





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							:       List[str]					 =	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
							import pyspark





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   int       ,			)      ->    Tuple:

						"""simple docstring"""





						import pyspark

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

						return generate_fn





class 				a			(      _BaseExamplesIterable			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   df
										__SCREAMING_SNAKE_CASE:				List[Any]           =   partition_order or range(self.df.rdd.getNumPartitions()					)
										__SCREAMING_SNAKE_CASE:				str           =   _generate_iterable_examples(self.df			, self.partition_order					)






				def __iter__(  self					):

										"""simple docstring"""




										yield from self.generate_examples_fn()






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(snake_case_					)
										return SparkExamplesIterable(self.df			, partition_order=snake_case_					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.split_shard_indices_by_worker(snake_case_			, snake_case_					)
										return SparkExamplesIterable(self.df			, partition_order=snake_case_					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return len(self.partition_order					)





class 				a			(      datasets.DatasetBuilder			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     SparkConfig






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										import pyspark

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

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										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=snake_case_					)
																__SCREAMING_SNAKE_CASE:				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(snake_case_			, '''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:				int           =   (
																    self._spark.sparkContext.parallelize(range(1					)			, 1					).mapPartitions(snake_case_					).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 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										import pyspark

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

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, ):

										"""simple docstring"""




										import pyspark

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

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

																if writer._num_bytes > 0:
																						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Union[str, 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(snake_case_					)					):
																												__SCREAMING_SNAKE_CASE:				List[Any]           =   os.path.join(os.path.dirname(snake_case_					)			, os.path.basename(snake_case_					)					)
																												shutil.move(snake_case_			, snake_case_					)

										__SCREAMING_SNAKE_CASE:				Dict           =   (
										    self.df.mapInArrow(snake_case_			, '''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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = "arrow"			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										self._validate_cache_dir()

										__SCREAMING_SNAKE_CASE:				int           =   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(snake_case_					)
										__SCREAMING_SNAKE_CASE:				Any           =   not is_remote_filesystem(self._fs					)
										__SCREAMING_SNAKE_CASE:				int           =   os.path.join if is_local else posixpath.join

										__SCREAMING_SNAKE_CASE:				Any           =   '''-TTTTT-SSSSS-of-NNNNN'''
										__SCREAMING_SNAKE_CASE:				int           =   f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
										__SCREAMING_SNAKE_CASE:				Dict           =   path_join(self._output_dir			, snake_case_					)

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

										for task_id, content in self._prepare_split_single(snake_case_			, snake_case_			, snake_case_					):
																(
																    (
																    __SCREAMING_SNAKE_CASE			
																)       ,(
																    __SCREAMING_SNAKE_CASE			
																)       ,(
																    __SCREAMING_SNAKE_CASE			
																)       ,(
																    __SCREAMING_SNAKE_CASE			
																)       ,			
																):				Union[str, Any]           =   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(snake_case_					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   total_num_examples
										__SCREAMING_SNAKE_CASE:				Any           =   total_num_bytes

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

																# use the -SSSSS-of-NNNNN pattern
																def _rename_shard(
																    _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, ):
																						rename(
																						    snake_case_			, 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:				Tuple           =   []
																__SCREAMING_SNAKE_CASE:				str           =   0
																for i in range(len(snake_case_					)					):
																						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   task_id_and_num_shards[i]
																						for shard_id in range(snake_case_					):
																												args.append([task_id, shard_id, global_shard_id]					)
																												global_shard_id += 1
																self._spark.sparkContext.parallelize(snake_case_			, len(snake_case_					)					).map(lambda _lowerCAmelCase					: _rename_shard(*snake_case_					)					).collect()
										else:
																# don't use any pattern
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   0
																__SCREAMING_SNAKE_CASE:				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(snake_case_			, ''''''					)			, )






				def 			snake_case_    (  self			, _lowerCAmelCase			, ):

										"""simple docstring"""




										return SparkExamplesIterable(self.df					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A							:			Optional[Any]         =			{}


try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[int]         =			["MLukeTokenizer"]

if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mluke import MLukeTokenizer


else:
 import sys

 __A							:			List[str]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





