



"""simple docstring"""






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowerCAmelCase__      =    get_logger(__name__)






class 	__snake_case	:
  snake_case__    :       Any        =						"dummy_data"
  snake_case__    :       Tuple        =						"datasets"
  snake_case__    :       Optional[Any]        =						False




  def __init__(			self					:						Optional[int]						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Union[Version, str]						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						bool = True						, __lowerCAmelCase					:						Optional[List[Callable]] = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			0
         _lowerCamelCase						:    str         =			dataset_name
         _lowerCamelCase						:    Optional[Any]         =			cache_dir
         _lowerCamelCase						:    Tuple         =			use_local_dummy_data
         _lowerCamelCase						:    Tuple         =			config
         # download_callbacks take a single url as input
         _lowerCamelCase						:    List[Callable]         =			download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         _lowerCamelCase						:    Dict         =			load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         _lowerCamelCase						:    Union[str, Any]         =			str(__lowerCAmelCase		)
         # to be downloaded
         _lowerCamelCase						:    Any         =			None
         _lowerCamelCase						:    Any         =			None




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         if self._dummy_file is None:
                _lowerCamelCase						:    Dict         =			self.download_dummy_data()
         return self._dummy_file




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         if self.config is not None:
                # structure is dummy / config_name / version_name
                return os.path.join('''dummy'''						, self.config.name						, self.version_name		)
         # structure is dummy / version_name
         return os.path.join('''dummy'''						, self.version_name		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         return os.path.join(self.dummy_data_folder						, '''dummy_data.zip'''		)




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






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         _lowerCamelCase						:    List[Any]         =			cached_path(
             __lowerCAmelCase						, cache_dir=self.cache_dir						, extract_compressed_file=__lowerCAmelCase						, force_extract=__lowerCAmelCase		)

         return os.path.join(__lowerCAmelCase						, self.dummy_file_name		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         return os.path.join(self.datasets_scripts_dir						, self.dataset_name						, self.dummy_zip_file		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         if self._bucket_url is None:
                _lowerCamelCase						:    List[Any]         =			hf_github_url(self.dataset_name						, self.dummy_zip_file.replace(os.sep						, '''/'''		)		)
         return self._bucket_url




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         if os.path.isdir(self.dummy_file		):
                return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep						, '''/'''		).split('''/'''		)[:-1]		)




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






         """simple docstring"""






         if self.load_existing_dummy_data:
                # dummy data is downloaded and tested
                _lowerCamelCase						:    Union[str, Any]         =			self.dummy_file
         else:
                # dummy data cannot be downloaded and only the path to dummy file is returned
                _lowerCamelCase						:    Optional[Any]         =			self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                return self.create_dummy_data_dict(__lowerCAmelCase						, __lowerCAmelCase		)
         elif isinstance(__lowerCAmelCase						, (list, tuple)		):
                return self.create_dummy_data_list(__lowerCAmelCase						, __lowerCAmelCase		)
         else:
                return self.create_dummy_data_single(__lowerCAmelCase						, __lowerCAmelCase		)




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






         """simple docstring"""






         return self.download_and_extract(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         return self.download_and_extract(__lowerCAmelCase		)




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






         """simple docstring"""






         return path




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         return {}




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			{}
         for key, single_urls in data_url.items():
                for download_callback in self.download_callbacks:
                       if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                              for single_url in single_urls:
                                     download_callback(__lowerCAmelCase		)
                       else:
                              _lowerCamelCase						:    Optional[int]         =			single_urls
                              download_callback(__lowerCAmelCase		)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                       _lowerCamelCase						:    List[Any]         =			[os.path.join(__lowerCAmelCase						, urllib.parse.quote_plus(Path(__lowerCAmelCase		).name		)		) for x in single_urls]
                else:
                       _lowerCamelCase						:    Dict         =			single_urls
                       _lowerCamelCase						:    str         =			os.path.join(__lowerCAmelCase						, urllib.parse.quote_plus(Path(__lowerCAmelCase		).name		)		)
                _lowerCamelCase						:    List[Any]         =			value

         # make sure that values are unique
         if all(isinstance(__lowerCAmelCase						, __lowerCAmelCase		) for i in dummy_data_dict.values()		) and len(set(dummy_data_dict.values()		)		) < len(
             dummy_data_dict.values()		):
                # append key to value to make its name unique
                _lowerCamelCase						:    Optional[int]         =			{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict




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






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         _lowerCamelCase						:    Optional[Any]         =			all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''						, __lowerCAmelCase		)		) for url in data_url		)
         _lowerCamelCase						:    List[str]         =			all(
             url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''		) for url in data_url		)
         if data_url and (is_tf_records or is_pubmed_records):
                _lowerCamelCase						:    str         =			[data_url[0]] * len(__lowerCAmelCase		)
         for single_url in data_url:
                for download_callback in self.download_callbacks:
                       download_callback(__lowerCAmelCase		)
                # we force the name of each key to be the last file / folder name of the url path
                # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                _lowerCamelCase						:    List[str]         =			os.path.join(__lowerCAmelCase						, urllib.parse.quote_plus(single_url.split('''/'''		)[-1]		)		)
                dummy_data_list.append(__lowerCAmelCase		)
         return dummy_data_list




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






         """simple docstring"""






         for download_callback in self.download_callbacks:
                download_callback(__lowerCAmelCase		)
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         _lowerCamelCase						:    Any         =			os.path.join(__lowerCAmelCase						, urllib.parse.quote_plus(data_url.split('''/'''		)[-1]		)		)
         if os.path.exists(__lowerCAmelCase		) or not self.load_existing_dummy_data:
                return value
         else:
                # Backward compatibility, maybe deprecate at one point.
                # For many datasets with single url calls to dl_manager.download_and_extract,
                # the dummy_data.zip file is actually the zipped downloaded file
                # while now we expected the dummy_data.zip file to be a directory containing
                # the downloaded file.
                return path_to_dummy_data




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         pass




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






         """simple docstring"""






         def _iter_archive_members(__lowerCAmelCase					:						str		):
                # this preserves the order of the members inside the ZIP archive
                _lowerCamelCase						:    Tuple         =			Path(self.dummy_file		).parent
                _lowerCamelCase						:    List[Any]         =			path.relative_to(__lowerCAmelCase		)
                with ZipFile(self.local_path_to_dummy_data		) as zip_file:
                       _lowerCamelCase						:    Optional[Any]         =			zip_file.namelist()
                for member in members:
                       if member.startswith(relative_path.as_posix()		):
                              yield dummy_parent_path.joinpath(__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			Path(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			_iter_archive_members(__lowerCAmelCase		) if self.use_local_dummy_data else path.rglob('''*'''		)
         for file_path in file_paths:
                if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')		):
                       yield file_path.relative_to(__lowerCAmelCase		).as_posix(), file_path.open('''rb'''		)




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






         """simple docstring"""






         if not isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                _lowerCamelCase						:    Optional[Any]         =			[paths]
         for path in paths:
                if os.path.isfile(__lowerCAmelCase		):
                       if os.path.basename(__lowerCAmelCase		).startswith(('''.''', '''__''')		):
                              return
                       yield path
                else:
                       for dirpath, dirnames, filenames in os.walk(__lowerCAmelCase		):
                              if os.path.basename(__lowerCAmelCase		).startswith(('''.''', '''__''')		):
                                     continue
                              dirnames.sort()
                              for filename in sorted(__lowerCAmelCase		):
                                     if filename.startswith(('''.''', '''__''')		):
                                            continue
                                     yield os.path.join(__lowerCAmelCase						, __lowerCAmelCase		)







"""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		)


