


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 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		List[str]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for part_id in partition_order:
														_UpperCAmelCase					=      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 	lowerCAmelCase__			(      )  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(1_0_0	).repartition(1	)
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      )  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(1_0	).repartition(2	)
							_UpperCAmelCase					=      [1, 0]
							_UpperCAmelCase					=      _generate_iterable_examples(a__      ,  a__	)  # Reverse the partitions.
							_UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  a__	)

							for i, (row_id, row_dict) in enumerate(generate_fn()	):
														_UpperCAmelCase		,	_UpperCAmelCase					=      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 	lowerCAmelCase__			(      )  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(1_0	).repartition(1	)
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      )  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(3_0	).repartition(3	)
							# Mock the generator so that shuffle reverses the partition indices.
							with patch('numpy.random.Generator'	) as generator_mock:
														_UpperCAmelCase					=      lambda a__	: x.reverse()
														_UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [2, 1, 0]	)

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


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(2_0	).repartition(4	)

							# Partitions 0 and 2
							_UpperCAmelCase					=      SparkExamplesIterable(a__	).shard_data_sources(worker_id=0      ,  num_workers=2	)
							assert shard_it_a.n_shards == 2
							_UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [0, 2]	)
							for i, (row_id, row_dict) in enumerate(a__	):
														_UpperCAmelCase		,	_UpperCAmelCase					=      expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							_UpperCAmelCase					=      SparkExamplesIterable(a__	).shard_data_sources(worker_id=1      ,  num_workers=2	)
							assert shard_it_a.n_shards == 2
							_UpperCAmelCase					=      _get_expected_row_ids_and_row_dicts_for_partition_order(a__      ,  [1, 3]	)
							for i, (row_id, row_dict) in enumerate(a__	):
														_UpperCAmelCase		,	_UpperCAmelCase					=      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 	lowerCAmelCase__			(      )  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      pyspark.sql.SparkSession.builder.master('local[*]'	).appName('pyspark'	).getOrCreate()
							_UpperCAmelCase					=      spark.range(1_0_0	).repartition(1	)
							_UpperCAmelCase					=      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




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
