





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


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

if TYPE_CHECKING:
			import pyspark






@dataclass
class       __A					(					datasets.BuilderConfig				):

			'''simple docstring'''







			lowerCAmelCase_							  =		None





def lowerCAmelCase__(__snake_case ,__snake_case ,)       ->   Any:







	'''simple docstring'''







	import pyspark

	def generate_fn():
		lowerCamelCase__              =	df.select('''*''' ,pyspark.sql.functions.spark_partition_id().alias('''part_id'''							)							)
		for partition_id in partition_order:
			lowerCamelCase__              =	df_with_partition_id.select('''*'''							).where(F'part_id = {partition_id}'							).drop('''part_id'''							)
			lowerCamelCase__              =	partition_df.collect()
			lowerCamelCase__              =	0
			for row in rows:
				yield F'{partition_id}_{row_id}', row.asDict()
				row_id += 1

	return generate_fn






class       __A					(					_BaseExamplesIterable				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						):



				'''simple docstring'''







				lowerCamelCase__              =	df
				lowerCamelCase__              =	partition_order or range(self.df.rdd.getNumPartitions()							)
				lowerCamelCase__              =	_generate_iterable_examples(self.df ,						self.partition_order							)





			def __iter__(					self							):



				'''simple docstring'''







				yield from self.generate_examples_fn()





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	list(range(self.df.rdd.getNumPartitions()							)							)
				generator.shuffle(__lowerCAmelCase							)
				return SparkExamplesIterable(self.df ,						partition_order=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.split_shard_indices_by_worker(__lowerCAmelCase ,						__lowerCAmelCase							)
				return SparkExamplesIterable(self.df ,						partition_order=__lowerCAmelCase							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return len(self.partition_order							)






class       __A					(					datasets.DatasetBuilder				):

			'''simple docstring'''







			lowerCAmelCase_							  =		SparkConfig





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				import pyspark

				lowerCamelCase__              =	pyspark.sql.SparkSession.builder.getOrCreate()
				lowerCamelCase__              =	df
				lowerCamelCase__              =	working_dir

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				def create_cache_and_write_probe(__lowerCAmelCase							):
					# 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=__lowerCAmelCase							)
					lowerCamelCase__              =	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(__lowerCAmelCase ,						'''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__              =	(
					    self._spark.sparkContext.parallelize(range(1							) ,						1							).mapPartitions(__lowerCAmelCase							).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							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				import pyspark

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

				lowerCamelCase__              =	self.df.count()
				lowerCamelCase__              =	df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
				# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
				lowerCamelCase__              =	(
				    self.df.limit(__lowerCAmelCase							)
				    .repartition(1							)
				    .mapInArrow(__lowerCAmelCase ,						'''batch_bytes: long'''							)
				    .agg(pyspark.sql.functions.sum('''batch_bytes'''							).alias('''sample_bytes'''							)							)
				    .collect()[0]
				    .sample_bytes
				    / sample_num_rows
				)
				lowerCamelCase__              =	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__              =	min(__lowerCAmelCase ,						int(approx_total_size / max_shard_size							)							)
					lowerCamelCase__              =	self.df.repartition(__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				import pyspark

				lowerCamelCase__              =	ParquetWriter if file_format == '''parquet''' else ArrowWriter
				lowerCamelCase__              =	os.path.join(self._working_dir ,						os.path.basename(__lowerCAmelCase							)							) if self._working_dir else fpath
				lowerCamelCase__              =	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__              =	self.config.features
				lowerCamelCase__              =	self._writer_batch_size
				lowerCamelCase__              =	self._fs.storage_options

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

					if writer._num_bytes > 0:
						lowerCamelCase__       ,			lowerCamelCase__              =	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(__lowerCAmelCase							)							):
							lowerCamelCase__              =	os.path.join(os.path.dirname(__lowerCAmelCase							) ,						os.path.basename(__lowerCAmelCase							)							)
							shutil.move(__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	(
				    self.df.mapInArrow(__lowerCAmelCase ,						'''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 ,						__lowerCAmelCase ,						__lowerCAmelCase = "arrow" ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				self._validate_cache_dir()

				lowerCamelCase__              =	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
				self._repartition_df_if_needed(__lowerCAmelCase							)
				lowerCamelCase__              =	not is_remote_filesystem(self._fs							)
				lowerCamelCase__              =	os.path.join if is_local else posixpath.join

				lowerCamelCase__              =	'''-TTTTT-SSSSS-of-NNNNN'''
				lowerCamelCase__              =	F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
				lowerCamelCase__              =	path_join(self._output_dir ,						__lowerCAmelCase							)

				lowerCamelCase__              =	0
				lowerCamelCase__              =	0
				lowerCamelCase__              =	0
				lowerCamelCase__              =	[]
				lowerCamelCase__              =	[]

				for task_id, content in self._prepare_split_single(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):
					(
					    (
					    lowerCamelCase__
					)       ,			(
					    lowerCamelCase__
					)       ,			(
					    lowerCamelCase__
					)       ,			(
					    lowerCamelCase__
					)       ,			
					)              =	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(__lowerCAmelCase							)

				lowerCamelCase__              =	total_num_examples
				lowerCamelCase__              =	total_num_bytes

				# should rename everything at the end
				logger.debug(F'Renaming {total_shards} shards.'							)
				if total_shards > 1:
					lowerCamelCase__              =	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__              =	self._fs

					# use the -SSSSS-of-NNNNN pattern
					def _rename_shard(
					    __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):
						rename(
						    __lowerCAmelCase ,						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__              =	[]
					lowerCamelCase__              =	0
					for i in range(len(__lowerCAmelCase							)							):
						lowerCamelCase__       ,			lowerCamelCase__              =	task_id_and_num_shards[i]
						for shard_id in range(__lowerCAmelCase							):
							args.append([task_id, shard_id, global_shard_id]							)
							global_shard_id += 1
					self._spark.sparkContext.parallelize(__lowerCAmelCase ,						len(__lowerCAmelCase							)							).map(lambda __lowerCAmelCase							: _rename_shard(*__lowerCAmelCase							)							).collect()
				else:
					# don't use any pattern
					lowerCamelCase__              =	0
					lowerCamelCase__              =	task_id_and_num_shards[0][0]
					self._rename(
					    fpath.replace('''SSSSS''' ,						F'{shard_id:05d}'							).replace('''TTTTT''' ,						F'{task_id:05d}'							) ,						fpath.replace(__lowerCAmelCase ,						''''''							) ,						)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				return SparkExamplesIterable(self.df							)










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if start is None:
		lowerCamelCase__              =	0

	if end is None:
		lowerCamelCase__              =	len(__snake_case							) - 1

	if start >= end:
		return

	lowerCamelCase__              =	(start + end) // 2

	slowsort(__snake_case ,__snake_case ,__snake_case							)
	slowsort(__snake_case ,mid + 1 ,__snake_case							)

	if sequence[end] < sequence[mid]:
		lowerCamelCase__       ,			lowerCamelCase__              =	sequence[mid], sequence[end]

	slowsort(__snake_case ,__snake_case ,end - 1							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



