



"""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


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

if TYPE_CHECKING:
   import pyspark






@dataclass
class 	__snake_case	(     datasets.BuilderConfig):
  snake_case__    :       Optional[datasets.Features]        =						None






def 	snake_case_  (				A_ :		"pyspark.sql.DataFrame",    A_ :		List[int],    ):


       '''simple docstring'''





       import pyspark

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

       return generate_fn






class 	__snake_case	(     _BaseExamplesIterable):




  def __init__(			self					:						Tuple						, __lowerCAmelCase					:						"pyspark.sql.DataFrame"						, __lowerCAmelCase					:						Optional[int]=None						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			df
         _lowerCamelCase						:    Union[str, Any]         =			partition_order or range(self.df.rdd.getNumPartitions()		)
         _lowerCamelCase						:    Dict         =			_generate_iterable_examples(self.df						, self.partition_order		)




  def __iter__(			self					:						List[Any]		):






         """simple docstring"""






         yield from self.generate_examples_fn()




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						np.random.Generator		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			list(range(self.df.rdd.getNumPartitions()		)		)
         generator.shuffle(__lowerCAmelCase		)
         return SparkExamplesIterable(self.df						, partition_order=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int		):






         """simple docstring"""






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




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         return len(self.partition_order		)






class 	__snake_case	(     datasets.DatasetBuilder):
  snake_case__    :       List[Any]        =						SparkConfig




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






         """simple docstring"""






         import pyspark

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

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         def create_cache_and_write_probe(__lowerCAmelCase					:						Optional[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=__lowerCAmelCase		)
                _lowerCamelCase						:    Optional[int]         =			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(__lowerCAmelCase						, '''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						:    Optional[Any]         =			(
                    self._spark.sparkContext.parallelize(range(1		)						, 1		).mapPartitions(__lowerCAmelCase		).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					:						Any		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						datasets.download.download_manager.DownloadManager		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         import pyspark

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

         _lowerCamelCase						:    Any         =			self.df.count()
         _lowerCamelCase						:    Union[str, Any]         =			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.
         _lowerCamelCase						:    List[Any]         =			(
             self.df.limit(__lowerCAmelCase		)
             .repartition(1		)
             .mapInArrow(__lowerCAmelCase						, '''batch_bytes: long'''		)
             .agg(pyspark.sql.functions.sum('''batch_bytes'''		).alias('''sample_bytes'''		)		)
             .collect()[0]
             .sample_bytes
             / sample_num_rows
         )
         _lowerCamelCase						:    Dict         =			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						:    List[str]         =			min(__lowerCAmelCase						, int(approx_total_size / max_shard_size		)		)
                _lowerCamelCase						:    Optional[int]         =			self.df.repartition(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						int						, ):






         """simple docstring"""






         import pyspark

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

         def write_arrow(__lowerCAmelCase					:						List[str]		):
                # Within the same SparkContext, no two task attempts will share the same attempt ID.
                _lowerCamelCase						:    List[str]         =			pyspark.TaskContext().taskAttemptId()
                _lowerCamelCase						:    Any         =			next(__lowerCAmelCase						, __lowerCAmelCase		)
                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						:    List[Any]         =			0
                _lowerCamelCase						:    Optional[int]         =			writer_class(
                    features=__lowerCAmelCase						, path=working_fpath.replace('''SSSSS'''						, f'''{shard_id:05d}'''		).replace('''TTTTT'''						, f'''{task_id:05d}'''		)						, writer_batch_size=__lowerCAmelCase						, storage_options=__lowerCAmelCase						, embed_local_files=__lowerCAmelCase						, )
                _lowerCamelCase						:    int         =			pa.Table.from_batches([first_batch]		)
                writer.write_table(__lowerCAmelCase		)
                for batch in it:
                       if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                              _lowerCamelCase      ,   _lowerCamelCase						:    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
                              _lowerCamelCase						:    Optional[int]         =			writer_class(
                                  features=writer._features						, path=working_fpath.replace('''SSSSS'''						, f'''{shard_id:05d}'''		).replace('''TTTTT'''						, f'''{task_id:05d}'''		)						, writer_batch_size=__lowerCAmelCase						, storage_options=__lowerCAmelCase						, embed_local_files=__lowerCAmelCase						, )
                       _lowerCamelCase						:    Optional[int]         =			pa.Table.from_batches([batch]		)
                       writer.write_table(__lowerCAmelCase		)

