





from ... import PretrainedConfig


_a     :    Optional[Any]         =						{
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class   a_ (			a      ):
			A__      :			str       	=  NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			A__      :			List[Any]       	=  "nezha"

			def __init__(   self   :  Dict     , UpperCAmelCase__   :  Any=21_128     , UpperCAmelCase__   :  Dict=768     , UpperCAmelCase__   :  Dict=12     , UpperCAmelCase__   :  Optional[Any]=12     , UpperCAmelCase__   :  Union[str, Any]=3_072     , UpperCAmelCase__   :  List[str]="gelu"     , UpperCAmelCase__   :  str=0.1     , UpperCAmelCase__   :  Dict=0.1     , UpperCAmelCase__   :  str=512     , UpperCAmelCase__   :  Any=64     , UpperCAmelCase__   :  Any=2     , UpperCAmelCase__   :  str=0.02     , UpperCAmelCase__   :  Union[str, Any]=1e-1_2     , UpperCAmelCase__   :  Dict=0.1     , UpperCAmelCase__   :  Tuple=0     , UpperCAmelCase__   :  str=2     , UpperCAmelCase__   :  List[str]=3     , UpperCAmelCase__   :  Any=True     , **UpperCAmelCase__   :  Optional[Any]     , ):






							"""simple docstring"""
							super().__init__(pad_token_id=a_     , bos_token_id=a_     , eos_token_id=a_     , **a_						)

							snake_case   :					Optional[int]    						=				vocab_size
							snake_case   :					List[str]    						=				hidden_size
							snake_case   :					int    						=				num_hidden_layers
							snake_case   :					Dict    						=				num_attention_heads
							snake_case   :					List[Any]    						=				hidden_act
							snake_case   :					Dict    						=				intermediate_size
							snake_case   :					Optional[Any]    						=				hidden_dropout_prob
							snake_case   :					Any    						=				attention_probs_dropout_prob
							snake_case   :					Tuple    						=				max_position_embeddings
							snake_case   :					Dict    						=				max_relative_position
							snake_case   :					int    						=				type_vocab_size
							snake_case   :					List[str]    						=				initializer_range
							snake_case   :					List[Any]    						=				layer_norm_eps
							snake_case   :					Union[str, Any]    						=				classifier_dropout
							snake_case   :					Tuple    						=				use_cache



'''simple docstring'''




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
  print('Googling.....')
  _a					:			List[Any]							=    'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
  _a					:			Dict							=    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)
  _a					:			Any							=    BeautifulSoup(res.text, 'html.parser')
  _a					:			Optional[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')}''')


