






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 				=	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark



@dataclass
class       __UpperCAmelCase		(datasets.BuilderConfig    ):
			__snake_case				:      Tuple 						=							None

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,)							->  Optional[Any]:






						"""simple docstring"""





						import pyspark

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

						return generate_fn



class       __UpperCAmelCase		(_BaseExamplesIterable    ):







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  df
									_SCREAMING_SNAKE_CASE	      =  partition_order or range(self.df.rdd.getNumPartitions()     )
									_SCREAMING_SNAKE_CASE	      =  _generate_iterable_examples(self.df      ,	self.partition_order     )







			def __iter__(	self:  Optional[int]     ):







									'''simple docstring'''




									yield from self.generate_examples_fn()







			def 							UpperCamelCase      (	self:  Any      ,	UpperCAmelCase_:  np.random.Generator     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  list(range(self.df.rdd.getNumPartitions()     )     )
									generator.shuffle(UpperCAmelCase_     )
									return SparkExamplesIterable(self.df      ,	partition_order=UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  int      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.split_shard_indices_by_worker(UpperCAmelCase_      ,	UpperCAmelCase_     )
									return SparkExamplesIterable(self.df      ,	partition_order=UpperCAmelCase_     )







			@property
			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									return len(self.partition_order     )



class       __UpperCAmelCase		(datasets.DatasetBuilder    ):
			__snake_case				:      Optional[int] 						=							SparkConfig







			def __init__(	self:  List[Any]      ,	UpperCAmelCase_:  "pyspark.sql.DataFrame"      ,	UpperCAmelCase_:  str = None      ,	UpperCAmelCase_:  str = None      ,	**UpperCAmelCase_:  Optional[Any]      ,	):







									'''simple docstring'''




									import pyspark

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

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







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									def create_cache_and_write_probe(UpperCAmelCase_:  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=UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  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(UpperCAmelCase_      ,	"""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:
															_SCREAMING_SNAKE_CASE	      =  (
															    self._spark.sparkContext.parallelize(range(1     )      ,	1     ).mapPartitions(UpperCAmelCase_     ).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     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[Any]      ,	UpperCAmelCase_:  datasets.download.download_manager.DownloadManager     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[Any]      ,	UpperCAmelCase_:  Optional[int]     ):







									'''simple docstring'''




									import pyspark

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

									_SCREAMING_SNAKE_CASE	      =  self.df.count()
									_SCREAMING_SNAKE_CASE	      =  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.
									_SCREAMING_SNAKE_CASE	      =  (
									    self.df.limit(UpperCAmelCase_     )
									    .repartition(1     )
									    .mapInArrow(UpperCAmelCase_      ,	"""batch_bytes: long"""     )
									    .agg(pyspark.sql.functions.sum("""batch_bytes"""     ).alias("""sample_bytes"""     )     )
									    .collect()[0]
									    .sample_bytes
									    / sample_num_rows
									)
									_SCREAMING_SNAKE_CASE	      =  approx_bytes_per_row * df_num_rows
									if approx_total_size > max_shard_size:
															# Make sure there is at least one row per partition.
															_SCREAMING_SNAKE_CASE	      =  min(UpperCAmelCase_      ,	int(approx_total_size / max_shard_size     )     )
															_SCREAMING_SNAKE_CASE	      =  self.df.repartition(UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Union[str, Any]      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  int      ,	):







									'''simple docstring'''




									import pyspark

									_SCREAMING_SNAKE_CASE	      =  ParquetWriter if file_format == 'parquet' else ArrowWriter
									_SCREAMING_SNAKE_CASE	      =  os.path.join(self._working_dir      ,	os.path.basename(UpperCAmelCase_     )     ) if self._working_dir else fpath
									_SCREAMING_SNAKE_CASE	      =  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.
									_SCREAMING_SNAKE_CASE	      =  self.config.features
									_SCREAMING_SNAKE_CASE	      =  self._writer_batch_size
									_SCREAMING_SNAKE_CASE	      =  self._fs.storage_options

