

"""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


snake_case_   :   List[str]        =       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
	import pyspark







@dataclass
class       snake_case__  ( datasets.BuilderConfig     ):
			SCREAMING_SNAKE_CASE__     				=						None



def   lowercase_	(    _lowercase       :		"pyspark.sql.DataFrame"							,      _lowercase       :		List[int]							,      ):




					'''simple docstring'''






					import pyspark

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

					return generate_fn







class       snake_case__  ( _BaseExamplesIterable     ):







			def __init__(			self			: Optional[int]			,  lowercase			: "pyspark.sql.DataFrame"			,  lowercase			: Optional[int]=None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				df
								UpperCAmelCase			:   str      =				partition_order or range(self.df.rdd.getNumPartitions()  )
								UpperCAmelCase			:   str      =				_generate_iterable_examples(self.df			,  self.partition_order  )







			def __iter__(			self			: List[Any]  ):





								'''simple docstring'''



								yield from self.generate_examples_fn()







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: np.random.Generator  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				list(range(self.df.rdd.getNumPartitions()  )  )
								generator.shuffle(lowercase  )
								return SparkExamplesIterable(self.df			,  partition_order=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: int			,  lowercase			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.split_shard_indices_by_worker(lowercase			,  lowercase  )
								return SparkExamplesIterable(self.df			,  partition_order=lowercase  )







			@property
			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								return len(self.partition_order  )







class       snake_case__  ( datasets.DatasetBuilder     ):
			SCREAMING_SNAKE_CASE__     				=						SparkConfig







			def __init__(			self			: List[Any]			,  lowercase			: "pyspark.sql.DataFrame"			,  lowercase			: str = None			,  lowercase			: str = None			,  **lowercase			: Union[str, Any]			,  ):





								'''simple docstring'''



								import pyspark

								UpperCAmelCase			:   List[str]      =				pyspark.sql.SparkSession.builder.getOrCreate()
								UpperCAmelCase			:   Dict      =				df
								UpperCAmelCase			:   str      =				working_dir

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







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								def create_cache_and_write_probe(lowercase			: 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=lowercase  )
													UpperCAmelCase			:   List[str]      =				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(lowercase			,  "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:
													UpperCAmelCase			:   Union[str, Any]      =				(
													    self._spark.sparkContext.parallelize(range(1  )			,  1  ).mapPartitions(lowercase  ).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			: Any  ):





								'''simple docstring'''



								return datasets.DatasetInfo(features=self.config.features  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: datasets.download.download_manager.DownloadManager  ):





								'''simple docstring'''



								return [datasets.SplitGenerator(name=datasets.Split.TRAIN  )]







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								import pyspark

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

								UpperCAmelCase			:   List[Any]      =				self.df.count()
								UpperCAmelCase			:   Dict      =				df_num_rows if df_num_rows <= 1_00 else 1_00
								# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
								UpperCAmelCase			:   Optional[int]      =				(
								    self.df.limit(lowercase  )
								    .repartition(1  )
								    .mapInArrow(lowercase			,  "batch_bytes: long"  )
								    .agg(pyspark.sql.functions.sum("batch_bytes"  ).alias("sample_bytes"  )  )
								    .collect()[0]
								    .sample_bytes
								    / sample_num_rows
								)
								UpperCAmelCase			:   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.
													UpperCAmelCase			:   Union[str, Any]      =				min(lowercase			,  int(approx_total_size / max_shard_size  )  )
													UpperCAmelCase			:   List[Any]      =				self.df.repartition(lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: str			,  lowercase			: str			,  lowercase			: int			,  ):





								'''simple docstring'''



								import pyspark

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

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

													if writer._num_bytes > 0:
																		UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				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(lowercase  )  ):
																							UpperCAmelCase			:   Any      =				os.path.join(os.path.dirname(lowercase  )			,  os.path.basename(lowercase  )  )
																							shutil.move(lowercase			,  lowercase  )

								UpperCAmelCase			:   str      =				(
								    self.df.mapInArrow(lowercase			,  "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			: Any			,  lowercase			: "datasets.SplitGenerator"			,  lowercase			: str = "arrow"			,  lowercase			: Optional[Union[str, int]] = None			,  lowercase			: Optional[int] = None			,  **lowercase			: Any			,  ):





								'''simple docstring'''



								self._validate_cache_dir()

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

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

								UpperCAmelCase			:   Dict      =				0
								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				0
								UpperCAmelCase			:   Tuple      =				[]
								UpperCAmelCase			:   str      =				[]

								for task_id, content in self._prepare_split_single(lowercase			,  lowercase			,  lowercase  ):
													(
													    (
													    UpperCAmelCase
													)      ,							(
													    UpperCAmelCase
													)      ,							(
													    UpperCAmelCase
													)      ,							(
													    UpperCAmelCase
													)      ,							
													)			:   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(lowercase  )

								UpperCAmelCase			:   Tuple      =				total_num_examples
								UpperCAmelCase			:   Union[str, Any]      =				total_num_bytes

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

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

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







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: "datasets.SplitGenerator"			,  ):





								'''simple docstring'''



								return SparkExamplesIterable(self.df  )





"""simple docstring"""






import os



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				os.path.join(os.path.dirname(_lowercase	)							,      "num.txt"	)
					with open(_lowercase	) as file_hand:
										return str(sum(int(_lowercase	) for line in file_hand	)	)[:10]


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


