
"""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 				A__ (			datasets.BeamBasedBuilder):

		def 							__lowerCamelCase							(   self				):
			return datasets.DatasetInfo(
			    features=datasets.Features({'content': datasets.Value('string'				)}				) ,	supervised_keys=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			import apache_beam as beam

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




class 				A__ (			datasets.BeamBasedBuilder):

		def 							__lowerCamelCase							(   self				):
			return datasets.DatasetInfo(
			    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				) ,	supervised_keys=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return [
			    datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={'examples': get_test_nested_examples()}				)
			]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			import apache_beam as beam

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



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 				A__ (			_lowerCamelCase):

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

		@require_beam
		def 							__lowerCamelCase							(   self				):
			import apache_beam as beam

			__lowerCAmelCase    :		Any		    =   beam.io.parquetio.WriteToParquet

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

		@require_beam
		def 							__lowerCamelCase							(   self				):
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		List[Any]		    =   DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE				)
				self.assertRaises(datasets.builder.MissingBeamOptions ,	builder.download_and_prepare				)







		@require_beam
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   len(get_test_nested_examples()				)
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		Dict		    =   NestedBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,	beam_runner='DirectRunner'				)
				builder.download_and_prepare()
				self.assertTrue(
				    os.path.exists(
				        os.path.join(_SCREAMING_SNAKE_CASE ,	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'				)}				)}				)				)
				__lowerCAmelCase    :		List[str]		    =   builder.as_dataset()
				self.assertEqual(dset['train'].num_rows ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(dset['train'].info.splits['train'].num_examples ,	_SCREAMING_SNAKE_CASE				)
				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(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	'dataset_info.json'				)				)				)
				del dset

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()