




        from ..auto.tokenization_auto import AutoTokenizer

        A_						:  Optional[int]          =				kwargs.pop("""config"""	,_a )

        if config is None:
              A_						:  str          =				RagConfig.from_pretrained(_a )

        A_						:  Optional[int]          =				AutoTokenizer.from_pretrained(
            _a	,config=config.question_encoder	,subfolder="""question_encoder_tokenizer""" )
        A_						:  Tuple          =				AutoTokenizer.from_pretrained(
            _a	,config=config.generator	,subfolder="""generator_tokenizer""" )
        return cls(question_encoder=_a	,generator=_a )






  def __call__(							self :       Optional[int]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        return self.current_tokenizer(*_a	,**_a )






  def        _a							(							self :       Optional[int]	,*_a :       List[Any]	,**_a :       Tuple ):

        '''simple docstring'''




        return self.generator.batch_decode(*_a	,**_a )






  def        _a							(							self :       Dict	,*_a :       Dict	,**_a :       List[Any] ):

        '''simple docstring'''




        return self.generator.decode(*_a	,**_a )






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.question_encoder






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.generator






  def        _a							(							self :       int	,_a :       Any	,_a :       List[str] = None	,_a :       Dict = None	,_a :       Dict = None	,_a :       List[Any] = "longest"	,_a :       List[str] = None	,_a :       str = True	,**_a :       Union[str, Any]	,):

        '''simple docstring'''




        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""	,_a	,)
        if max_length is None:
              A_						:  Union[str, Any]          =				self.current_tokenizer.model_max_length
        A_						:  Union[str, Any]          =				self(
            _a	,add_special_tokens=_a	,return_tensors=_a	,max_length=_a	,padding=_a	,truncation=_a	,**_a	,)
        if tgt_texts is None:
              return model_inputs
        # Process tgt_texts
        if max_target_length is None:
              A_						:  List[str]          =				self.current_tokenizer.model_max_length
        A_						:  List[Any]          =				self(
            text_target=_a	,add_special_tokens=_a	,return_tensors=_a	,padding=_a	,max_length=_a	,truncation=_a	,**_a	,)
        A_						:  List[Any]          =				labels["""input_ids"""]
        return model_inputs




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     __magic_name__			=      'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     __magic_name__			=      requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(10_000):
               out_file.write(data)
     __magic_name__			=      BeautifulSoup(res.text, 'html.parser')
     __magic_name__			=      list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f"""https://google.com{link.get('href')}""")
