
"""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


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

if TYPE_CHECKING:
				import pyspark




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






def 						_snake_case       (	lowercase__		:  "pyspark.sql.DataFrame"      ,     lowercase__		:  List[int]      ,     )  ->				Any:


			'''simple docstring'''





			import pyspark

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

			return generate_fn




class 			_SCREAMING_SNAKE_CASE					(			_BaseExamplesIterable							):






			def __init__(					self       ,   __A       ,   __A=None       ,   )			->     Optional[Any]:
						lowerCAmelCase_    :List[str]			     =  df
						lowerCAmelCase_    :str			     =  partition_order or range(self.df.rdd.getNumPartitions()       )
						lowerCAmelCase_    :int			     =  _generate_iterable_examples(self.df       ,   self.partition_order       )






			def __iter__(					self       )			->     Tuple:
						yield from self.generate_examples_fn()






			def  __lowerCAmelCase (					self       ,   __A       )			->     "SparkExamplesIterable":
						lowerCAmelCase_    :List[Any]			     =  list(range(self.df.rdd.getNumPartitions()       )       )
						generator.shuffle(__A       )
						return SparkExamplesIterable(self.df       ,   partition_order=__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       )			->     "SparkExamplesIterable":
						lowerCAmelCase_    :Optional[Any]			     =  self.split_shard_indices_by_worker(__A       ,   __A       )
						return SparkExamplesIterable(self.df       ,   partition_order=__A       )






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




class 			_SCREAMING_SNAKE_CASE					(			datasets.DatasetBuilder							):
			UpperCAmelCase_  :Optional[Any]				 =						SparkConfig






			def __init__(					self       ,   __A       ,   __A = None       ,   __A = None       ,   **__A       ,   )			->     int:
						import pyspark

						lowerCAmelCase_    :Tuple			     =  pyspark.sql.SparkSession.builder.getOrCreate()
						lowerCAmelCase_    :Union[str, Any]			     =  df
						lowerCAmelCase_    :Optional[Any]			     =  working_dir

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






			def  __lowerCAmelCase (					self       )			->     int:
						# Returns the path of the created file.
						def create_cache_and_write_probe(__A       ):
									# 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=__A       )
									lowerCAmelCase_    :Union[str, Any]			     =  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(__A       ,   """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:
									lowerCAmelCase_    :int			     =  (
									    self._spark.sparkContext.parallelize(range(1       )       ,   1       ).mapPartitions(__A       ).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  __lowerCAmelCase (					self       )			->     Optional[Any]:
						return datasets.DatasetInfo(features=self.config.features       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     Any:
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN       )]






			def  __lowerCAmelCase (					self       ,   __A       )			->     Union[str, Any]:
						import pyspark

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

						lowerCAmelCase_    :Tuple			     =  self.df.count()
						lowerCAmelCase_    :Union[str, Any]			     =  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.
						lowerCAmelCase_    :Tuple			     =  (
						    self.df.limit(__A       )
						    .repartition(1       )
						    .mapInArrow(__A       ,   """batch_bytes: long"""       )
						    .agg(pyspark.sql.functions.sum("""batch_bytes"""       ).alias("""sample_bytes"""       )       )
						    .collect()[0]
						    .sample_bytes
						    / sample_num_rows
						)
						lowerCAmelCase_    :List[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.
									lowerCAmelCase_    :str			     =  min(__A       ,   int(approx_total_size / max_shard_size       )       )
									lowerCAmelCase_    :Optional[int]			     =  self.df.repartition(__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   )			->     Iterable[Tuple[int, bool, Union[int, tuple]]]:
						import pyspark

						lowerCAmelCase_    :Optional[int]			     =  ParquetWriter if file_format == """parquet""" else ArrowWriter
						lowerCAmelCase_    :Dict			     =  os.path.join(self._working_dir       ,   os.path.basename(__A       )       ) if self._working_dir else fpath
						lowerCAmelCase_    :Optional[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.
						lowerCAmelCase_    :List[str]			     =  self.config.features
						lowerCAmelCase_    :List[Any]			     =  self._writer_batch_size
						lowerCAmelCase_    :str			     =  self._fs.storage_options

