






from typing import TYPE_CHECKING

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


A_						:						List[Any]						      =					{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:						str						      =					['MobileViTFeatureExtractor']
   A_						:						Dict						      =					['MobileViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:						str						      =					[
       'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTForImageClassification',
       'MobileViTForSemanticSegmentation',
       'MobileViTModel',
       'MobileViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:						Optional[int]						      =					[
       'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFMobileViTForImageClassification',
       'TFMobileViTForSemanticSegmentation',
       'TFMobileViTModel',
       'TFMobileViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

   A_						:						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.....""")
     snake_case									=    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
     snake_case									=    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(1_00_00):
               out_file.write(data)
     snake_case									=    BeautifulSoup(res.text, """html.parser""")
     snake_case									=    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")}')