                if writer._num_bytes > 0:
                       _lowerCamelCase      ,   _lowerCamelCase						:    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(__lowerCAmelCase		)		):
                              _lowerCamelCase						:    Optional[Any]         =			os.path.join(os.path.dirname(__lowerCAmelCase		)						, os.path.basename(__lowerCAmelCase		)		)
                              shutil.move(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			(
             self.df.mapInArrow(__lowerCAmelCase						, '''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					:						Tuple						, __lowerCAmelCase					:						"datasets.SplitGenerator"						, __lowerCAmelCase					:						str = "arrow"						, __lowerCAmelCase					:						Optional[Union[str, int]] = None						, __lowerCAmelCase					:						Optional[int] = None						, **__lowerCAmelCase					:						Tuple						, ):






         """simple docstring"""






         self._validate_cache_dir()

         _lowerCamelCase						:    str         =			convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
         self._repartition_df_if_needed(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			not is_remote_filesystem(self._fs		)
         _lowerCamelCase						:    Tuple         =			os.path.join if is_local else posixpath.join

         _lowerCamelCase						:    int         =			'''-TTTTT-SSSSS-of-NNNNN'''
         _lowerCamelCase						:    Tuple         =			f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
         _lowerCamelCase						:    List[Any]         =			path_join(self._output_dir						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			0
         _lowerCamelCase						:    Any         =			0
         _lowerCamelCase						:    str         =			0
         _lowerCamelCase						:    int         =			[]
         _lowerCamelCase						:    List[str]         =			[]

         for task_id, content in self._prepare_split_single(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		):
                (
                    (
                    _lowerCamelCase
                )      ,   (
                    _lowerCamelCase
                )      ,   (
                    _lowerCamelCase
                )      ,   (
                    _lowerCamelCase
                )      ,   
                )						:    str         =			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(__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			total_num_examples
         _lowerCamelCase						:    str         =			total_num_bytes

         # should rename everything at the end
         logger.debug(f'''Renaming {total_shards} shards.'''		)
         if total_shards > 1:
                _lowerCamelCase						:    Optional[Any]         =			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						:    str         =			self._fs

                # use the -SSSSS-of-NNNNN pattern
                def _rename_shard(
                    __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, ):
                       rename(
                           __lowerCAmelCase						, 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						:    Union[str, Any]         =			[]
                _lowerCamelCase						:    Any         =			0
                for i in range(len(__lowerCAmelCase		)		):
                       _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			task_id_and_num_shards[i]
                       for shard_id in range(__lowerCAmelCase		):
                              args.append([task_id, shard_id, global_shard_id]		)
                              global_shard_id += 1
                self._spark.sparkContext.parallelize(__lowerCAmelCase						, len(__lowerCAmelCase		)		).map(lambda __lowerCAmelCase		: _rename_shard(*__lowerCAmelCase		)		).collect()
         else:
                # don't use any pattern
                _lowerCamelCase						:    Any         =			0
                _lowerCamelCase						:    List[str]         =			task_id_and_num_shards[0][0]
                self._rename(
                    fpath.replace('''SSSSS'''						, f'''{shard_id:05d}'''		).replace('''TTTTT'''						, f'''{task_id:05d}'''		)						, fpath.replace(__lowerCAmelCase						, ''''''		)						, )




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						"datasets.SplitGenerator"						, ):






         """simple docstring"""






         return SparkExamplesIterable(self.df		)







"""simple docstring"""






def 	snake_case_  (				A_ :		list						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			len(A_						)
       for i in range(1,    A_						):
              _lowerCamelCase						:    Tuple         =			collection[i]
              _lowerCamelCase						:    Dict         =			0
              _lowerCamelCase						:    Any         =			i - 1

              while low <= high:
                     _lowerCamelCase						:    Optional[int]         =			(low + high) // 2
                     if val < collection[mid]:
                            _lowerCamelCase						:    List[str]         =			mid - 1
                     else:
                            _lowerCamelCase						:    Dict         =			mid + 1
              for j in range(A_,    A_,    -1						):
                     _lowerCamelCase						:    Optional[int]         =			collection[j - 1]
              _lowerCamelCase						:    Tuple         =			val
       return collection


if __name__ == "__main__":
   lowerCAmelCase__      =    input('''Enter numbers separated by a comma:\n''').strip()
   lowerCAmelCase__      =    [int(item) for item in user_input.split(''',''')]
   print(binary_insertion_sort(unsorted))


