





'''simple docstring'''




from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[str]:
	snake_case__	       =   []
	for part_id in partition_order:
		snake_case__	       =   df.where(f'''SPARK_PARTITION_ID() = {part_id}'''							).collect()
		for row_idx, row in enumerate(UpperCAmelCase_							):
			expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict())							)
	return expected_row_ids_and_row_dicts




@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->Optional[int]:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(1_00							).repartition(1							)
	snake_case__	       =   Spark(UpperCAmelCase_							)
	# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
	# that each partition can hold 2 rows.
	spark_builder._repartition_df_if_needed(max_shard_size=16							)
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 50




@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->List[str]:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(10							).repartition(2							)
	snake_case__	       =   [1, 0]
	snake_case__	       =   _generate_iterable_examples(UpperCAmelCase_					,				UpperCAmelCase_							)  # Reverse the partitions.
	snake_case__	       =   _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_					,				UpperCAmelCase_							)

	for i, (row_id, row_dict) in enumerate(generate_fn()							):
		snake_case__       ,  snake_case__	       =   expected_row_ids_and_row_dicts[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict




@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->int:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(10							).repartition(1							)
	snake_case__	       =   SparkExamplesIterable(UpperCAmelCase_							)
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
		assert row_id == f'''0_{i}'''
		assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->Optional[int]:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(30							).repartition(3							)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch('numpy.random.Generator'							) as generator_mock:
		snake_case__	       =   lambda UpperCAmelCase_							: x.reverse()
		snake_case__	       =   _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_					,				[2, 1, 0]							)

		snake_case__	       =   SparkExamplesIterable(UpperCAmelCase_							).shuffle_data_sources(UpperCAmelCase_							)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
			snake_case__       ,  snake_case__	       =   expected_row_ids_and_row_dicts[i]
			assert row_id == expected_row_id
			assert row_dict == expected_row_dict




@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->str:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(20							).repartition(4							)

	# Partitions 0 and 2
	snake_case__	       =   SparkExamplesIterable(UpperCAmelCase_							).shard_data_sources(worker_id=0					,				num_workers=2							)
	assert shard_it_a.n_shards == 2
	snake_case__	       =   _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_					,				[0, 2]							)
	for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
		snake_case__       ,  snake_case__	       =   expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	snake_case__	       =   SparkExamplesIterable(UpperCAmelCase_							).shard_data_sources(worker_id=1					,				num_workers=2							)
	assert shard_it_a.n_shards == 2
	snake_case__	       =   _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_					,				[1, 3]							)
	for i, (row_id, row_dict) in enumerate(UpperCAmelCase_							):
		snake_case__       ,  snake_case__	       =   expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def 						__lowerCamelCase			(	)   ->List[Any]:
	snake_case__	       =   pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
	snake_case__	       =   spark.range(1_00							).repartition(1							)
	snake_case__	       =   Spark(UpperCAmelCase_							)
	# Choose a small max_shard_size for maximum partitioning.
	spark_builder._repartition_df_if_needed(max_shard_size=1							)
	# The new number of partitions should not be greater than the number of rows.
	assert spark_builder.df.rdd.getNumPartitions() == 1_00










'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



