


'''simple docstring'''


import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam



class 	__lowerCAmelCase		(							datasets.BeamBasedBuilder    ):


  '''simple docstring'''







  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return datasets.DatasetInfo(
            features=datasets.Features({"""content""": datasets.Value("""string""" )} )	,supervised_keys=_a	,)






  def        _a							(							self :       List[str]	,_a :       Dict	,_a :       int ):

        '''simple docstring'''




        return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,gen_kwargs={"""examples""": get_test_dummy_examples()} )]






  def        _a							(							self :       Any	,_a :       Union[str, Any]	,_a :       Optional[Any] ):

        '''simple docstring'''




        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(_a )







class 	__lowerCAmelCase		(							datasets.BeamBasedBuilder    ):


  '''simple docstring'''







  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return datasets.DatasetInfo(
            features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} )	,supervised_keys=_a	,)






  def        _a							(							self :       Optional[Any]	,_a :       Union[str, Any]	,_a :       Dict ):

        '''simple docstring'''




        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN	,gen_kwargs={"""examples""": get_test_nested_examples()} )
        ]






  def        _a							(							self :       Tuple	,_a :       Tuple	,_a :       Dict ):

        '''simple docstring'''




        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(_a )







def      lowerCamelCase					(      ):
      return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]




def      lowerCamelCase					(      ):
      return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @require_beam
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				len(get_test_dummy_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
              A_						:  Union[str, Any]          =				DummyBeamDataset(cache_dir=_a	,beam_runner="""DirectRunner""" )
              builder.download_and_prepare()
              self.assertTrue(
                  os.path.exists(
                      os.path.join(_a	,builder.name	,"""default"""	,"""0.0.0"""	,f'{builder.name}-train.arrow' ) ) )
              self.assertDictEqual(builder.info.features	,datasets.Features({"""content""": datasets.Value("""string""" )} ) )
              A_						:  Optional[Any]          =				builder.as_dataset()
              self.assertEqual(dset["""train"""].num_rows	,_a )
              self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,_a )
              self.assertDictEqual(dset["""train"""][0]	,get_test_dummy_examples()[0][1] )
              self.assertDictEqual(
                  dset["""train"""][expected_num_examples - 1]	,get_test_dummy_examples()[expected_num_examples - 1][1] )
              self.assertTrue(
                  os.path.exists(os.path.join(_a	,builder.name	,"""default"""	,"""0.0.0"""	,"""dataset_info.json""" ) ) )
              del dset






  @require_beam
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        import apache_beam as beam

        A_						:  Dict          =				beam.io.parquetio.WriteToParquet

        A_						:  Optional[int]          =				len(get_test_dummy_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
              A_						:  List[Any]          =				DummyBeamDataset(cache_dir=_a	,beam_runner="""DirectRunner""" )
              with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
                    A_						:  int          =				partial(_a	,num_shards=2 )
                    builder.download_and_prepare()
              self.assertTrue(
                  os.path.exists(
                      os.path.join(
                          _a	,builder.name	,"""default"""	,"""0.0.0"""	,f'{builder.name}-train-00000-of-00002.arrow' ) ) )
              self.assertTrue(
                  os.path.exists(
                      os.path.join(
                          _a	,builder.name	,"""default"""	,"""0.0.0"""	,f'{builder.name}-train-00000-of-00002.arrow' ) ) )
              self.assertDictEqual(builder.info.features	,datasets.Features({"""content""": datasets.Value("""string""" )} ) )
              A_						:  int          =				builder.as_dataset()
              self.assertEqual(dset["""train"""].num_rows	,_a )
              self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,_a )
              # Order is not preserved when sharding, so we just check that all the elements are there
              self.assertListEqual(sorted(dset["""train"""]["""content"""] )	,sorted(["""foo""", """bar""", """foobar"""] ) )
              self.assertTrue(
                  os.path.exists(os.path.join(_a	,builder.name	,"""default"""	,"""0.0.0"""	,"""dataset_info.json""" ) ) )
              del dset






  @require_beam
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmp_cache_dir:
              A_						:  List[Any]          =				DummyBeamDataset(cache_dir=_a )
              self.assertRaises(datasets.builder.MissingBeamOptions	,builder.download_and_prepare )






  @require_beam
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				len(get_test_nested_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
              A_						:  str          =				NestedBeamDataset(cache_dir=_a	,beam_runner="""DirectRunner""" )
              builder.download_and_prepare()
              self.assertTrue(
                  os.path.exists(
                      os.path.join(_a	,builder.name	,"""default"""	,"""0.0.0"""	,f'{builder.name}-train.arrow' ) ) )
              self.assertDictEqual(
                  builder.info.features	,datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
              A_						:  Tuple          =				builder.as_dataset()
              self.assertEqual(dset["""train"""].num_rows	,_a )
              self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples	,_a )
              self.assertDictEqual(dset["""train"""][0]	,get_test_nested_examples()[0][1] )
              self.assertDictEqual(
                  dset["""train"""][expected_num_examples - 1]	,get_test_nested_examples()[expected_num_examples - 1][1] )
              self.assertTrue(
                  os.path.exists(os.path.join(_a	,builder.name	,"""default"""	,"""0.0.0"""	,"""dataset_info.json""" ) ) )
              del dset




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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