





'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase					:     List[Any]												={
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     List[Any]												=['''AlbertTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Optional[int]												=['''AlbertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlbertForMaskedLM''',
     '''AlbertForMultipleChoice''',
     '''AlbertForPreTraining''',
     '''AlbertForQuestionAnswering''',
     '''AlbertForSequenceClassification''',
     '''AlbertForTokenClassification''',
     '''AlbertModel''',
     '''AlbertPreTrainedModel''',
     '''load_tf_weights_in_albert''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     int												=[
     '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFAlbertForMaskedLM''',
     '''TFAlbertForMultipleChoice''',
     '''TFAlbertForPreTraining''',
     '''TFAlbertForQuestionAnswering''',
     '''TFAlbertForSequenceClassification''',
     '''TFAlbertForTokenClassification''',
     '''TFAlbertMainLayer''',
     '''TFAlbertModel''',
     '''TFAlbertPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     int												=[
     '''FlaxAlbertForMaskedLM''',
     '''FlaxAlbertForMultipleChoice''',
     '''FlaxAlbertForPreTraining''',
     '''FlaxAlbertForQuestionAnswering''',
     '''FlaxAlbertForSequenceClassification''',
     '''FlaxAlbertForTokenClassification''',
     '''FlaxAlbertModel''',
     '''FlaxAlbertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

 lowerCAmelCase					:     Any												=_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')}''')


