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


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

if TYPE_CHECKING:
				import pyspark



@dataclass
class        lowerCamelCase__				(   datasets.BuilderConfig				):
			UpperCamelCase__    							=None







def _lowerCamelCase  (		snake_case     , snake_case     , ):
							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        lowerCamelCase__				(   _BaseExamplesIterable				):





			def __init__(       self	:	int  ,  lowercase__	:	"pyspark.sql.DataFrame"  ,  lowercase__	:	Union[str, Any]=None  ,  ):
										_lowerCAmelCase							=							df
										_lowerCAmelCase							=							partition_order or range(self.df.rdd.getNumPartitions()						)
										_lowerCAmelCase							=							_generate_iterable_examples(self.df  ,  self.partition_order						)





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	np.random.Generator						):
										_lowerCAmelCase							=							list(range(self.df.rdd.getNumPartitions()						)						)
										generator.shuffle(lowercase__						)
										return SparkExamplesIterable(self.df  ,  partition_order=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	int  ,  lowercase__	:	int						):
										_lowerCAmelCase							=							self.split_shard_indices_by_worker(lowercase__  ,  lowercase__						)
										return SparkExamplesIterable(self.df  ,  partition_order=lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return len(self.partition_order						)







class        lowerCamelCase__				(   datasets.DatasetBuilder				):
			UpperCamelCase__    							=SparkConfig





			def __init__(       self	:	Dict  ,  lowercase__	:	"pyspark.sql.DataFrame"  ,  lowercase__	:	str = None  ,  lowercase__	:	str = None  ,  **lowercase__	:	Optional[int]  ,  ):
										import pyspark

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase__	:	Optional[Any]						):
																	# 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=lowercase__						)
																	_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(lowercase__  ,  '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(lowercase__						).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   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										return datasets.DatasetInfo(features=self.config.features						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	datasets.download.download_manager.DownloadManager						):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Union[str, Any]						):
										import pyspark

										def get_arrow_batch_size(lowercase__	:	List[str]						):
																	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(lowercase__						)
										    .repartition(1						)
										    .mapInArrow(lowercase__  ,  '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(lowercase__  ,  int(approx_total_size / max_shard_size						)						)
																	_lowerCAmelCase							=							self.df.repartition(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	str  ,  lowercase__	:	str  ,  lowercase__	:	int  ,  ):
										import pyspark

										_lowerCAmelCase							=							ParquetWriter if file_format == 'parquet' else ArrowWriter
										_lowerCAmelCase							=							os.path.join(self._working_dir  ,  os.path.basename(lowercase__						)						) 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__	:	str						):
																	# Within the same SparkContext, no two task attempts will share the same attempt ID.
																	_lowerCAmelCase							=							pyspark.TaskContext().taskAttemptId()
																	_lowerCAmelCase							=							next(lowercase__  ,  lowercase__						)
																	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=lowercase__  ,  path=working_fpath.replace('SSSSS'  ,  f'{shard_id:05d}'						).replace('TTTTT'  ,  f'{task_id:05d}'						)  ,  writer_batch_size=lowercase__  ,  storage_options=lowercase__  ,  embed_local_files=lowercase__  ,  )
																	_lowerCAmelCase							=							pa.Table.from_batches([first_batch]						)
																	writer.write_table(lowercase__						)
																	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=lowercase__  ,  storage_options=lowercase__  ,  embed_local_files=lowercase__  ,  )
																								_lowerCAmelCase							=							pa.Table.from_batches([batch]						)
																								writer.write_table(lowercase__						)

																	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(lowercase__						)						):
																															_lowerCAmelCase							=							os.path.join(os.path.dirname(lowercase__						)  ,  os.path.basename(lowercase__						)						)
																															shutil.move(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							(
										    self.df.mapInArrow(lowercase__  ,  '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   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	"datasets.SplitGenerator"  ,  lowercase__	:	str = "arrow"  ,  lowercase__	:	Optional[Union[str, int]] = None  ,  lowercase__	:	Optional[int] = None  ,  **lowercase__	:	Dict  ,  ):
										self._validate_cache_dir()

										_lowerCAmelCase							=							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
										self._repartition_df_if_needed(lowercase__						)
										_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  ,  lowercase__						)

										_lowerCAmelCase							=							0
										_lowerCAmelCase							=							0
										_lowerCAmelCase							=							0
										_lowerCAmelCase							=							[]
										_lowerCAmelCase							=							[]

										for task_id, content in self._prepare_split_single(lowercase__  ,  lowercase__  ,  lowercase__						):
																	(
																	    (
																	    _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(lowercase__						)

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	"datasets.SplitGenerator"  ,  ):
										return SparkExamplesIterable(self.df						)







def _lowerCamelCase  (		snake_case = 10  ):
							if not isinstance(snake_case     , snake_case  ) or n < 0:
														raise ValueError('Invalid input'  )
							_lowerCAmelCase							=							10**n
							_lowerCAmelCase							=							28_433 * (pow(2     , 7_830_457     , snake_case  )) + 1
							return str(number % modulus  )


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

				testmod()
				print(f"""{solution(1_0) = }""")






