





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

if TYPE_CHECKING:
       import pyspark


@dataclass
class        __SCREAMING_SNAKE_CASE      (			datasets.BuilderConfig ):
       _UpperCAmelCase				:  Optional[datasets.Features]			   =   None



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  )    ->    Any:







   """simple docstring"""
   import pyspark

   def generate_fn():
      lowerCamelCase__       :						Tuple        =							df.select('''*'''      ,  pyspark.sql.functions.spark_partition_id().alias('''part_id'''					)					)
      for partition_id in partition_order:
         lowerCamelCase__       :						int        =							df_with_partition_id.select('''*'''					).where(f"part_id = {partition_id}"					).drop('''part_id'''					)
         lowerCamelCase__       :						str        =							partition_df.collect()
         lowerCamelCase__       :						Dict        =							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					:     Any		, A					:     "pyspark.sql.DataFrame"		, A					:     Optional[Any]=None		, )       ->int:
          lowerCamelCase__       :						Any        =							df
          lowerCamelCase__       :						Any        =							partition_order or range(self.df.rdd.getNumPartitions()      )
          lowerCamelCase__       :						Optional[int]        =							_generate_iterable_examples(self.df		, self.partition_order      )







       def __iter__(				self					:     Tuple      )       ->Optional[int]:
          yield from self.generate_examples_fn()







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     np.random.Generator      )       ->"SparkExamplesIterable":
          lowerCamelCase__       :						Any        =							list(range(self.df.rdd.getNumPartitions()      )      )
          generator.shuffle(A      )
          return SparkExamplesIterable(self.df		, partition_order=A      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     int		, A					:     int      )       ->"SparkExamplesIterable":
          lowerCamelCase__       :						Optional[int]        =							self.split_shard_indices_by_worker(A		, A      )
          return SparkExamplesIterable(self.df		, partition_order=A      )







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


class        __SCREAMING_SNAKE_CASE      (			datasets.DatasetBuilder ):
       _UpperCAmelCase				:  List[str]			   =   SparkConfig







       def __init__(				self					:     Union[str, Any]		, A					:     "pyspark.sql.DataFrame"		, A					:     str = None		, A					:     str = None		, **A					:     List[str]		, )       ->str:
          import pyspark

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

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







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[Any]:
          # Returns the path of the created file.
          def create_cache_and_write_probe(A					:     List[str]      ):
             # 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=A      )
             lowerCamelCase__       :						Union[str, Any]        =							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(A		, '''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__       :						str        =							(
                 self._spark.sparkContext.parallelize(range(1      )		, 1      ).mapPartitions(A      ).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 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          return datasets.DatasetInfo(features=self.config.features      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     datasets.download.download_manager.DownloadManager      )       ->Union[str, Any]:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Optional[Any]      )       ->str:
          import pyspark

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

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







       def 						__lowerCamelCase   (				self					:     int		, A					:     str		, A					:     str		, A					:     int		, )       ->Iterable[Tuple[int, bool, Union[int, tuple]]]:
          import pyspark

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

          def write_arrow(A					:     Optional[int]      ):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             lowerCamelCase__       :						List[Any]        =							pyspark.TaskContext().taskAttemptId()
             lowerCamelCase__       :						Union[str, Any]        =							next(A		, A      )
             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[str]        =							0
             lowerCamelCase__       :						Tuple        =							writer_class(
                 features=A		, path=working_fpath.replace('''SSSSS'''		, F"{shard_id:05d}"      ).replace('''TTTTT'''		, F"{task_id:05d}"      )		, writer_batch_size=A		, storage_options=A		, embed_local_files=A		, )
             lowerCamelCase__       :						int        =							pa.Table.from_batches([first_batch]      )
             writer.write_table(A      )
             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__       :						Any        =							writer_class(
                       features=writer._features		, path=working_fpath.replace('''SSSSS'''		, F"{shard_id:05d}"      ).replace('''TTTTT'''		, F"{task_id:05d}"      )		, writer_batch_size=A		, storage_options=A		, embed_local_files=A		, )
                lowerCamelCase__       :						str        =							pa.Table.from_batches([batch]      )
                writer.write_table(A      )

