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

if TYPE_CHECKING:
  import pyspark
@dataclass
class  A_			(							datasets.BuilderConfig			):
  _SCREAMING_SNAKE_CASE     					=      None



def 	__A		(      _A				,					_A				,					):



 """simple docstring"""


 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  A_			(							_BaseExamplesIterable			):

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

  def __iter__(		self			:   Optional[Any]							):
   yield from self.generate_examples_fn()

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   np.random.Generator							):
   __a							=	list(range(self.df.rdd.getNumPartitions()							)							)
   generator.shuffle(__SCREAMING_SNAKE_CASE							)
   return SparkExamplesIterable(self.df  ,				partition_order=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	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			:   List[str]							):
   return len(self.partition_order							)
class  A_			(							datasets.DatasetBuilder			):
  _SCREAMING_SNAKE_CASE     					=      SparkConfig

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   "pyspark.sql.DataFrame"  ,				__SCREAMING_SNAKE_CASE			:   str = None  ,				__SCREAMING_SNAKE_CASE			:   str = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   import pyspark

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

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

  def 		_UpperCAmelCase (		self			:   List[str]							):
   # Returns the path of the created file.
   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							=	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							=	(
        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			:   Tuple							):
   return datasets.DatasetInfo(features=self.config.features							)

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

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any							):
   import pyspark

   def get_arrow_batch_size(__SCREAMING_SNAKE_CASE			:   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(__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							=	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(__SCREAMING_SNAKE_CASE  ,				int(approx_total_size / max_shard_size							)							)
    __a							=	self.df.repartition(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				):
   import pyspark

   __a							=	ParquetWriter if file_format == "parquet" else ArrowWriter
   __a							=	os.path.join(self._working_dir  ,				os.path.basename(__SCREAMING_SNAKE_CASE							)							) 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(__SCREAMING_SNAKE_CASE			:   int							):
    # Within the same SparkContext, no two task attempts will share the same attempt ID.
    __a							=	pyspark.TaskContext().taskAttemptId()
    __a							=	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							=	0
    __a							=	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							=	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							,  __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=__SCREAMING_SNAKE_CASE  ,				storage_options=__SCREAMING_SNAKE_CASE  ,				embed_local_files=__SCREAMING_SNAKE_CASE  ,				)
     __a							=	pa.Table.from_batches([batch]							)
     writer.write_table(__SCREAMING_SNAKE_CASE							)

    if writer._num_bytes > 0:
     __a							,  __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(__SCREAMING_SNAKE_CASE							)							):
      __a							=	os.path.join(os.path.dirname(__SCREAMING_SNAKE_CASE							)  ,				os.path.basename(__SCREAMING_SNAKE_CASE							)							)
      shutil.move(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	(
       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			:   Tuple  ,				__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			:   Union[str, Any]  ,				):
   self._validate_cache_dir()

   __a							=	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
   self._repartition_df_if_needed(__SCREAMING_SNAKE_CASE							)
   __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  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	0
   __a							=	0
   __a							=	0
   __a							=	[]
   __a							=	[]

   for task_id, content in self._prepare_split_single(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    (
        (
        __a
    )							,  (
        __a
    )							,  (
        __a
    )							,  (
        __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(__SCREAMING_SNAKE_CASE							)

   __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(
        __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							=	[]
    __a							=	0
    for i in range(len(__SCREAMING_SNAKE_CASE							)							):
     __a							,  __a							=	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							=	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(__SCREAMING_SNAKE_CASE  ,				""							)  ,				)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   "datasets.SplitGenerator"  ,				):
   return SparkExamplesIterable(self.df							)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


if __name__ == "__main__":
  print(f'''{solution() = }''')


