





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase					:     Tuple												={
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=['''DebertaTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Optional[int]												=[
     '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''DebertaForMaskedLM''',
     '''DebertaForQuestionAnswering''',
     '''DebertaForSequenceClassification''',
     '''DebertaForTokenClassification''',
     '''DebertaModel''',
     '''DebertaPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Union[str, Any]												=[
     '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFDebertaForMaskedLM''',
     '''TFDebertaForQuestionAnswering''',
     '''TFDebertaForSequenceClassification''',
     '''TFDebertaForTokenClassification''',
     '''TFDebertaModel''',
     '''TFDebertaPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase					:     Dict												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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


