






"""simple docstring"""







import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_lowerCAmelCase            =	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark


@dataclass
class      UpperCamelCase		(datasets.BuilderConfig     ):
						_SCREAMING_SNAKE_CASE  : Optional[datasets.Features]												=		None


def      UpperCamelCase       ( _A  ,				_A  ,				)     ->       Optional[int]:
				import pyspark

				def generate_fn():
								lowercase    :      Dict       = df.select("""*"""  ,				pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
								for partition_id in partition_order:
												lowercase    :      Any       = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
												lowercase    :      List[Any]       = partition_df.collect()
												lowercase    :      Optional[int]       = 0
												for row in rows:
																yield F"""{partition_id}_{row_id}""", row.asDict()
																row_id += 1

				return generate_fn


class      UpperCamelCase		(_BaseExamplesIterable     ):






						def __init__(		self  :Any			, __magic_name__  :"pyspark.sql.DataFrame"			, __magic_name__  :Union[str, Any]=None			, )      ->List[Any]:
										lowercase    :      List[Any]       = df
										lowercase    :      Union[str, Any]       = partition_order or range(self.df.rdd.getNumPartitions()				)
										lowercase    :      Dict       = _generate_iterable_examples(self.df			, self.partition_order				)






						def __iter__(		self  :List[str]				)      ->List[str]:
										yield from self.generate_examples_fn()






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.random.Generator				)      ->"SparkExamplesIterable":
										lowercase    :      Optional[Any]       = list(range(self.df.rdd.getNumPartitions()				)				)
										generator.shuffle(__magic_name__				)
										return SparkExamplesIterable(self.df			, partition_order=__magic_name__				)






						def      __snake_case      (		self  :Any			, __magic_name__  :int			, __magic_name__  :int				)      ->"SparkExamplesIterable":
										lowercase    :      Dict       = self.split_shard_indices_by_worker(__magic_name__			, __magic_name__				)
										return SparkExamplesIterable(self.df			, partition_order=__magic_name__				)






						@property
						def      __snake_case      (		self  :int				)      ->int:
										return len(self.partition_order				)


class      UpperCamelCase		(datasets.DatasetBuilder     ):
						_SCREAMING_SNAKE_CASE  : Tuple												=		SparkConfig






						def __init__(		self  :Dict			, __magic_name__  :"pyspark.sql.DataFrame"			, __magic_name__  :str = None			, __magic_name__  :str = None			, **__magic_name__  :str			, )      ->str:
										import pyspark

										lowercase    :      List[str]       = pyspark.sql.SparkSession.builder.getOrCreate()
										lowercase    :      int       = df
										lowercase    :      Optional[int]       = working_dir

										super().__init__(
										    cache_dir=__magic_name__			, config_name=str(self.df.semanticHash()				)			, **__magic_name__			, )






						def      __snake_case      (		self  :Optional[Any]				)      ->Any:
										# Returns the path of the created file.
										def create_cache_and_write_probe(__magic_name__  :List[Any]				):
														# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
														# already exist.
														os.makedirs(self._cache_dir			, exist_ok=__magic_name__				)
														lowercase    :      Dict       = os.path.join(self._cache_dir			, """fs_test""" + uuid.uuida().hex				)
														# Opening the file in append mode will create a new file unless it already exists, in which case it will not
														# change the file contents.
														open(__magic_name__			, """a"""				)
														return [probe_file]

										if self._spark.conf.get("""spark.master"""			, """"""				).startswith("""local"""				):
														return

										# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
										# accessible to the driver.
										# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
										if self._cache_dir:
														lowercase    :      Tuple       = (
														    self._spark.sparkContext.parallelize(range(1				)			, 1				).mapPartitions(__magic_name__				).collect()
														)
														if os.path.isfile(probe[0]				):
																		return

										raise ValueError(
										    """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""				)






