





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   A			(datasets.BeamBasedBuilder    ):
			'''simple docstring'''




			def    a_       (			self   :    str   )	->	int:







					"""simple docstring"""






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






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    int   )	->	Dict:







					"""simple docstring"""






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







			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Any   )	->	str:







					"""simple docstring"""






					import apache_beam as beam

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


class   A			(datasets.BeamBasedBuilder    ):
			'''simple docstring'''




			def    a_       (			self   :    int   )	->	Tuple:







					"""simple docstring"""






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






			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    List[str]   )	->	Tuple:







					"""simple docstring"""






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







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Tuple   )	->	Dict:







					"""simple docstring"""






					import apache_beam as beam

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







def    __lowerCamelCase					(							)		->				List[str]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							)		->				Optional[int]:






		"""simple docstring"""

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






class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@require_beam
			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






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






			@require_beam
			def    a_       (			self   :    Any   )	->	int:







					"""simple docstring"""






					import apache_beam as beam

					A__        =   beam.io.parquetio.WriteToParquet

					A__        =   len(get_test_dummy_examples()   )
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__        =   DummyBeamDataset(cache_dir=__lowerCAmelCase							,	beam_runner="""DirectRunner"""   )
							with patch("""apache_beam.io.parquetio.WriteToParquet"""   ) as write_parquet_mock:
									A__        =   partial(__lowerCAmelCase							,	num_shards=2   )
									builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            __lowerCAmelCase							,	builder.name							,	"""default"""							,	"""0.0.0"""							,	f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            __lowerCAmelCase							,	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__        =   builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows							,	__lowerCAmelCase   )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples							,	__lowerCAmelCase   )
							# 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(__lowerCAmelCase							,	builder.name							,	"""default"""							,	"""0.0.0"""							,	"""dataset_info.json"""   )   )   )
							del dset






			@require_beam
			def    a_       (			self   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__        =   DummyBeamDataset(cache_dir=__lowerCAmelCase   )
							self.assertRaises(datasets.builder.MissingBeamOptions							,	builder.download_and_prepare   )







			@require_beam
			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   len(get_test_nested_examples()   )
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__        =   NestedBeamDataset(cache_dir=__lowerCAmelCase							,	beam_runner="""DirectRunner"""   )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(__lowerCAmelCase							,	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__        =   builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows							,	__lowerCAmelCase   )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples							,	__lowerCAmelCase   )
							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(__lowerCAmelCase							,	builder.name							,	"""default"""							,	"""0.0.0"""							,	"""dataset_info.json"""   )   )   )
							del dset










from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      BeautifulSoup(requests.get(url).content, '''html.parser''')
					# The image URL is in the content field of the first meta tag with property og:image
					A    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
					with open(file_name, '''wb''') as fp:
										fp.write(image_data)
					print(F'''Done. Image saved to disk as {file_name}.''')



