


"""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_       							=    datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark
@dataclass
class snake_case      (  datasets.BuilderConfig):
		__UpperCamelCase           =			None






def      a__ (				__lowercase     ,			__lowercase     ,			)	->	str:
				import pyspark

				def generate_fn():
								_A					  =			df.select("*"     ,			pyspark.sql.functions.spark_partition_id().alias("part_id"   )   )
								for partition_id in partition_order:
												_A					  =			df_with_partition_id.select("*"   ).where(f"""part_id = {partition_id}"""   ).drop("part_id"   )
												_A					  =			partition_df.collect()
												_A					  =			0
												for row in rows:
																yield f"""{partition_id}_{row_id}""", row.asDict()
																row_id += 1

				return generate_fn
class snake_case      (  _BaseExamplesIterable):







		def __init__(					self      :       int    ,   a__      :       "pyspark.sql.DataFrame"    ,   a__      :       Optional[int]=None    ,   )       ->						str:





						'''simple docstring'''







						_A					  =			df
						_A					  =			partition_order or range(self.df.rdd.getNumPartitions()		)
						_A					  =			_generate_iterable_examples(self.df    ,   self.partition_order		)







		def __iter__(					self      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						yield from self.generate_examples_fn()







		def  a_ (					self      :       Optional[int]    ,   a__      :       np.random.Generator		)       ->						int:





						'''simple docstring'''







						_A					  =			list(range(self.df.rdd.getNumPartitions()		)		)
						generator.shuffle(lowercase_		)
						return SparkExamplesIterable(self.df    ,   partition_order=lowercase_		)







		def  a_ (					self      :       Optional[int]    ,   a__      :       int    ,   a__      :       int		)       ->						int:





						'''simple docstring'''







						_A					  =			self.split_shard_indices_by_worker(lowercase_    ,   lowercase_		)
						return SparkExamplesIterable(self.df    ,   partition_order=lowercase_		)







		@property
		def  a_ (					self      :       List[str]		)       ->						Dict:





						'''simple docstring'''







						return len(self.partition_order		)





class snake_case      (  datasets.DatasetBuilder):
		__UpperCamelCase           =			SparkConfig







		def __init__(					self      :       Tuple    ,   a__      :       "pyspark.sql.DataFrame"    ,   a__      :       str = None    ,   a__      :       str = None    ,   **a__      :       str    ,   )       ->						str:





						'''simple docstring'''







						import pyspark

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

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







		def  a_ (					self      :       str		)       ->						Any:





						'''simple docstring'''







						def create_cache_and_write_probe(a__      :       str		):
										# 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_		)
										_A					  =			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:
										_A					  =			(
										    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  a_ (					self      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]    ,   a__      :       datasets.download.download_manager.DownloadManager		)       ->						Union[str, Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       List[str]    ,   a__      :       Union[str, Any]		)       ->						str:





						'''simple docstring'''







						import pyspark

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

						_A					  =			self.df.count()
						_A					  =			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.
						_A					  =			(
						    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
						)
						_A					  =			approx_bytes_per_row * df_num_rows
						if approx_total_size > max_shard_size:
										# Make sure there is at least one row per partition.
										_A					  =			min(lowercase_    ,   int(approx_total_size / max_shard_size		)		)
										_A					  =			self.df.repartition(lowercase_		)







		def  a_ (					self      :       Any    ,   a__      :       str    ,   a__      :       str    ,   a__      :       int    ,   )       ->						Any:





						'''simple docstring'''







						import pyspark

						_A					  =			ParquetWriter if file_format == """parquet""" else ArrowWriter
						_A					  =			os.path.join(self._working_dir    ,   os.path.basename(lowercase_		)		) if self._working_dir else fpath
						_A					  =			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.
						_A					  =			self.config.features
						_A					  =			self._writer_batch_size
						_A					  =			self._fs.storage_options

						def write_arrow(a__      :       str		):
										# Within the same SparkContext, no two task attempts will share the same attempt ID.
										_A					  =			pyspark.TaskContext().taskAttemptId()
										_A					  =			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"]    ,   )
										_A					  =			0
										_A					  =			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_    ,   )
										_A					  =			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:
																		_A					  =			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
																		_A					  =			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_    ,   )
														_A					  =			pa.Table.from_batches([batch]		)
														writer.write_table(lowercase_		)

										if writer._num_bytes > 0:
														_A					  =			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_		)		):
																		_A					  =			os.path.join(os.path.dirname(lowercase_		)    ,   os.path.basename(lowercase_		)		)
																		shutil.move(lowercase_    ,   lowercase_		)

						_A					  =			(
						    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  a_ (					self      :       Dict    ,   a__      :       "datasets.SplitGenerator"    ,   a__      :       str = "arrow"    ,   a__      :       Optional[Union[str, int]] = None    ,   a__      :       Optional[int] = None    ,   **a__      :       List[str]    ,   )       ->						Union[str, Any]:





						'''simple docstring'''







						self._validate_cache_dir()

						_A					  =			convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
						self._repartition_df_if_needed(lowercase_		)
						_A					  =			not is_remote_filesystem(self._fs		)
						_A					  =			os.path.join if is_local else posixpath.join

						_A					  =			"""-TTTTT-SSSSS-of-NNNNN"""
						_A					  =			F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
						_A					  =			path_join(self._output_dir    ,   lowercase_		)

						_A					  =			0
						_A					  =			0
						_A					  =			0
						_A					  =			[]
						_A					  =			[]

						for task_id, content in self._prepare_split_single(lowercase_    ,   lowercase_    ,   lowercase_		):
										(
										    _A		
										)					  =			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_		)

						_A					  =			total_num_examples
						_A					  =			total_num_bytes

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

										# use the -SSSSS-of-NNNNN pattern
										def _rename_shard(
										    a__      :       int    ,   a__      :       int    ,   a__      :       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}"""		)    ,   )

										_A					  =			[]
										_A					  =			0
										for i in range(len(lowercase_		)		):
														_A					  =			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 a__		: _rename_shard(*lowercase_		)		).collect()
						else:
										# don't use any pattern
										_A					  =			0
										_A					  =			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  a_ (					self      :       Union[str, Any]    ,   a__      :       "datasets.SplitGenerator"    ,   )       ->						Any:





						'''simple docstring'''







						return SparkExamplesIterable(self.df		)






"""simple docstring"""


from __future__ import annotations






def      a__ (				__lowercase     ,			__lowercase   )	->	float:
				_A					  =			sorted(numsa + numsa   )
				_A  ,		_A					  =			divmod(len(__lowercase   )     ,			2   )
				if mod == 1:
								return all_numbers[div]
				else:
								return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a_       							=    [float(x) for x in input("Enter the elements of first array: ").split()]
		a_       							=    [float(x) for x in input("Enter the elements of second array: ").split()]
		print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')