



'''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


a   :			Optional[int]					  =    datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
							import pyspark

@dataclass
class 			UpperCamelCase_ (							datasets.BuilderConfig			):
	lowercase          = None




def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				)      ->			List[str]:
							import pyspark

							def generate_fn():
														UpperCAmelCase     :		List[str]							     =		df.select("""*"""	,				pyspark.sql.functions.spark_partition_id().alias("""part_id"""		)		)
														for partition_id in partition_order:
																					UpperCAmelCase     :		Tuple							     =		df_with_partition_id.select("""*"""		).where(F'''part_id = {partition_id}'''		).drop("""part_id"""		)
																					UpperCAmelCase     :		Union[str, Any]							     =		partition_df.collect()
																					UpperCAmelCase     :		List[Any]							     =		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		,		A		,		A=None		,		)			->			List[str]:
								UpperCAmelCase     :		Union[str, Any]							     =		df
								UpperCAmelCase     :		List[str]							     =		partition_order or range(self.df.rdd.getNumPartitions()    )
								UpperCAmelCase     :		Optional[int]							     =		_generate_iterable_examples(self.df		,		self.partition_order    )





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





	def  _lowercase(  self		,		A    )			->			"SparkExamplesIterable":
								UpperCAmelCase     :		Any							     =		list(range(self.df.rdd.getNumPartitions()    )    )
								generator.shuffle(A    )
								return SparkExamplesIterable(self.df		,		partition_order=A    )





	def  _lowercase(  self		,		A		,		A    )			->			"SparkExamplesIterable":
								UpperCAmelCase     :		str							     =		self.split_shard_indices_by_worker(A		,		A    )
								return SparkExamplesIterable(self.df		,		partition_order=A    )






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




class 			UpperCamelCase_ (							datasets.DatasetBuilder			):
	lowercase          = SparkConfig





	def __init__(  self		,		A		,		A = None		,		A = None		,		**A		,		)			->			Optional[int]:
								import pyspark

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

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





	def  _lowercase(  self    )			->			str:
								# 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    )
															UpperCAmelCase     :		Optional[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:
															UpperCAmelCase     :		Dict							     =		(
															    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  _lowercase(  self    )			->			Tuple:
								return datasets.DatasetInfo(features=self.config.features    )





	def  _lowercase(  self		,		A    )			->			int:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN    )]





	def  _lowercase(  self		,		A    )			->			Optional[Any]:
								import pyspark

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

								UpperCAmelCase     :		Optional[Any]							     =		self.df.count()
								UpperCAmelCase     :		Optional[int]							     =		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.
								UpperCAmelCase     :		Optional[int]							     =		(
								    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
								)
								UpperCAmelCase     :		Union[str, 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     :		str							     =		min(A		,		int(approx_total_size / max_shard_size    )    )
															UpperCAmelCase     :		str							     =		self.df.repartition(A    )





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

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

								def write_arrow(A    ):
															# Within the same SparkContext, no two task attempts will share the same attempt ID.
															UpperCAmelCase     :		str							     =		pyspark.TaskContext().taskAttemptId()
															UpperCAmelCase     :		Optional[Any]							     =		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"""]		,		)
															UpperCAmelCase     :		List[str]							     =		0
															UpperCAmelCase     :		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		,		)
															UpperCAmelCase     :		Any							     =		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:
																													UpperCAmelCase     ,     UpperCAmelCase     :		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
																													UpperCAmelCase     :		Tuple							     =		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		,		)
																						UpperCAmelCase     :		Dict							     =		pa.Table.from_batches([batch]    )
																						writer.write_table(A    )

															if writer._num_bytes > 0:
																						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"""]		,		)

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

								UpperCAmelCase     :		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  _lowercase(  self		,		A		,		A = "arrow"		,		A = None		,		A = None		,		**A		,		)			->			Union[str, Any]:
								self._validate_cache_dir()

								UpperCAmelCase     :		Tuple							     =		convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE    )
								self._repartition_df_if_needed(A    )
								UpperCAmelCase     :		Optional[int]							     =		not is_remote_filesystem(self._fs    )
								UpperCAmelCase     :		Union[str, Any]							     =		os.path.join if is_local else posixpath.join

