


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_				:			Any   =	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
    import pyspark
@dataclass
class 			SCREAMING_SNAKE_CASE__  (    datasets.BuilderConfig      ):
      snake_case__    :					Optional[datasets.Features]					       =						None







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Tuple  ,   )     ->		Optional[int]:

       """simple docstring"""

       import pyspark

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

       return generate_fn
class 			SCREAMING_SNAKE_CASE__  (    _BaseExamplesIterable      ):
      def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	"pyspark.sql.DataFrame"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=None					,							)		->      List[Any]:
             a_ :		Optional[int]							=  df
             a_ :		Tuple							=  partition_order or range(self.df.rdd.getNumPartitions()					)
             a_ :		Any							=  _generate_iterable_examples(self.df					,							self.partition_order					)
      def __iter__(	self    :	str					)		->      int:
             yield from self.generate_examples_fn()
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	np.random.Generator					)		->      "SparkExamplesIterable":
             a_ :		Any							=  list(range(self.df.rdd.getNumPartitions()					)					)
             generator.shuffle(SCREAMING_SNAKE_CASE__					)
             return SparkExamplesIterable(self.df					,							partition_order=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	int					)		->      "SparkExamplesIterable":
             a_ :		int							=  self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
             return SparkExamplesIterable(self.df					,							partition_order=SCREAMING_SNAKE_CASE__					)

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             return len(self.partition_order					)
class 			SCREAMING_SNAKE_CASE__  (    datasets.DatasetBuilder      ):
      snake_case__    :					Optional[int]					       =						SparkConfig
      def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	"pyspark.sql.DataFrame"					,							SCREAMING_SNAKE_CASE__    :	str = None					,							SCREAMING_SNAKE_CASE__    :	str = None					,							**SCREAMING_SNAKE_CASE__    :	int					,							)		->      str:
             import pyspark

             a_ :		Optional[Any]							=  pyspark.sql.SparkSession.builder.getOrCreate()
             a_ :		Optional[int]							=  df
             a_ :		Dict							=  working_dir

             super().__init__(
                 cache_dir=SCREAMING_SNAKE_CASE__					,							config_name=str(self.df.semanticHash()					)					,							**SCREAMING_SNAKE_CASE__					,							)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:

             def create_cache_and_write_probe(SCREAMING_SNAKE_CASE__    :	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=SCREAMING_SNAKE_CASE__					)
                    a_ :		List[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(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:
                    a_ :		str							=  (
                        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 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Dict:
             return datasets.DatasetInfo(features=self.config.features					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	datasets.download.download_manager.DownloadManager					)		->      str:
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Union[str, Any]:
             import pyspark

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

             a_ :		List[str]							=  self.df.count()
             a_ :		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.
             a_ :		Tuple							=  (
                 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
             )
             a_ :		Optional[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.
                    a_ :		str							=  min(SCREAMING_SNAKE_CASE__					,							int(approx_total_size / max_shard_size					)					)
                    a_ :		Optional[int]							=  self.df.repartition(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	int					,							)		->      Iterable[Tuple[int, bool, Union[int, tuple]]]:
             import pyspark

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

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

                    if writer._num_bytes > 0:
                           a_ :		List[Any]							=  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__					)					):
                                  a_ :		Union[str, Any]							=  os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__					)					,							os.path.basename(SCREAMING_SNAKE_CASE__					)					)
                                  shutil.move(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

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

             a_ :		Tuple							=  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
             self._repartition_df_if_needed(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  not is_remote_filesystem(self._fs					)
             a_ :		Optional[Any]							=  os.path.join if is_local else posixpath.join

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

             a_ :		Any							=  0
             a_ :		List[Any]							=  0
             a_ :		Optional[int]							=  0
             a_ :		Union[str, Any]							=  []
             a_ :		str							=  []

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

             a_ :		Optional[int]							=  total_num_examples
             a_ :		int							=  total_num_bytes

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

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

                    a_ :		int							=  []
                    a_ :		Optional[Any]							=  0
                    for i in range(len(SCREAMING_SNAKE_CASE__					)					):
                           a_ :		Tuple							=  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 SCREAMING_SNAKE_CASE__					: _rename_shard(*SCREAMING_SNAKE_CASE__					)					).collect()
             else:
                    # don't use any pattern
                    a_ :		Optional[int]							=  0
                    a_ :		Optional[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(SCREAMING_SNAKE_CASE__					,							''					)					,							)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	"datasets.SplitGenerator"					,							)		->      SparkExamplesIterable:
             return SparkExamplesIterable(self.df					)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		list:

       """simple docstring"""

       a_ :		int							=  len(__A    )
       for _ in range(__A    ):
              for i in range(_ % 2  ,   arr_size - 1  ,   2    ):
                     if arr[i + 1] < arr[i]:
                            a_							,		a_ :		int							=  arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	list(range(10, 0, -1))
   print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')