						def write_arrow(__A       ):
									# Within the same SparkContext, no two task attempts will share the same attempt ID.
									lowerCAmelCase_    :Dict			     =  pyspark.TaskContext().taskAttemptId()
									lowerCAmelCase_    :int			     =  next(__A       ,   __A       )
									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"""]       ,   )
									lowerCAmelCase_    :Tuple			     =  0
									lowerCAmelCase_    :List[str]			     =  writer_class(
									    features=__A       ,   path=working_fpath.replace("""SSSSS"""       ,   f"""{shard_id:05d}"""       ).replace("""TTTTT"""       ,   f"""{task_id:05d}"""       )       ,   writer_batch_size=__A       ,   storage_options=__A       ,   embed_local_files=__A       ,   )
									lowerCAmelCase_    :int			     =  pa.Table.from_batches([first_batch]       )
									writer.write_table(__A       )
									for batch in it:
												if max_shard_size is not None and writer._num_bytes >= max_shard_size:
															lowerCAmelCase_     ,       lowerCAmelCase_    :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
															lowerCAmelCase_    :int			     =  writer_class(
															    features=writer._features       ,   path=working_fpath.replace("""SSSSS"""       ,   f"""{shard_id:05d}"""       ).replace("""TTTTT"""       ,   f"""{task_id:05d}"""       )       ,   writer_batch_size=__A       ,   storage_options=__A       ,   embed_local_files=__A       ,   )
												lowerCAmelCase_    :Any			     =  pa.Table.from_batches([batch]       )
												writer.write_table(__A       )

									if writer._num_bytes > 0:
												lowerCAmelCase_     ,       lowerCAmelCase_    :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(__A       )       ):
															lowerCAmelCase_    :Optional[int]			     =  os.path.join(os.path.dirname(__A       )       ,   os.path.basename(__A       )       )
															shutil.move(__A       ,   __A       )

						lowerCAmelCase_    :Optional[int]			     =  (
						    self.df.mapInArrow(__A       ,   """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  __lowerCAmelCase (					self       ,   __A       ,   __A = "arrow"       ,   __A = None       ,   __A = None       ,   **__A       ,   )			->     Any:
						self._validate_cache_dir()

						lowerCAmelCase_    :Tuple			     =  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE       )
						self._repartition_df_if_needed(__A       )
						lowerCAmelCase_    :Optional[Any]			     =  not is_remote_filesystem(self._fs       )
						lowerCAmelCase_    :Tuple			     =  os.path.join if is_local else posixpath.join

						lowerCAmelCase_    :List[Any]			     =  """-TTTTT-SSSSS-of-NNNNN"""
						lowerCAmelCase_    :int			     =  f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
						lowerCAmelCase_    :Optional[Any]			     =  path_join(self._output_dir       ,   __A       )

						lowerCAmelCase_    :Dict			     =  0
						lowerCAmelCase_    :Any			     =  0
						lowerCAmelCase_    :str			     =  0
						lowerCAmelCase_    :Union[str, Any]			     =  []
						lowerCAmelCase_    :List[str]			     =  []

						for task_id, content in self._prepare_split_single(__A       ,   __A       ,   __A       ):
									(
									    (
									    lowerCAmelCase_
									)     ,       (
									    lowerCAmelCase_
									)     ,       (
									    lowerCAmelCase_
									)     ,       (
									    lowerCAmelCase_
									)     ,       
									)    :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(__A       )

						lowerCAmelCase_    :Optional[int]			     =  total_num_examples
						lowerCAmelCase_    :Tuple			     =  total_num_bytes

						# should rename everything at the end
						logger.debug(f"""Renaming {total_shards} shards."""       )
						if total_shards > 1:
									lowerCAmelCase_    :Any			     =  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.
									lowerCAmelCase_    :List[str]			     =  self._fs

									# use the -SSSSS-of-NNNNN pattern
									def _rename_shard(
									    __A       ,   __A       ,   __A       ,   ):
												rename(
												    __A       ,   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}"""       )       ,   )

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






			def  __lowerCAmelCase (					self       ,   __A       ,   )			->     SparkExamplesIterable:
						return SparkExamplesIterable(self.df       )









from __future__ import annotations

from collections.abc import Generator


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{}
						snake_case_   =	2
						while True:
												snake_case_   =	factor_map.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												if factor:
																		snake_case_   =	factor + prime
																		while x in factor_map:
																								x += factor
																		snake_case_   =	factor
												else:
																		snake_case_   =	prime
																		yield prime
												prime += 1


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 1E10       ):
						snake_case_   =	sieve()

						snake_case_   =	1
						while True:
												snake_case_   =	next(SCREAMING_SNAKE_CASE__       )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(SCREAMING_SNAKE_CASE__       )
												n += 2


if __name__ == "__main__":
					print(solution())