





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__:      Tuple									=		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
       import pyspark




@dataclass
class     SCREAMING_SNAKE_CASE__ (						datasets.BuilderConfig      ):
 __SCREAMING_SNAKE_CASE							       =	None


def 	UpperCamelCase__(       UpperCamelCase__			:       "pyspark.sql.DataFrame" ,     UpperCamelCase__			:       List[int] ,     )->Tuple:
       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     SCREAMING_SNAKE_CASE__ (						_BaseExamplesIterable      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=None,):
        A__											=						df
        A__											=						partition_order or range(self.df.rdd.getNumPartitions() )
        A__											=						_generate_iterable_examples(self.df,self.partition_order )



 def __iter__(							self ):
        yield from self.generate_examples_fn()



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						list(range(self.df.rdd.getNumPartitions() ) )
        generator.shuffle(lowercase_ )
        return SparkExamplesIterable(self.df,partition_order=lowercase_ )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.split_shard_indices_by_worker(lowercase_,lowercase_ )
        return SparkExamplesIterable(self.df,partition_order=lowercase_ )




 @property
 def 				UpperCamelCase			(							self ):
        return len(self.partition_order )




class     SCREAMING_SNAKE_CASE__ (						datasets.DatasetBuilder      ):
 __SCREAMING_SNAKE_CASE							       =	SparkConfig



 def __init__(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):
        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 				UpperCamelCase			(							self ):
        # Returns the path of the created file.
        def create_cache_and_write_probe(__lowerCamelCase ):
               # 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 				UpperCamelCase			(							self ):
        return datasets.DatasetInfo(features=self.config.features )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        import pyspark

        def get_arrow_batch_size(__lowerCamelCase ):
               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 <= 100 else 100
        # 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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        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(__lowerCamelCase ):
               # 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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = "arrow",__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):
        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(
                   __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
                      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 __lowerCamelCase : _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 				UpperCamelCase			(							self,__lowerCamelCase,):
        return SparkExamplesIterable(self.df )






import operator

def 			lowercase_				(			_lowerCamelCase							:       list		,	_lowerCamelCase							:       bool = False		,	_lowerCamelCase							:       list | None = None):
    lowercase__ :   int    					=				operator.lt if reverse else operator.gt
    lowercase__ :   str    					=				solution or []

    if not arr:
        return solution

    lowercase__ :   List[str]    					=				[arr.pop(0)]
    for i, item in enumerate(_lowerCamelCase):
        if _operator(_lowerCamelCase		,	sublist[-1]):
            sublist.append(_lowerCamelCase)
            arr.pop(_lowerCamelCase)

    #  merging sublist into solution list
    if not solution:
        solution.extend(_lowerCamelCase)
    else:
        while sublist:
            lowercase__ :   str    					=				sublist.pop(0)
            for i, xx in enumerate(_lowerCamelCase):
                if not _operator(_lowerCamelCase		,	_lowerCamelCase):
                    solution.insert(_lowerCamelCase		,	_lowerCamelCase)
                    break
            else:
                solution.append(_lowerCamelCase)

    strand_sort(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)
    return solution


if __name__ == "__main__":
      assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
      assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



