


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


__UpperCamelCase       :       int						       =				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
       import pyspark

@dataclass
class       __magic_name__	(     datasets.BuilderConfig):
     A:		Optional[datasets.Features]			=      None



def 			_a  (				SCREAMING_SNAKE_CASE	:				"pyspark.sql.DataFrame"    , SCREAMING_SNAKE_CASE	:				List[int]    , ):







 """simple docstring"""



 import pyspark

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

 return generate_fn

class       __magic_name__	(     _BaseExamplesIterable):


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     "pyspark.sql.DataFrame"							,	lowerCamelCase__							:     List[Any]=None							,	)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   df
      UpperCamelCase__					:    Dict          =   partition_order or range(self.df.rdd.getNumPartitions()			)
      UpperCamelCase__					:    Dict          =   _generate_iterable_examples(self.df							,	self.partition_order			)


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





      '''simple docstring'''






      yield from self.generate_examples_fn()


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     np.random.Generator			)  ->      "SparkExamplesIterable":





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   list(range(self.df.rdd.getNumPartitions()			)			)
      generator.shuffle(lowerCamelCase__			)
      return SparkExamplesIterable(self.df							,	partition_order=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      "SparkExamplesIterable":





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.split_shard_indices_by_worker(lowerCamelCase__							,	lowerCamelCase__			)
      return SparkExamplesIterable(self.df							,	partition_order=lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      int:





      '''simple docstring'''






      return len(self.partition_order			)



class       __magic_name__	(     datasets.DatasetBuilder):
     A:		List[Any]			=      SparkConfig


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     "pyspark.sql.DataFrame"							,	lowerCamelCase__							:     str = None							,	lowerCamelCase__							:     str = None							,	**lowerCamelCase__							:     Dict							,	)  ->      Tuple:





      '''simple docstring'''






      import pyspark

      UpperCamelCase__					:    List[str]          =   pyspark.sql.SparkSession.builder.getOrCreate()
      UpperCamelCase__					:    Tuple          =   df
      UpperCamelCase__					:    Any          =   working_dir

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      Dict:





      '''simple docstring'''






      def create_cache_and_write_probe(lowerCamelCase__							:     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=lowerCamelCase__			)
       UpperCamelCase__					:    Optional[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(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:
       UpperCamelCase__					:    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      UpperCAmelCase__	(      self							:     int			)  ->      List[str]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     datasets.download.download_manager.DownloadManager			)  ->      Dict:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Any:





      '''simple docstring'''






      import pyspark

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

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


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	)  ->      Iterable[Tuple[int, bool, Union[int, tuple]]]:





      '''simple docstring'''






      import pyspark

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

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

       if writer._num_bytes > 0:
        UpperCamelCase__       ,				UpperCamelCase__					:    Tuple          =   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__			)			):
         UpperCamelCase__					:    Tuple          =   os.path.join(os.path.dirname(lowerCamelCase__			)							,	os.path.basename(lowerCamelCase__			)			)
         shutil.move(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Union[str, 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      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     "datasets.SplitGenerator"							,	lowerCamelCase__							:     str = "arrow"							,	lowerCamelCase__							:     Optional[Union[str, int]] = None							,	lowerCamelCase__							:     Optional[int] = None							,	**lowerCamelCase__							:     Dict							,	)  ->      Optional[Any]:





      '''simple docstring'''






      self._validate_cache_dir()

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

      UpperCamelCase__					:    Tuple          =   '''-TTTTT-SSSSS-of-NNNNN'''
      UpperCamelCase__					:    Optional[int]          =   F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
      UpperCamelCase__					:    List[str]          =   path_join(self._output_dir							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   0
      UpperCamelCase__					:    Union[str, Any]          =   0
      UpperCamelCase__					:    Dict          =   0
      UpperCamelCase__					:    Dict          =   []
      UpperCamelCase__					:    List[str]          =   []

      for task_id, content in self._prepare_split_single(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			):
       (
           (
           UpperCamelCase__
       )       ,				(
           UpperCamelCase__
       )       ,				(
           UpperCamelCase__
       )       ,				(
           UpperCamelCase__
       )       ,				
       )					:    Tuple          =   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__			)

      UpperCamelCase__					:    Tuple          =   total_num_examples
      UpperCamelCase__					:    Any          =   total_num_bytes

      # should rename everything at the end
      logger.debug(F"Renaming {total_shards} shards."			)
      if total_shards > 1:
       UpperCamelCase__					:    List[str]          =   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.
       UpperCamelCase__					:    Any          =   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}"			)							,	)

       UpperCamelCase__					:    Optional[int]          =   []
       UpperCamelCase__					:    Dict          =   0
       for i in range(len(lowerCamelCase__			)			):
        UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   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
       UpperCamelCase__					:    Tuple          =   0
       UpperCamelCase__					:    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(lowerCamelCase__							,	''''''			)							,	)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     "datasets.SplitGenerator"							,	)  ->      SparkExamplesIterable:





      '''simple docstring'''






      return SparkExamplesIterable(self.df			)






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


