






"""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


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark






@dataclass
class      a						(					datasets.BuilderConfig		):
    SCREAMING_SNAKE_CASE						:     Optional[datasets.Features]						= None







def      lowerCamelCase__       (							_lowerCamelCase       :		"pyspark.sql.DataFrame"		,				_lowerCamelCase       :		List[int]		,				)      ->				Dict:
  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		):






    def __init__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							"pyspark.sql.DataFrame"       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None       ,						)     ->		Optional[Any]:
      lowerCamelCase_         =						df
      lowerCamelCase_         =						partition_order or range(self.df.rdd.getNumPartitions()					)
      lowerCamelCase_         =						_generate_iterable_examples(self.df       ,						self.partition_order					)






    def __iter__(       self     :							int					)     ->		Union[str, Any]:
      yield from self.generate_examples_fn()






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							np.random.Generator					)     ->		"SparkExamplesIterable":
      lowerCamelCase_         =						list(range(self.df.rdd.getNumPartitions()					)					)
      generator.shuffle(__SCREAMING_SNAKE_CASE					)
      return SparkExamplesIterable(self.df       ,						partition_order=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		"SparkExamplesIterable":
      lowerCamelCase_         =						self.split_shard_indices_by_worker(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      return SparkExamplesIterable(self.df       ,						partition_order=__SCREAMING_SNAKE_CASE					)






    @property
    def    UpperCamelCase       (       self     :							str					)     ->		int:
      return len(self.partition_order					)






class      a						(					datasets.DatasetBuilder		):
    SCREAMING_SNAKE_CASE						:     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     :							Any       ,						)     ->		Tuple:
      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    UpperCamelCase       (       self     :							Tuple					)     ->		Tuple:
      # Returns the path of the created file.
      def create_cache_and_write_probe(__SCREAMING_SNAKE_CASE     :							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=__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    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Optional[int]:
      return datasets.DatasetInfo(features=self.config.features					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							datasets.download.download_manager.DownloadManager					)     ->		Optional[Any]:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Dict					)     ->		str:
      import pyspark

      def get_arrow_batch_size(__SCREAMING_SNAKE_CASE     :							Optional[Any]					):
        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 <= 100 else 100
      # 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    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							int       ,						)     ->		Iterable[Tuple[int, bool, Union[int, tuple]]]:
      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(__SCREAMING_SNAKE_CASE     :							Tuple					):
        # 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_     ,	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_     ,	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    UpperCamelCase       (       self     :							Union[str, Any]       ,						__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     :							str       ,						)     ->		Any:
      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_
        )     ,	(
            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(__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(
            __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}'''					)       ,						)

        lowerCamelCase_         =						[]
        lowerCamelCase_         =						0
        for i in range(len(__SCREAMING_SNAKE_CASE					)					):
          lowerCamelCase_     ,	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 __SCREAMING_SNAKE_CASE					: _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    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							"datasets.SplitGenerator"       ,						)     ->		SparkExamplesIterable:
      return SparkExamplesIterable(self.df					)












"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