             if writer._num_bytes > 0:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[Any]        =							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(A      )      ):
                   lowerCamelCase__       :						Dict        =							os.path.join(os.path.dirname(A      )		, os.path.basename(A      )      )
                   shutil.move(A		, A      )

          lowerCamelCase__       :						Union[str, Any]        =							(
              self.df.mapInArrow(A		, '''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 						__lowerCamelCase   (				self					:     Any		, A					:     "datasets.SplitGenerator"		, A					:     str = "arrow"		, A					:     Optional[Union[str, int]] = None		, A					:     Optional[int] = None		, **A					:     Union[str, Any]		, )       ->Dict:
          self._validate_cache_dir()

          lowerCamelCase__       :						Tuple        =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE      )
          self._repartition_df_if_needed(A      )
          lowerCamelCase__       :						Union[str, Any]        =							not is_remote_filesystem(self._fs      )
          lowerCamelCase__       :						List[Any]        =							os.path.join if is_local else posixpath.join

          lowerCamelCase__       :						List[str]        =							'''-TTTTT-SSSSS-of-NNNNN'''
          lowerCamelCase__       :						Union[str, Any]        =							F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
          lowerCamelCase__       :						Dict        =							path_join(self._output_dir		, A      )

          lowerCamelCase__       :						List[str]        =							0
          lowerCamelCase__       :						Tuple        =							0
          lowerCamelCase__       :						str        =							0
          lowerCamelCase__       :						str        =							[]
          lowerCamelCase__       :						Any        =							[]

          for task_id, content in self._prepare_split_single(A		, A		, A      ):
             (
                 (
                 lowerCamelCase__
             )       ,							(
                 lowerCamelCase__
             )       ,							(
                 lowerCamelCase__
             )       ,							(
                 lowerCamelCase__
             )       ,							
             )       :						Optional[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(A      )

          lowerCamelCase__       :						List[Any]        =							total_num_examples
          lowerCamelCase__       :						Union[str, Any]        =							total_num_bytes

          # should rename everything at the end
          logger.debug(F"Renaming {total_shards} shards."      )
          if total_shards > 1:
             lowerCamelCase__       :						int        =							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(
                 A					:     int		, A					:     int		, A					:     int		, ):
                rename(
                    A		, 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__       :						str        =							[]
             lowerCamelCase__       :						Dict        =							0
             for i in range(len(A      )      ):
                lowerCamelCase__       ,							lowerCamelCase__       :						List[Any]        =							task_id_and_num_shards[i]
                for shard_id in range(A      ):
                   args.append([task_id, shard_id, global_shard_id]      )
                   global_shard_id += 1
             self._spark.sparkContext.parallelize(A		, len(A      )      ).map(lambda A      : _rename_shard(*A      )      ).collect()
          else:
             # don't use any pattern
             lowerCamelCase__       :						Optional[int]        =							0
             lowerCamelCase__       :						Optional[Any]        =							task_id_and_num_shards[0][0]
             self._rename(
                 fpath.replace('''SSSSS'''		, F"{shard_id:05d}"      ).replace('''TTTTT'''		, F"{task_id:05d}"      )		, fpath.replace(A		, ''''''      )		, )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     "datasets.SplitGenerator"		, )       ->SparkExamplesIterable:
          return SparkExamplesIterable(self.df      )










def 	_a						(   UpperCAmelCase					)    ->    bool:







   """simple docstring"""
   return str(UpperCAmelCase					) == str(UpperCAmelCase					)[::-1]



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return int(UpperCAmelCase					) + int(str(UpperCAmelCase					)[::-1]					)



def 	_a						(   UpperCAmelCase = 10000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]
   for num in range(1      ,  UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							0
      lowerCamelCase__       :						Union[str, Any]        =							num
      while iterations < 50:
         lowerCamelCase__       :						Dict        =							sum_reverse(UpperCAmelCase					)
         iterations += 1
         if is_palindrome(UpperCAmelCase					):
            break
      else:
         lychrel_nums.append(UpperCAmelCase					)
   return len(UpperCAmelCase					)


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