						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[int]:
										return datasets.DatasetInfo(features=self.config.features				)






						def      __snake_case      (		self  :int			, __magic_name__  :datasets.download.download_manager.DownloadManager				)      ->str:
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]






						def      __snake_case      (		self  :Any			, __magic_name__  :int				)      ->Any:
										import pyspark

										def get_arrow_batch_size(__magic_name__  :Any				):
														for batch in it:
																		yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]}				)

										lowercase    :      Tuple       = self.df.count()
										lowercase    :      List[str]       = df_num_rows if df_num_rows <= 100 else 100
										# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
										lowercase    :      Optional[Any]       = (
										    self.df.limit(__magic_name__				)
										    .repartition(1				)
										    .mapInArrow(__magic_name__			, """batch_bytes: long"""				)
										    .agg(pyspark.sql.functions.sum("""batch_bytes"""				).alias("""sample_bytes"""				)				)
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										lowercase    :      Any       = approx_bytes_per_row * df_num_rows
										if approx_total_size > max_shard_size:
														# Make sure there is at least one row per partition.
														lowercase    :      Optional[Any]       = min(__magic_name__			, int(approx_total_size / max_shard_size				)				)
														lowercase    :      Dict       = self.df.repartition(__magic_name__				)






						def      __snake_case      (		self  :str			, __magic_name__  :str			, __magic_name__  :str			, __magic_name__  :int			, )      ->Iterable[Tuple[int, bool, Union[int, tuple]]]:
										import pyspark

										lowercase    :      int       = ParquetWriter if file_format == """parquet""" else ArrowWriter
										lowercase    :      Optional[Any]       = os.path.join(self._working_dir			, os.path.basename(__magic_name__				)				) if self._working_dir else fpath
										lowercase    :      List[Any]       = file_format == """parquet"""

										# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
										# pickling the SparkContext.
										lowercase    :      Tuple       = self.config.features
										lowercase    :      List[str]       = self._writer_batch_size
										lowercase    :      Any       = self._fs.storage_options

										def write_arrow(__magic_name__  :str				):
														# Within the same SparkContext, no two task attempts will share the same attempt ID.
														lowercase    :      Tuple       = pyspark.TaskContext().taskAttemptId()
														lowercase    :      Union[str, Any]       = next(__magic_name__			, __magic_name__				)
														if first_batch is None:
																		# Some partitions might not receive any data.
																		return pa.RecordBatch.from_arrays(
																		    [[task_id], [0], [0]]			, names=["""task_id""", """num_examples""", """num_bytes"""]			, )
														lowercase    :      Optional[Any]       = 0
														lowercase    :      Optional[int]       = writer_class(
														    features=__magic_name__			, path=working_fpath.replace("""SSSSS"""			, f"""{shard_id:05d}"""				).replace("""TTTTT"""			, f"""{task_id:05d}"""				)			, writer_batch_size=__magic_name__			, storage_options=__magic_name__			, embed_local_files=__magic_name__			, )
														lowercase    :      List[Any]       = pa.Table.from_batches([first_batch]				)
														writer.write_table(__magic_name__				)
														for batch in it:
																		if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																						lowercase      ,      lowercase    :      Optional[int]       = writer.finalize()
																						writer.close()
																						yield pa.RecordBatch.from_arrays(
																						    [[task_id], [num_examples], [num_bytes]]			, names=["""task_id""", """num_examples""", """num_bytes"""]			, )
																						shard_id += 1
																						lowercase    :      Tuple       = writer_class(
																						    features=writer._features			, path=working_fpath.replace("""SSSSS"""			, f"""{shard_id:05d}"""				).replace("""TTTTT"""			, f"""{task_id:05d}"""				)			, writer_batch_size=__magic_name__			, storage_options=__magic_name__			, embed_local_files=__magic_name__			, )
																		lowercase    :      Optional[Any]       = pa.Table.from_batches([batch]				)
																		writer.write_table(__magic_name__				)

														if writer._num_bytes > 0:
																		lowercase      ,      lowercase    :      Any       = writer.finalize()
																		writer.close()
																		yield pa.RecordBatch.from_arrays(
																		    [[task_id], [num_examples], [num_bytes]]			, names=["""task_id""", """num_examples""", """num_bytes"""]			, )

														if working_fpath != fpath:
																		for file in os.listdir(os.path.dirname(__magic_name__				)				):
																						lowercase    :      Optional[Any]       = os.path.join(os.path.dirname(__magic_name__				)			, os.path.basename(__magic_name__				)				)
																						shutil.move(__magic_name__			, __magic_name__				)

										lowercase    :      List[str]       = (
										    self.df.mapInArrow(__magic_name__			, """task_id: long, num_examples: long, num_bytes: long"""				)
										    .groupBy("""task_id"""				)
										    .agg(
										        pyspark.sql.functions.sum("""num_examples"""				).alias("""total_num_examples"""				)			, pyspark.sql.functions.sum("""num_bytes"""				).alias("""total_num_bytes"""				)			, pyspark.sql.functions.count("""num_bytes"""				).alias("""num_shards"""				)			, pyspark.sql.functions.collect_list("""num_examples"""				).alias("""shard_lengths"""				)			, )
										    .collect()
										)
										for row in stats:
														yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






