





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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Union[str, Any]											=	[]
	for part_id in partition_order:
		snake_case_		:			Any											=	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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Any											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			Any											=	spark.range(1_00   ).repartition(1   )
	snake_case_		:			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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			int											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			Any											=	spark.range(10   ).repartition(2   )
	snake_case_		:			int											=	[1, 0]
	snake_case_		:			int											=	_generate_iterable_examples(__UpperCamelCase   , __UpperCamelCase   )  # Reverse the partitions.
	snake_case_		:			int											=	_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_		:			Optional[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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Union[str, Any]											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			int											=	spark.range(10   ).repartition(1   )
	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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Union[str, Any]											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			List[Any]											=	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_		:			Tuple											=	lambda __a   : x.reverse()
		snake_case_		:			str											=	_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase   , [2, 1, 0]   )

		snake_case_		:			Tuple											=	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_		:			int											=	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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[Any]											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			Any											=	spark.range(20   ).repartition(4   )

	# Partitions 0 and 2
	snake_case_		:			List[str]											=	SparkExamplesIterable(__UpperCamelCase   ).shard_data_sources(worker_id=0   , num_workers=2   )
	assert shard_it_a.n_shards == 2
	snake_case_		:			Optional[int]											=	_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_		:			List[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
	snake_case_		:			Tuple											=	SparkExamplesIterable(__UpperCamelCase   ).shard_data_sources(worker_id=1   , num_workers=2   )
	assert shard_it_a.n_shards == 2
	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   ):
		snake_case_						,snake_case_		:			Dict											=	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 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Dict											=	pyspark.sql.SparkSession.builder.master('local[*]'   ).appName('pyspark'   ).getOrCreate()
	snake_case_		:			Optional[int]											=	spark.range(1_00   ).repartition(1   )
	snake_case_		:			List[Any]											=	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__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

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

						webbrowser.open(link)

