


'''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 :       Union[str, Any]										= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
	import pyspark


@dataclass
class 					a__ (       datasets.BuilderConfig					):







				"""simple docstring"""



				__UpperCamelCase			:						Optional[datasets.Features]		=		None

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, ):
						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				,	__lowercase				,	__lowercase=None				,	):
										__lowerCAmelCase											= df
										__lowerCAmelCase											= partition_order or range(self.df.rdd.getNumPartitions()						)
										__lowerCAmelCase											= _generate_iterable_examples(self.df				,	self.partition_order						)




				def __iter__(self						):
										yield from self.generate_examples_fn()




				def     _snake_case     (self				,	__lowercase						):
										__lowerCAmelCase											= list(range(self.df.rdd.getNumPartitions()						)						)
										generator.shuffle(SCREAMING_SNAKE_CASE__						)
										return SparkExamplesIterable(self.df				,	partition_order=SCREAMING_SNAKE_CASE__						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase											= self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__						)
										return SparkExamplesIterable(self.df				,	partition_order=SCREAMING_SNAKE_CASE__						)




				@property
				def     _snake_case     (self						):
										return len(self.partition_order						)


class 					a__ (       datasets.DatasetBuilder					):







				"""simple docstring"""



				__UpperCamelCase			:						List[Any]		=		SparkConfig




				def __init__(self				,	__lowercase				,	__lowercase = None				,	__lowercase = None				,	**__lowercase				,	):
										import pyspark

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

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




				def     _snake_case     (self						):






										def create_cache_and_write_probe(__lowercase						):
																# 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=SCREAMING_SNAKE_CASE__						)
																__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(SCREAMING_SNAKE_CASE__				,	'''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(SCREAMING_SNAKE_CASE__						).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						):
										return datasets.DatasetInfo(features=self.config.features						)




				def     _snake_case     (self				,	__lowercase						):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]




				def     _snake_case     (self				,	__lowercase						):
										import pyspark

										def get_arrow_batch_size(__lowercase						):
																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_00 else 1_00
										# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
										__lowerCAmelCase											= (
										    self.df.limit(SCREAMING_SNAKE_CASE__						)
										    .repartition(1						)
										    .mapInArrow(SCREAMING_SNAKE_CASE__				,	'''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(SCREAMING_SNAKE_CASE__				,	int(approx_total_size / max_shard_size						)						)
																__lowerCAmelCase											= self.df.repartition(SCREAMING_SNAKE_CASE__						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase				,	):
										import pyspark

										__lowerCAmelCase											= ParquetWriter if file_format == '''parquet''' else ArrowWriter
										__lowerCAmelCase											= os.path.join(self._working_dir				,	os.path.basename(SCREAMING_SNAKE_CASE__						)						) 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(__lowercase						):
																# Within the same SparkContext, no two task attempts will share the same attempt ID.
																__lowerCAmelCase											= pyspark.TaskContext().taskAttemptId()
																__lowerCAmelCase											= next(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__						)
																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=SCREAMING_SNAKE_CASE__				,	path=working_fpath.replace('''SSSSS'''				,	F"""{shard_id:05d}"""						).replace('''TTTTT'''				,	F"""{task_id:05d}"""						)				,	writer_batch_size=SCREAMING_SNAKE_CASE__				,	storage_options=SCREAMING_SNAKE_CASE__				,	embed_local_files=SCREAMING_SNAKE_CASE__				,	)
																__lowerCAmelCase											= pa.Table.from_batches([first_batch]						)
																writer.write_table(SCREAMING_SNAKE_CASE__						)
																for batch in it:
																						if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																												__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=SCREAMING_SNAKE_CASE__				,	storage_options=SCREAMING_SNAKE_CASE__				,	embed_local_files=SCREAMING_SNAKE_CASE__				,	)
																						__lowerCAmelCase											= pa.Table.from_batches([batch]						)
																						writer.write_table(SCREAMING_SNAKE_CASE__						)

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

										__lowerCAmelCase											= (
										    self.df.mapInArrow(SCREAMING_SNAKE_CASE__				,	'''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				,	__lowercase				,	__lowercase = "arrow"				,	__lowercase = None				,	__lowercase = None				,	**__lowercase				,	):
										self._validate_cache_dir()

										__lowerCAmelCase											= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
										self._repartition_df_if_needed(SCREAMING_SNAKE_CASE__						)
										__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				,	SCREAMING_SNAKE_CASE__						)

										__lowerCAmelCase											= 0
										__lowerCAmelCase											= 0
										__lowerCAmelCase											= 0
										__lowerCAmelCase											= []
										__lowerCAmelCase											= []

										for task_id, content in self._prepare_split_single(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__						):
																(
																    __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(SCREAMING_SNAKE_CASE__						)

										__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(
																    __lowercase				,	__lowercase				,	__lowercase				,	):
																						rename(
																						    SCREAMING_SNAKE_CASE__				,	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(SCREAMING_SNAKE_CASE__						)						):
																						__lowerCAmelCase											= task_id_and_num_shards[i]
																						for shard_id in range(SCREAMING_SNAKE_CASE__						):
																												args.append([task_id, shard_id, global_shard_id]						)
																												global_shard_id += 1
																self._spark.sparkContext.parallelize(SCREAMING_SNAKE_CASE__				,	len(SCREAMING_SNAKE_CASE__						)						).map(lambda __lowercase						: _rename_shard(*SCREAMING_SNAKE_CASE__						)						).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(SCREAMING_SNAKE_CASE__				,	''''''						)				,	)




				def     _snake_case     (self				,	__lowercase				,	):
										return SparkExamplesIterable(self.df						)








"""simple docstring"""
def      __lowerCamelCase   (							a_     :		int							)			->      bool:
      if not isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :int						=  f'''Input value of [number={number}] must be an integer'''
            raise TypeError(a_							)
      if number < 0:
            return False
      __SCREAMING_SNAKE_CASE   :Any						=  number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()