





"""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 			_a						(				datasets.BuilderConfig    ):
				UpperCamelCase								=   None





def 	a_    (							_lowercase	,						_lowercase	,						):
						import pyspark

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

						return generate_fn




class 			_a						(				_BaseExamplesIterable    ):






				def __init__(			self		:   Dict,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   int=None,			)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   df
										_UpperCamelCase      : Tuple         =   partition_order or range(self.df.rdd.getNumPartitions()							)
										_UpperCamelCase      : Optional[int]         =   _generate_iterable_examples(self.df,			self.partition_order							)






				def __iter__(			self		:   List[Any]							)     ->				int:




										'''simple docstring'''



										yield from self.generate_examples_fn()






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Optional[Any]							)     ->				"SparkExamplesIterable":




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   list(range(self.df.rdd.getNumPartitions()							)							)
										generator.shuffle(a__							)
										return SparkExamplesIterable(self.df,			partition_order=a__							)






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Any							)     ->				"SparkExamplesIterable":




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.split_shard_indices_by_worker(a__,			a__							)
										return SparkExamplesIterable(self.df,			partition_order=a__							)






				@property
				def     snake_case     (			self		:   Optional[int]							)     ->				int:




										'''simple docstring'''



										return len(self.partition_order							)




class 			_a						(				datasets.DatasetBuilder    ):
				UpperCamelCase								=   SparkConfig






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Any = None,			lowerCAmelCase__		:   Dict = None,			**lowerCAmelCase__		:   Optional[Any],			)     ->				Optional[Any]:




										'''simple docstring'''



										import pyspark

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

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






				def     snake_case     (			self		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										def create_cache_and_write_probe(lowerCAmelCase__		:   Optional[int]							):
																# 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      : str         =   (
																    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     snake_case     (			self		:   Any							)     ->				Optional[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Optional[int]							)     ->				Any:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										import pyspark

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

										_UpperCamelCase      : Tuple         =   self.df.count()
										_UpperCamelCase      : str         =   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.
										_UpperCamelCase      : 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      : str         =   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(a__,			int(approx_total_size / max_shard_size							)							)
																_UpperCamelCase      : Dict         =   self.df.repartition(a__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Tuple,			)     ->				Iterable[Tuple[int, bool, Union[int, tuple]]]:




										'''simple docstring'''



										import pyspark

										_UpperCamelCase      : List[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      : 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.
										_UpperCamelCase      : Tuple         =   self.config.features
										_UpperCamelCase      : str         =   self._writer_batch_size
										_UpperCamelCase      : Any         =   self._fs.storage_options

										def write_arrow(lowerCAmelCase__		:   Optional[int]							):
																# Within the same SparkContext, no two task attempts will share the same attempt ID.
																_UpperCamelCase      : Optional[int]         =   pyspark.TaskContext().taskAttemptId()
																_UpperCamelCase      : Optional[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'''],			)
																_UpperCamelCase      : Union[str, Any]         =   0
																_UpperCamelCase      : Any         =   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      : 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:
																												_UpperCamelCase       ,							_UpperCamelCase      : Optional[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      : str         =   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      : Any         =   pa.Table.from_batches([batch]							)
																						writer.write_table(a__							)

																if writer._num_bytes > 0:
																						_UpperCamelCase       ,							_UpperCamelCase      : Dict         =   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      : List[str]         =   (
										    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     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Any = "arrow",			lowerCAmelCase__		:   Tuple = None,			lowerCAmelCase__		:   Dict = None,			**lowerCAmelCase__		:   List[str],			)     ->				Dict:




										'''simple docstring'''



										self._validate_cache_dir()

										_UpperCamelCase      : List[Any]         =   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      : Any         =   os.path.join if is_local else posixpath.join

										_UpperCamelCase      : List[str]         =   '''-TTTTT-SSSSS-of-NNNNN'''
										_UpperCamelCase      : List[Any]         =   f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
										_UpperCamelCase      : Dict         =   path_join(self._output_dir,			a__							)

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

										for task_id, content in self._prepare_split_single(a__,			a__,			a__							):
																(
																    (
																    _UpperCamelCase				
																)       ,							(
																    _UpperCamelCase				
																)       ,							(
																    _UpperCamelCase				
																)       ,							(
																    _UpperCamelCase				
																)       ,											
																)      : Optional[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      : Any         =   total_num_examples
										_UpperCamelCase      : List[str]         =   total_num_bytes

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

																# use the -SSSSS-of-NNNNN pattern
																def _rename_shard(
																    lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Optional[int],			):
																						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      : Optional[int]         =   []
																_UpperCamelCase      : Optional[Any]         =   0
																for i in range(len(a__							)							):
																						_UpperCamelCase       ,							_UpperCamelCase      : Any         =   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 lowerCAmelCase__							: _rename_shard(*a__							)							).collect()
										else:
																# don't use any pattern
																_UpperCamelCase      : Tuple         =   0
																_UpperCamelCase      : 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(a__,			''''''							),			)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   int,			)     ->				SparkExamplesIterable:




										'''simple docstring'''



										return SparkExamplesIterable(self.df							)










"""simple docstring"""




def 	a_    (							_lowercase = 100       ):
						_UpperCamelCase      : List[Any]         =   0
						_UpperCamelCase      : Optional[Any]         =   0
						for i in range(1	,						n + 1       ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


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



