




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
  print("""Googling.....""")
  _lowerCamelCase      :						List[Any]							=  """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
  _lowerCamelCase      :						Union[str, Any]							=  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(10000):
      out_file.write(data)
  _lowerCamelCase      :						Union[str, Any]							=  BeautifulSoup(res.text, """html.parser""")
  _lowerCamelCase      :						Any							=  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')}''')



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      Any            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    'transfo-xl'
    SCREAMING_SNAKE_CASE:				List[Any]          =    ['mems']
    SCREAMING_SNAKE_CASE:				List[Any]          =    {
        'n_token': 'vocab_size',
        'hidden_size': 'd_model',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=267_735       ,						lowerCamelCase__=[20_000, 40_000, 200_000]       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=16       ,						lowerCamelCase__=64       ,						lowerCamelCase__=4_096       ,						lowerCamelCase__=4       ,						lowerCamelCase__=False       ,						lowerCamelCase__=18       ,						lowerCamelCase__=1_600       ,						lowerCamelCase__=1_000       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0       ,						lowerCamelCase__=-1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=True       ,						lowerCamelCase__="normal"       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Dict       =	vocab_size
       lowerCAmelCase_:							Any       =	[]
       self.cutoffs.extend(lowerCamelCase__ )
       if proj_share_all_but_first:
          lowerCAmelCase_:							int       =	[False] + [True] * len(self.cutoffs )
       else:
          lowerCAmelCase_:							Any       =	[False] + [False] * len(self.cutoffs )
       lowerCAmelCase_:							Dict       =	d_model
       lowerCAmelCase_:							Union[str, Any]       =	d_embed
       lowerCAmelCase_:							Dict       =	d_head
       lowerCAmelCase_:							Dict       =	d_inner
       lowerCAmelCase_:							List[str]       =	div_val
       lowerCAmelCase_:							List[str]       =	pre_lnorm
       lowerCAmelCase_:							List[str]       =	n_layer
       lowerCAmelCase_:							List[str]       =	n_head
       lowerCAmelCase_:							str       =	mem_len
       lowerCAmelCase_:							Any       =	same_length
       lowerCAmelCase_:							Optional[Any]       =	attn_type
       lowerCAmelCase_:							int       =	clamp_len
       lowerCAmelCase_:							Optional[int]       =	sample_softmax
       lowerCAmelCase_:							Optional[Any]       =	adaptive
       lowerCAmelCase_:							Optional[int]       =	dropout
       lowerCAmelCase_:							Union[str, Any]       =	dropatt
       lowerCAmelCase_:							Tuple       =	untie_r
       lowerCAmelCase_:							Union[str, Any]       =	init
       lowerCAmelCase_:							Optional[Any]       =	init_range
       lowerCAmelCase_:							Optional[Any]       =	proj_init_std
       lowerCAmelCase_:							Tuple       =	init_std
       lowerCAmelCase_:							Tuple       =	layer_norm_epsilon
       super().__init__(eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )




    @property
    def      _a    (    self ):
       # Message copied from Transformer-XL documentation
       logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1




    @max_position_embeddings.setter
    def      _a    (    self       ,						lowerCamelCase__ ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )