
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 (      lowercase					, lowercase				)     ->						Dict:
		'''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(lowercase				):
						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 (      )     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
		UpperCamelCase						=  spark.range(100				).repartition(1				)
		UpperCamelCase						=  Spark(lowercase				)
		# 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 (      )     ->						Dict:
		'''simple docstring'''

		UpperCamelCase						=  pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
		UpperCamelCase						=  spark.range(10				).repartition(2				)
		UpperCamelCase						=  [1, 0]
		UpperCamelCase						=  _generate_iterable_examples(lowercase					, lowercase				)  # Reverse the partitions.
		UpperCamelCase						=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase					, lowercase				)

		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    A (      )     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
		UpperCamelCase						=  spark.range(10				).repartition(1				)
		UpperCamelCase						=  SparkExamplesIterable(lowercase				)
		assert it.n_shards == 1
		for i, (row_id, row_dict) in enumerate(lowercase				):
				assert row_id == f'''0_{i}'''
				assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def    A (      )     ->						str:
		'''simple docstring'''

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

				UpperCamelCase						=  SparkExamplesIterable(lowercase				).shuffle_data_sources(lowercase				)
				assert shuffled_it.n_shards == 3
				for i, (row_id, row_dict) in enumerate(lowercase				):
						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    A (      )     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
		UpperCamelCase						=  spark.range(20				).repartition(4				)

		# Partitions 0 and 2
		UpperCamelCase						=  SparkExamplesIterable(lowercase				).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(lowercase					, [0, 2]				)
		for i, (row_id, row_dict) in enumerate(lowercase				):
				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(lowercase				).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(lowercase					, [1, 3]				)
		for i, (row_id, row_dict) in enumerate(lowercase				):
				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    A (      )     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
		UpperCamelCase						=  spark.range(100				).repartition(1				)
		UpperCamelCase						=  Spark(lowercase				)
		# 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


import os

_UpperCAmelCase      :				int          = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		UpperCamelCase						=  0
		while index < len(lowercase				) - 1:
				UpperCamelCase						=  SYMBOLS[numerals[index]]
				UpperCamelCase						=  SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''

		UpperCamelCase						=  num // 1_000
		numerals += m_count * "M"
		num %= 1_000

		UpperCamelCase						=  num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		UpperCamelCase						=  num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals


def    A (      lowercase = "/p089_roman.txt"				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		with open(os.path.dirname(lowercase				) + roman_numerals_filename				) as filea:
				UpperCamelCase						=  filea.readlines()

		for line in lines:
				UpperCamelCase						=  line.strip()
				UpperCamelCase						=  parse_roman_numerals(lowercase				)
				UpperCamelCase						=  generate_roman_numerals(lowercase				)
				savings += len(lowercase				) - len(lowercase				)

		return savings


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