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

															if writer._num_bytes > 0:
																					_SCREAMING_SNAKE_CASE	      =  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(UpperCAmelCase_     )     ):
																											_SCREAMING_SNAKE_CASE	      =  os.path.join(os.path.dirname(UpperCAmelCase_     )      ,	os.path.basename(UpperCAmelCase_     )     )
																											shutil.move(UpperCAmelCase_      ,	UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  (
									    self.df.mapInArrow(UpperCAmelCase_      ,	"""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:  int      ,	UpperCAmelCase_:  "datasets.SplitGenerator"      ,	UpperCAmelCase_:  str = "arrow"      ,	UpperCAmelCase_:  Optional[Union[str, int]] = None      ,	UpperCAmelCase_:  Optional[int] = None      ,	**UpperCAmelCase_:  Any      ,	):







									'''simple docstring'''




									self._validate_cache_dir()

									_SCREAMING_SNAKE_CASE	      =  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE     )
									self._repartition_df_if_needed(UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  not is_remote_filesystem(self._fs     )
									_SCREAMING_SNAKE_CASE	      =  os.path.join if is_local else posixpath.join

									_SCREAMING_SNAKE_CASE	      =  '-TTTTT-SSSSS-of-NNNNN'
									_SCREAMING_SNAKE_CASE	      =  F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
									_SCREAMING_SNAKE_CASE	      =  path_join(self._output_dir      ,	UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  0
									_SCREAMING_SNAKE_CASE	      =  0
									_SCREAMING_SNAKE_CASE	      =  0
									_SCREAMING_SNAKE_CASE	      =  []
									_SCREAMING_SNAKE_CASE	      =  []

									for task_id, content in self._prepare_split_single(UpperCAmelCase_      ,	UpperCAmelCase_      ,	UpperCAmelCase_     ):
															(
															    _SCREAMING_SNAKE_CASE 
															)	      =  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(UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  total_num_examples
									_SCREAMING_SNAKE_CASE	      =  total_num_bytes

									# should rename everything at the end
									logger.debug(F'Renaming {total_shards} shards.'     )
									if total_shards > 1:
															_SCREAMING_SNAKE_CASE	      =  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.
															_SCREAMING_SNAKE_CASE	      =  self._fs

															# use the -SSSSS-of-NNNNN pattern
															def _rename_shard(
															    UpperCAmelCase_:  int      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  int      ,	):
																					rename(
																					    UpperCAmelCase_      ,	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}'     )      ,	)

															_SCREAMING_SNAKE_CASE	      =  []
															_SCREAMING_SNAKE_CASE	      =  0
															for i in range(len(UpperCAmelCase_     )     ):
																					_SCREAMING_SNAKE_CASE	      =  task_id_and_num_shards[i]
																					for shard_id in range(UpperCAmelCase_     ):
																											args.append([task_id, shard_id, global_shard_id]     )
																											global_shard_id += 1
															self._spark.sparkContext.parallelize(UpperCAmelCase_      ,	len(UpperCAmelCase_     )     ).map(lambda UpperCAmelCase_     : _rename_shard(*UpperCAmelCase_     )     ).collect()
									else:
															# don't use any pattern
															_SCREAMING_SNAKE_CASE	      =  0
															_SCREAMING_SNAKE_CASE	      =  task_id_and_num_shards[0][0]
															self._rename(
															    fpath.replace("""SSSSS"""      ,	F'{shard_id:05d}'     ).replace("""TTTTT"""      ,	F'{task_id:05d}'     )      ,	fpath.replace(UpperCAmelCase_      ,	""""""     )      ,	)







			def 							UpperCamelCase      (	self:  Tuple      ,	UpperCAmelCase_:  "datasets.SplitGenerator"      ,	):







									'''simple docstring'''




									return SparkExamplesIterable(self.df     )


def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

 def get_matched_characters(lowerCamelCase					:     str  ,  lowerCamelCase					:     str	) -> str:
  UpperCamelCase_					:						Tuple =					[]
  UpperCamelCase_					:						List[Any] =					min(len(_stra	)  ,  len(_stra	)	) // 2
  for i, l in enumerate(_stra	):
   UpperCamelCase_					:						int =					int(max(0  ,  i - limit	)	)
   UpperCamelCase_					:						Dict =					int(min(i + limit + 1  ,  len(_stra	)	)	)
   if l in _stra[left:right]:
    matched.append(lowerCamelCase	)
    UpperCamelCase_					:						Dict =					F"{_stra[0:_stra.index(lowerCamelCase	)]} {_stra[_stra.index(lowerCamelCase	) + 1:]}"

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					0
 for ca, ca in zip(stra[:4]  ,  stra[:4]	):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler('hello', 'world'))



