


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   (							A__      ,    A__							)	-> Tuple:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		[]
      for part_id in partition_order:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		df.where(f"""SPARK_PARTITION_ID() = {part_id}"""							).collect()
            for row_idx, row in enumerate(A__							):
                  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   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		spark.range(1_0_0							).repartition(1							)
      SCREAMING_SNAKE_CASE__       :  str						     =		Spark(A__							)
      # 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    a   (							)	-> List[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  int						     =		spark.range(1_0							).repartition(2							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[1, 0]
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		_generate_iterable_examples(A__      ,    A__							)  # Reverse the partitions.
      SCREAMING_SNAKE_CASE__       :  Dict						     =		_get_expected_row_ids_and_row_dicts_for_partition_order(A__      ,    A__							)

      for i, (row_id, row_dict) in enumerate(generate_fn()							):
            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   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  Dict						     =		spark.range(1_0							).repartition(1							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		SparkExamplesIterable(A__							)
      assert it.n_shards == 1
      for i, (row_id, row_dict) in enumerate(A__							):
            assert row_id == f"""0_{i}"""
            assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def    a   (							)	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  str						     =		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__       :  Optional[int]						     =		lambda A__							: x.reverse()
            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		_get_expected_row_ids_and_row_dicts_for_partition_order(A__      ,    [2, 1, 0]							)

            SCREAMING_SNAKE_CASE__       :  Any						     =		SparkExamplesIterable(A__							).shuffle_data_sources(A__							)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(A__							):
                  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   (							)	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		spark.range(2_0							).repartition(4							)

      # Partitions 0 and 2
      SCREAMING_SNAKE_CASE__       :  int						     =		SparkExamplesIterable(A__							).shard_data_sources(worker_id=0      ,    num_workers=2							)
      assert shard_it_a.n_shards == 2
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		_get_expected_row_ids_and_row_dicts_for_partition_order(A__      ,    [0, 2]							)
      for i, (row_id, row_dict) in enumerate(A__							):
            SCREAMING_SNAKE_CASE__       :  int						     =		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(A__							).shard_data_sources(worker_id=1      ,    num_workers=2							)
      assert shard_it_a.n_shards == 2
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		_get_expected_row_ids_and_row_dicts_for_partition_order(A__      ,    [1, 3]							)
      for i, (row_id, row_dict) in enumerate(A__							):
            SCREAMING_SNAKE_CASE__       :  List[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   (							)	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		pyspark.sql.SparkSession.builder.master('''local[*]'''							).appName('''pyspark'''							).getOrCreate()
      SCREAMING_SNAKE_CASE__       :  int						     =		spark.range(1_0_0							).repartition(1							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		Spark(A__							)
      # 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










'''simple docstring'''






from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__A   :    Tuple											= 10







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  for i in range(A__				,       A__   ):
    if array[i] == target:
      return i
  return -1







def 							UpperCamelCase_     (       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  0
  lowerCAmelCase_     : Union[str, Any]     =  len(A__   )
  while left <= right:
    if right - left < precision:
      return lin_search(A__				,       A__				,       A__				,       A__   )

    lowerCAmelCase_     : Dict     =  (left + right) // 3 + 1
    lowerCAmelCase_     : List[Any]     =  2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowerCAmelCase_     : List[str]     =  one_third - 1
    elif array[two_third] < target:
      lowerCAmelCase_     : Tuple     =  two_third + 1

    else:
      lowerCAmelCase_     : Tuple     =  one_third + 1
      lowerCAmelCase_     : Any     =  two_third - 1
  else:
    return -1







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  list[int]				,       A__   :  int   ):
  '''simple docstring'''





  if left < right:
    if right - left < precision:
      return lin_search(A__				,       A__				,       A__				,       A__   )
    lowerCAmelCase_     : Dict     =  (left + right) // 3 + 1
    lowerCAmelCase_     : Dict     =  2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(A__				,       one_third - 1				,       A__				,       A__   )
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1				,       A__				,       A__				,       A__   )
    else:
      return rec_ternary_search(one_third + 1				,       two_third - 1				,       A__				,       A__   )
  else:
    return -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A   :    List[Any]											= input("Enter numbers separated by comma:\n").strip()
  __A   :    Union[str, Any]											= [int(item.strip()) for item in user_input.split(",")]
  assert collection == sorted(collection), F"List must be ordered.\n{collection}."
  __A   :    List[Any]											= int(input("Enter the number to be found in the list:\n").strip())
  __A   :    str											= ite_ternary_search(collection, target)
  __A   :    Dict											= rec_ternary_search(0, len(collection) - 1, collection, target)
  if resulta != -1:
    print(F'''Iterative search: {target} found at positions: {resulta}''')
    print(F'''Recursive search: {target} found at positions: {resulta}''')
  else:
    print("Not found")