						def      __snake_case      (		self  :Dict			, __magic_name__  :"datasets.SplitGenerator"			, __magic_name__  :str = "arrow"			, __magic_name__  :Optional[Union[str, int]] = None			, __magic_name__  :Optional[int] = None			, **__magic_name__  :Dict			, )      ->Union[str, Any]:
										self._validate_cache_dir()

										lowercase    :      str       = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE				)
										self._repartition_df_if_needed(__magic_name__				)
										lowercase    :      str       = not is_remote_filesystem(self._fs				)
										lowercase    :      int       = os.path.join if is_local else posixpath.join

										lowercase    :      Union[str, Any]       = """-TTTTT-SSSSS-of-NNNNN"""
										lowercase    :      List[Any]       = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
										lowercase    :      Optional[Any]       = path_join(self._output_dir			, __magic_name__				)

										lowercase    :      str       = 0
										lowercase    :      Tuple       = 0
										lowercase    :      Tuple       = 0
										lowercase    :      List[str]       = []
										lowercase    :      str       = []

										for task_id, content in self._prepare_split_single(__magic_name__			, __magic_name__			, __magic_name__				):
														(
														    (
														    lowercase
														)      ,      (
														    lowercase
														)      ,      (
														    lowercase
														)      ,      (
														    lowercase
														)      ,      
														)    :      List[Any]       = content
														if num_bytes > 0:
																		total_num_examples += num_examples
																		total_num_bytes += num_bytes
																		total_shards += num_shards
																		task_id_and_num_shards.append((task_id, num_shards)				)
																		all_shard_lengths.extend(__magic_name__				)

										lowercase    :      str       = total_num_examples
										lowercase    :      Optional[Any]       = total_num_bytes

										# should rename everything at the end
										logger.debug(f"""Renaming {total_shards} shards."""				)
										if total_shards > 1:
														lowercase    :      Optional[int]       = all_shard_lengths

														# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
														# pickling error due to pickling the SparkContext.
														lowercase    :      Optional[int]       = self._fs

														# use the -SSSSS-of-NNNNN pattern
														def _rename_shard(
														    __magic_name__  :int			, __magic_name__  :int			, __magic_name__  :int			, ):
																		rename(
																		    __magic_name__			, fpath.replace("""SSSSS"""			, f"""{shard_id:05d}"""				).replace("""TTTTT"""			, f"""{task_id:05d}"""				)			, fpath.replace("""TTTTT-SSSSS"""			, f"""{global_shard_id:05d}"""				).replace("""NNNNN"""			, f"""{total_shards:05d}"""				)			, )

														lowercase    :      str       = []
														lowercase    :      Optional[Any]       = 0
														for i in range(len(__magic_name__				)				):
																		lowercase      ,      lowercase    :      Optional[int]       = task_id_and_num_shards[i]
																		for shard_id in range(__magic_name__				):
																						args.append([task_id, shard_id, global_shard_id]				)
																						global_shard_id += 1
														self._spark.sparkContext.parallelize(__magic_name__			, len(__magic_name__				)				).map(lambda __magic_name__				: _rename_shard(*__magic_name__				)				).collect()
										else:
														# don't use any pattern
														lowercase    :      str       = 0
														lowercase    :      Tuple       = task_id_and_num_shards[0][0]
														self._rename(
														    fpath.replace("""SSSSS"""			, f"""{shard_id:05d}"""				).replace("""TTTTT"""			, f"""{task_id:05d}"""				)			, fpath.replace(__magic_name__			, """"""				)			, )






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :"datasets.SplitGenerator"			, )      ->SparkExamplesIterable:
										return SparkExamplesIterable(self.df				)








"""simple docstring"""







def      UpperCamelCase       ( _A )     ->       int:
				lowercase    :      Dict       = 0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum


def      UpperCamelCase       ( _A = 100 )     ->       int:
				lowercase    :      Union[str, Any]       = 1
				lowercase    :      Tuple       = 2

				for i in range(2  ,				max_n + 1 ):
								lowercase    :      Any       = pre_numerator
								lowercase    :      Dict       = 2 * i // 3 if i % 3 == 0 else 1
								lowercase    :      Optional[Any]       = cur_numerator
								lowercase    :      str       = e_cont * pre_numerator + temp

				return sum_digits(_A )


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