
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_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Union[str, Any]				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				int		=[]
       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 							SCREAMING_SNAKE_CASE_	(					)			->						Tuple:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				str		=spark.range(1_0_0				).repartition(1				)
       SCREAMING_SNAKE_CASE_							:				Dict		=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=1_6				)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=spark.range(1_0				).repartition(2				)
       SCREAMING_SNAKE_CASE_							:				int		=[1, 0]
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=_generate_iterable_examples(UpperCAmelCase_			,							UpperCAmelCase_				)  # Reverse the partitions.
       SCREAMING_SNAKE_CASE_							:				List[Any]		=_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_	,     SCREAMING_SNAKE_CASE_							:				List[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_	(					)			->						Any:
       SCREAMING_SNAKE_CASE_							:				str		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				List[Any]		=spark.range(1_0				).repartition(1				)
       SCREAMING_SNAKE_CASE_							:				Optional[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_	(					)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				int		=spark.range(3_0				).repartition(3				)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch('''numpy.random.Generator'''				) as generator_mock:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=lambda UpperCAmelCase_				: x.reverse()
              SCREAMING_SNAKE_CASE_							:				Any		=_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_			,							[2, 1, 0]				)

              SCREAMING_SNAKE_CASE_							:				int		=SparkExamplesIterable(UpperCAmelCase_				).shuffle_data_sources(UpperCAmelCase_				)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(UpperCAmelCase_				):
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				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 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Dict		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				str		=spark.range(2_0				).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_							:				List[Any]		=_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_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=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_							:				List[str]		=SparkExamplesIterable(UpperCAmelCase_				).shard_data_sources(worker_id=1			,							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_			,							[1, 3]				)
       for i, (row_id, row_dict) in enumerate(UpperCAmelCase_				):
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=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_	(					)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				int		=pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=spark.range(1_0_0				).repartition(1				)
       SCREAMING_SNAKE_CASE_							:				Dict		=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_0_0


from math import ceil, sqrt
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 1_0_0_0_0_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0

       for outer_width in range(3			,							(limit // 4) + 2				):
              if outer_width**2 > limit:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=max(ceil(sqrt(outer_width**2 - limit				)				)			,							1				)
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1
              if (outer_width - hole_width_lower_bound) % 2:
                     hole_width_lower_bound += 1

              answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

       return answer


if __name__ == "__main__":
 print(F"{solution() = }")