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

								UpperCAmelCase     :		Any							     =		0
								UpperCAmelCase     :		Union[str, Any]							     =		0
								UpperCAmelCase     :		int							     =		0
								UpperCAmelCase     :		int							     =		[]
								UpperCAmelCase     :		Optional[int]							     =		[]

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

								UpperCAmelCase     :		int							     =		total_num_examples
								UpperCAmelCase     :		Dict							     =		total_num_bytes

								# should rename everything at the end
								logger.debug(f'''Renaming {total_shards} shards.'''    )
								if total_shards > 1:
															UpperCAmelCase     :		List[str]							     =		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(
															    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}'''    )		,		)

															UpperCAmelCase     :		int							     =		[]
															UpperCAmelCase     :		Union[str, Any]							     =		0
															for i in range(len(A    )    ):
																						UpperCAmelCase     ,     UpperCAmelCase     :		Tuple							     =		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
															UpperCAmelCase     :		List[Any]							     =		0
															UpperCAmelCase     :		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(A		,		""""""    )		,		)






	def  _lowercase(  self		,		A		,		)			->			SparkExamplesIterable:
								return SparkExamplesIterable(self.df    )






'''simple docstring'''




from __future__ import annotations

import math

class 			UpperCamelCase_ :





	def __init__(  self		,		A    )			->			None:
								UpperCAmelCase     :		Optional[int]							     =		size
								# approximate the overall size of segment tree with given value
								UpperCAmelCase     :		Optional[int]							     =		[0 for i in range(0		,		4 * size    )]
								# create array to store lazy update
								UpperCAmelCase     :		Any							     =		[0 for i in range(0		,		4 * size    )]
								UpperCAmelCase     :		Tuple							     =		[0 for i in range(0		,		4 * size    )]  # flag for lazy update





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2 + 1





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			None:
								if left_element == right_element:
															UpperCAmelCase     :		str							     =		a[left_element - 1]
								else:
															UpperCAmelCase     :		Tuple							     =		(left_element + right_element) // 2
															self.build(self.left(A    )		,		A		,		A		,		A    )
															self.build(self.right(A    )		,		mid + 1		,		A		,		A    )
															UpperCAmelCase     :		str							     =		max(
															    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			bool:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
															UpperCAmelCase     :		int							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		List[str]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		int							     =		True

								if right_element < a or left_element > b:
															return True
								if left_element >= a and right_element <= b:
															UpperCAmelCase     :		Optional[Any]							     =		val
															if left_element != right_element:
																						UpperCAmelCase     :		Tuple							     =		val
																						UpperCAmelCase     :		int							     =		val
																						UpperCAmelCase     :		Any							     =		True
																						UpperCAmelCase     :		str							     =		True
															return True
								UpperCAmelCase     :		str							     =		(left_element + right_element) // 2
								self.update(self.left(A    )		,		A		,		A		,		A		,		A		,		A    )
								self.update(self.right(A    )		,		mid + 1		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		List[str]							     =		max(
								    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )
								return True





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A    )			->			int | float:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Any							     =		self.lazy[idx]
															UpperCAmelCase     :		Any							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Tuple							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		Tuple							     =		True
								if right_element < a or left_element > b:
															return -math.inf
								if left_element >= a and right_element <= b:
															return self.segment_tree[idx]
								UpperCAmelCase     :		Dict							     =		(left_element + right_element) // 2
								UpperCAmelCase     :		List[Any]							     =		self.query(self.left(A    )		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		str							     =		self.query(self.right(A    )		,		mid + 1		,		A		,		A		,		A    )
								return max(A		,		A    )






	def __str__(  self    )			->			str:
								return str([self.query(1		,		1		,		self.size		,		A		,		A    ) for i in range(1		,		self.size + 1    )]    )


if __name__ == "__main__":
							a   :			Optional[int]					  =    [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
							a   :			Optional[Any]					  =    1_5
							a   :			Union[str, Any]					  =    SegmentTree(size)
							segt.build(1, 1, size, A)
							print(segt.query(1, 1, size, 4, 6))
							print(segt.query(1, 1, size, 7, 1_1))
							print(segt.query(1, 1, size, 7, 1_2))
							segt.update(1, 1, size, 1, 3, 1_1_1)
							print(segt.query(1, 1, size, 1, 1_5))
							segt.update(1, 1, size, 7, 8, 2_3_5)
							print(segt)

