





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

if TYPE_CHECKING:
      import pyspark



@dataclass
class        _lowerCAmelCase    (			datasets.BuilderConfig							):


  '''simple docstring'''
  a_							:       Optional[datasets.Features]      					=None




def     lowerCamelCase_	(      lowerCAmelCase:	"pyspark.sql.DataFrame"    ,							lowerCAmelCase:	List[int]    ,							)->		Tuple:
  import pyspark

  def generate_fn():
    _snake_case   :				Tuple														=	df.select('*'    ,							pyspark.sql.functions.spark_partition_id().alias('part_id'				)				)
    for partition_id in partition_order:
      _snake_case   :				Any														=	df_with_partition_id.select('*'				).where(F"""part_id = {partition_id}"""				).drop('part_id'				)
      _snake_case   :				Optional[int]														=	partition_df.collect()
      _snake_case   :				Optional[Any]														=	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      :					str      ,				UpperCamelCase      :					"pyspark.sql.DataFrame"      ,				UpperCamelCase      :					Tuple=None      ,				):


    '''simple docstring'''





    _snake_case   :				int														=	df
    _snake_case   :				List[str]														=	partition_order or range(self.df.rdd.getNumPartitions()							)
    _snake_case   :				Union[str, Any]														=	_generate_iterable_examples(self.df      ,				self.partition_order							)







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


    '''simple docstring'''





    yield from self.generate_examples_fn()







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					np.random.Generator							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	list(range(self.df.rdd.getNumPartitions()							)							)
    generator.shuffle(UpperCamelCase							)
    return SparkExamplesIterable(self.df      ,				partition_order=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	self.split_shard_indices_by_worker(UpperCamelCase      ,				UpperCamelCase							)
    return SparkExamplesIterable(self.df      ,				partition_order=UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return len(self.partition_order							)




class        _lowerCAmelCase    (			datasets.DatasetBuilder							):


  '''simple docstring'''
  a_							:       int      					=SparkConfig







  def __init__(      self      :					str      ,				UpperCamelCase      :					"pyspark.sql.DataFrame"      ,				UpperCamelCase      :					str = None      ,				UpperCamelCase      :					str = None      ,				**UpperCamelCase      :					Union[str, Any]      ,				):


    '''simple docstring'''





    import pyspark

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

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







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    def create_cache_and_write_probe(UpperCamelCase      :					int							):
      # 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							)
      _snake_case   :				List[str]														=	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:
      _snake_case   :				str														=	(
          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 UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					datasets.download.download_manager.DownloadManager							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    import pyspark

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

    _snake_case   :				Union[str, Any]														=	self.df.count()
    _snake_case   :				Tuple														=	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.
    _snake_case   :				int														=	(
        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
    )
    _snake_case   :				Optional[int]														=	approx_bytes_per_row * df_num_rows
    if approx_total_size > max_shard_size:
      # Make sure there is at least one row per partition.
      _snake_case   :				Dict														=	min(UpperCamelCase      ,				int(approx_total_size / max_shard_size							)							)
      _snake_case   :				Optional[int]														=	self.df.repartition(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					int      ,				):


    '''simple docstring'''





    import pyspark

    _snake_case   :				str														=	ParquetWriter if file_format == 'parquet' else ArrowWriter
    _snake_case   :				Optional[Any]														=	os.path.join(self._working_dir      ,				os.path.basename(UpperCamelCase							)							) if self._working_dir else fpath
    _snake_case   :				List[str]														=	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.
    _snake_case   :				List[Any]														=	self.config.features
    _snake_case   :				Tuple														=	self._writer_batch_size
    _snake_case   :				str														=	self._fs.storage_options

    def write_arrow(UpperCamelCase      :					int							):
      # Within the same SparkContext, no two task attempts will share the same attempt ID.
      _snake_case   :				str														=	pyspark.TaskContext().taskAttemptId()
      _snake_case   :				List[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']      ,				)
      _snake_case   :				int														=	0
      _snake_case   :				List[Any]														=	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      ,				)
      _snake_case   :				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:
          _snake_case							,					_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']      ,				)
          shard_id += 1
          _snake_case   :				Tuple														=	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      ,				)
        _snake_case   :				Optional[Any]														=	pa.Table.from_batches([batch]							)
        writer.write_table(UpperCamelCase							)

      if writer._num_bytes > 0:
        _snake_case							,					_snake_case   :				Optional[int]														=	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							)							):
          _snake_case   :				List[Any]														=	os.path.join(os.path.dirname(UpperCamelCase							)      ,				os.path.basename(UpperCamelCase							)							)
          shutil.move(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				int														=	(
        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 UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					"datasets.SplitGenerator"      ,				UpperCamelCase      :					str = "arrow"      ,				UpperCamelCase      :					Optional[Union[str, int]] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				**UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    self._validate_cache_dir()

    _snake_case   :				Optional[int]														=	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
    self._repartition_df_if_needed(UpperCamelCase							)
    _snake_case   :				List[Any]														=	not is_remote_filesystem(self._fs							)
    _snake_case   :				str														=	os.path.join if is_local else posixpath.join

    _snake_case   :				Dict														=	'-TTTTT-SSSSS-of-NNNNN'
    _snake_case   :				int														=	f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
    _snake_case   :				int														=	path_join(self._output_dir      ,				UpperCamelCase							)

    _snake_case   :				int														=	0
    _snake_case   :				List[Any]														=	0
    _snake_case   :				List[str]														=	0
    _snake_case   :				List[str]														=	[]
    _snake_case   :				Optional[int]														=	[]

    for task_id, content in self._prepare_split_single(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							):
      (
          (
          _snake_case
      )							,					(
          _snake_case
      )							,					(
          _snake_case
      )							,					(
          _snake_case
      )							,					
      )   :				int														=	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							)

    _snake_case   :				Tuple														=	total_num_examples
    _snake_case   :				str														=	total_num_bytes

    # should rename everything at the end
    logger.debug(f"""Renaming {total_shards} shards."""							)
    if total_shards > 1:
      _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.
      _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}"""							)      ,				)

      _snake_case   :				Tuple														=	[]
      _snake_case   :				Optional[Any]														=	0
      for i in range(len(UpperCamelCase							)							):
        _snake_case							,					_snake_case   :				Any														=	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
      _snake_case   :				Union[str, Any]														=	0
      _snake_case   :				Tuple														=	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 UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					"datasets.SplitGenerator"      ,				):


    '''simple docstring'''





    return SparkExamplesIterable(self.df							)











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''simple docstring'''





    from pprint import pformat

    if self.left is None and self.right is None:
      return f"""'{self.value}: {self.prior:.5}'"""
    else:
      return pformat(
          {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1							)







  def __str__(      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




