



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       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					[]
							for part_id in partition_order:
														_lowerCAmelCase  :							List[Any]				     =					df.where(F"SPARK_PARTITION_ID() = {part_id}"						).collect()
														for row_idx, row in enumerate(_lowerCamelCase						):
																					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       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
							_lowerCAmelCase  :							List[Any]				     =					spark.range(100						).repartition(1						)
							_lowerCAmelCase  :							Optional[Any]				     =					Spark(_lowerCamelCase						)
							# 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       (      ):





							'''simple docstring'''




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

							for i, (row_id, row_dict) in enumerate(generate_fn()						):
														_lowerCAmelCase     ,					_lowerCAmelCase  :							Dict				     =					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       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
							_lowerCAmelCase  :							List[Any]				     =					spark.range(10						).repartition(1						)
							_lowerCAmelCase  :							Dict				     =					SparkExamplesIterable(_lowerCamelCase						)
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
														assert row_id == F"0_{i}"
														assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def        A       (      ):





							'''simple docstring'''




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

														_lowerCAmelCase  :							List[str]				     =					SparkExamplesIterable(_lowerCamelCase						).shuffle_data_sources(_lowerCamelCase						)
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
																					_lowerCAmelCase     ,					_lowerCAmelCase  :							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        A       (      ):





							'''simple docstring'''




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

							# Partitions 0 and 2
							_lowerCAmelCase  :							Optional[int]				     =					SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=0       ,				num_workers=2						)
							assert shard_it_a.n_shards == 2
							_lowerCAmelCase  :							Tuple				     =					_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase       ,				[0, 2]						)
							for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
														_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[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
							_lowerCAmelCase  :							str				     =					SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=1       ,				num_workers=2						)
							assert shard_it_a.n_shards == 2
							_lowerCAmelCase  :							Union[str, Any]				     =					_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase       ,				[1, 3]						)
							for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
														_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					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       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					pyspark.sql.SparkSession.builder.master("local[*]"						).appName("pyspark"						).getOrCreate()
							_lowerCAmelCase  :							Optional[int]				     =					spark.range(100						).repartition(1						)
							_lowerCAmelCase  :							Optional[Any]				     =					Spark(_lowerCamelCase						)
							# 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() == 100





def        A       (      _lowerCamelCase = 1_000_000						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					1
							_lowerCAmelCase  :							Optional[Any]				     =					1
							_lowerCAmelCase  :							List[str]				     =					{1: 1}

							for inputa in range(2       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							int				     =					0
														_lowerCAmelCase  :							Any				     =					inputa

														while True:
																					if number in counters:
																												counter += counters[number]
																												break
																					if number % 2 == 0:
																												number //= 2
																												counter += 1
																					else:
																												_lowerCAmelCase  :							Any				     =					(3 * number) + 1
																												counter += 1

														if inputa not in counters:
																					_lowerCAmelCase  :							Tuple				     =					counter

														if counter > pre_counter:
																					_lowerCAmelCase  :							Union[str, Any]				     =					inputa
																					_lowerCAmelCase  :							Union[str, Any]				     =					counter
							return largest_number


if __name__ == "__main__":
						print(solution(int(input().strip())))
