





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 _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  List[Any]       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
							for part_id in partition_order:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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 _a (  )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      Any     =						spark.range(1_00       ).repartition(1       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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 _a (  )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      List[str]     =						spark.range(10       ).repartition(2       )
							SCREAMING_SNAKE_CASE__       :      int     =						[1, 0]
							SCREAMING_SNAKE_CASE__       :      Any     =						_generate_iterable_examples(UpperCAmelCase__			,				UpperCAmelCase__       )  # Reverse the partitions.
							SCREAMING_SNAKE_CASE__       :      str     =						_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__			,				UpperCAmelCase__       )

							for i, (row_id, row_dict) in enumerate(generate_fn()       ):
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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 _a (  )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      Dict     =						spark.range(10       ).repartition(1       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						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 _a (  )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      int     =						spark.range(30       ).repartition(3       )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("numpy.random.Generator"       ) as generator_mock:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						lambda SCREAMING_SNAKE_CASE__       : x.reverse()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__			,				[2, 1, 0]       )

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						SparkExamplesIterable(UpperCAmelCase__       ).shuffle_data_sources(UpperCAmelCase__       )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(UpperCAmelCase__       ):
																					SCREAMING_SNAKE_CASE__       :      Any     =						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 _a (  )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      int     =						spark.range(20       ).repartition(4       )

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

							# Partitions 1 and 3
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						SparkExamplesIterable(UpperCAmelCase__       ).shard_data_sources(worker_id=1			,				num_workers=2       )
							assert shard_it_a.n_shards == 2
							SCREAMING_SNAKE_CASE__       :      Any     =						_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase__			,				[1, 3]       )
							for i, (row_id, row_dict) in enumerate(UpperCAmelCase__       ):
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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 _a (  )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pyspark.sql.SparkSession.builder.master("local[*]"       ).appName("pyspark"       ).getOrCreate()
							SCREAMING_SNAKE_CASE__       :      Dict     =						spark.range(1_00       ).repartition(1       )
							SCREAMING_SNAKE_CASE__       :      str     =						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













import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		_lowerCamelCase :    Union[str, Any]  							= '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

		print('''Googling.....''')

		_lowerCamelCase :    Any  							= f"https://www.google.com/search?q={query}&num=100"

		_lowerCamelCase :    Optional[Any]  							= requests.get(
		    url,
		    headers={'''User-Agent''': str(UserAgent().random)},
		)

		try:
				_lowerCamelCase :    Union[str, Any]  							= (
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''yuRUbf'''})
				    .find('''a''')
				    .get('''href''')
				)

		except AttributeError:
				_lowerCamelCase :    Optional[Any]  							= parse_qs(
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''kCrYT'''})
				    .find('''a''')
				    .get('''href''')
				)['''url'''][0]

		webbrowser.open(link)






