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__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Union[str, Any]   ):
 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       A__   (						):
 SCREAMING_SNAKE_CASE__: List[Any]=   pyspark.sql.SparkSession.builder.master('''local[*]'''   ).appName('''pyspark'''   ).getOrCreate()
 SCREAMING_SNAKE_CASE__: str=   spark.range(100   ).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=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__   (						):
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   pyspark.sql.SparkSession.builder.master('''local[*]'''   ).appName('''pyspark'''   ).getOrCreate()
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   spark.range(10   ).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__: 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       A__   (						):
 SCREAMING_SNAKE_CASE__: str=   pyspark.sql.SparkSession.builder.master('''local[*]'''   ).appName('''pyspark'''   ).getOrCreate()
 SCREAMING_SNAKE_CASE__: List[Any]=   spark.range(10   ).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       A__   (						):
 SCREAMING_SNAKE_CASE__: Optional[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__: Union[str, Any]=   lambda snake_case_   : 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__: 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__   (						):
 SCREAMING_SNAKE_CASE__: Dict=   pyspark.sql.SparkSession.builder.master('''local[*]'''   ).appName('''pyspark'''   ).getOrCreate()
 SCREAMING_SNAKE_CASE__: str=   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__: 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__: 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__: 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       A__   (						):
 SCREAMING_SNAKE_CASE__: int=   pyspark.sql.SparkSession.builder.master('''local[*]'''   ).appName('''pyspark'''   ).getOrCreate()
 SCREAMING_SNAKE_CASE__: Optional[Any]=   spark.range(100   ).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() == 100



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() = }")
