





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


_lowerCamelCase :    int  							= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark


@dataclass
class 						lowerCamelCase       (datasets.BuilderConfig      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   None




def _a (  SCREAMING_SNAKE_CASE__      :  "pyspark.sql.DataFrame"			,				SCREAMING_SNAKE_CASE__      :  List[int]			,				)	->    Optional[Any]:

							'''simple docstring'''






							import pyspark

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

							return generate_fn


class 						lowerCamelCase       (_BaseExamplesIterable      ):







							"""simple docstring"""




							def __init__( self     :					Tuple,			_UpperCAmelCase     :					"pyspark.sql.DataFrame",			_UpperCAmelCase     :					List[Any]=None,			)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						df
														SCREAMING_SNAKE_CASE__       :      Tuple     =						partition_order or range(self.df.rdd.getNumPartitions()						)
														SCREAMING_SNAKE_CASE__       :      str     =						_generate_iterable_examples(self.df,			self.partition_order						)






							def __iter__( self     :					Dict						)				->			Dict:





														"""simple docstring"""
														yield from self.generate_examples_fn()






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					np.random.Generator						)				->			"SparkExamplesIterable":





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						list(range(self.df.rdd.getNumPartitions()						)						)
														generator.shuffle(_UpperCAmelCase						)
														return SparkExamplesIterable(self.df,			partition_order=_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int						)				->			"SparkExamplesIterable":





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.split_shard_indices_by_worker(_UpperCAmelCase,			_UpperCAmelCase						)
														return SparkExamplesIterable(self.df,			partition_order=_UpperCAmelCase						)






							@property
							def  A_     ( self     :					Any						)				->			int:





														"""simple docstring"""
														return len(self.partition_order						)


class 						lowerCamelCase       (datasets.DatasetBuilder      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   SparkConfig






							def __init__( self     :					Dict,			_UpperCAmelCase     :					"pyspark.sql.DataFrame",			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					Any,			)				->			Optional[Any]:





														"""simple docstring"""
														import pyspark

														SCREAMING_SNAKE_CASE__       :      str     =						pyspark.sql.SparkSession.builder.getOrCreate()
														SCREAMING_SNAKE_CASE__       :      int     =						df
														SCREAMING_SNAKE_CASE__       :      Dict     =						working_dir

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






							def  A_     ( self     :					List[Any]						)				->			List[Any]:





														"""simple docstring"""
														# Returns the path of the created file.
														def create_cache_and_write_probe(_UpperCAmelCase     :					Tuple						):
																					# 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=_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, 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(_UpperCAmelCase,			"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:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						(
																					    self._spark.sparkContext.parallelize(range(1						),			1						).mapPartitions(_UpperCAmelCase						).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  A_     ( self     :					str						)				->			str:





														"""simple docstring"""
														return datasets.DatasetInfo(features=self.config.features						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					datasets.download.download_manager.DownloadManager						)				->			int:





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






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														import pyspark

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

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.df.count()
														SCREAMING_SNAKE_CASE__       :      int     =						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.
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(
														    self.df.limit(_UpperCAmelCase						)
														    .repartition(1						)
														    .mapInArrow(_UpperCAmelCase,			"batch_bytes: long"						)
														    .agg(pyspark.sql.functions.sum("batch_bytes"						).alias("sample_bytes"						)						)
														    .collect()[0]
														    .sample_bytes
														    / sample_num_rows
														)
														SCREAMING_SNAKE_CASE__       :      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.
																					SCREAMING_SNAKE_CASE__       :      Any     =						min(_UpperCAmelCase,			int(approx_total_size / max_shard_size						)						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.df.repartition(_UpperCAmelCase						)






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					int,			)				->			Iterable[Tuple[int, bool, Union[int, tuple]]]:





														"""simple docstring"""
														import pyspark

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

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

																					if writer._num_bytes > 0:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						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(_UpperCAmelCase						)						):
																																			SCREAMING_SNAKE_CASE__       :      Dict     =						os.path.join(os.path.dirname(_UpperCAmelCase						),			os.path.basename(_UpperCAmelCase						)						)
																																			shutil.move(_UpperCAmelCase,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Any     =						(
														    self.df.mapInArrow(_UpperCAmelCase,			"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  A_     ( self     :					List[str],			_UpperCAmelCase     :					"datasets.SplitGenerator",			_UpperCAmelCase     :					str = "arrow",			_UpperCAmelCase     :					Optional[Union[str, int]] = None,			_UpperCAmelCase     :					Optional[int] = None,			**_UpperCAmelCase     :					Any,			)				->			int:





														"""simple docstring"""
														self._validate_cache_dir()

														SCREAMING_SNAKE_CASE__       :      Dict     =						convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
														self._repartition_df_if_needed(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						not is_remote_filesystem(self._fs						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						os.path.join if is_local else posixpath.join

														SCREAMING_SNAKE_CASE__       :      int     =						"-TTTTT-SSSSS-of-NNNNN"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
														SCREAMING_SNAKE_CASE__       :      Dict     =						path_join(self._output_dir,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						0
														SCREAMING_SNAKE_CASE__       :      str     =						0
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0
														SCREAMING_SNAKE_CASE__       :      Any     =						[]
														SCREAMING_SNAKE_CASE__       :      str     =						[]

														for task_id, content in self._prepare_split_single(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						):
																					(
																					    (
																					    SCREAMING_SNAKE_CASE__
																					)   ,(
																					    SCREAMING_SNAKE_CASE__
																					)   ,(
																					    SCREAMING_SNAKE_CASE__
																					)   ,(
																					    SCREAMING_SNAKE_CASE__
																					)   ,
																					)       :      Tuple     =						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(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						total_num_examples
														SCREAMING_SNAKE_CASE__       :      List[str]     =						total_num_bytes

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

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

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






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					"datasets.SplitGenerator",			)				->			SparkExamplesIterable:





														"""simple docstring"""
														return SparkExamplesIterable(self.df						)













def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    str:

							'''simple docstring'''






							if not all(char in "01" for char in bin_string       ):
														raise ValueError("Non-binary value was passed to the function"       )
							if not bin_string:
														raise ValueError("Empty string was passed to the function"       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						""
							while len(SCREAMING_SNAKE_CASE__       ) % 3 != 0:
														SCREAMING_SNAKE_CASE__       :      str     =						"0" + bin_string
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[
							    bin_string[index : index + 3]
							    for index in range(len(SCREAMING_SNAKE_CASE__       )       )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														for index, val in enumerate(SCREAMING_SNAKE_CASE__       ):
																					oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE__       )       )
														oct_string += str(SCREAMING_SNAKE_CASE__       )
							return oct_string


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

		testmod()






