


'''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


UpperCAmelCase__  			=				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark
@dataclass
class 						a__					(					datasets.BuilderConfig   ):





			'''simple docstring'''

			A						:				Optional[datasets.Features]					      =			None






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: "pyspark.sql.DataFrame",_SCREAMING_SNAKE_CASE					: List[int],):






			"""simple docstring"""




			import pyspark

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

			return generate_fn
class 						a__					(					_BaseExamplesIterable   ):





			'''simple docstring'''

			def __init__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			"pyspark.sql.DataFrame"			,    lowerCAmelCase_							:			Tuple=None			,    )      ->		int:
						__A=	df
						__A=	partition_order or range(self.df.rdd.getNumPartitions()	)
						__A=	_generate_iterable_examples(self.df			,    self.partition_order	)
			def __iter__(  self							:			Dict	)      ->		Optional[Any]:
						yield from self.generate_examples_fn()
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			np.random.Generator	)      ->		"SparkExamplesIterable":
						__A=	list(range(self.df.rdd.getNumPartitions()	)	)
						generator.shuffle(lowerCAmelCase_	)
						return SparkExamplesIterable(self.df			,    partition_order=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int	)      ->		"SparkExamplesIterable":
						__A=	self.split_shard_indices_by_worker(lowerCAmelCase_			,    lowerCAmelCase_	)
						return SparkExamplesIterable(self.df			,    partition_order=lowerCAmelCase_	)

			@property
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						return len(self.partition_order	)


class 						a__					(					datasets.DatasetBuilder   ):





			'''simple docstring'''

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

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

						super().__init__(
						    cache_dir=lowerCAmelCase_			,    config_name=str(self.df.semanticHash()	)			,    **lowerCAmelCase_			,    )
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		str:
						# Returns the path of the created file.
						def create_cache_and_write_probe(lowerCAmelCase_							:			Union[str, Any]	):
									# 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_	)
									__A=	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:
									__A=	(
									    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 					lowerCAmelCase     (  self							:			List[Any]	)      ->		str:
						return datasets.DatasetInfo(features=self.config.features	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			datasets.download.download_manager.DownloadManager	)      ->		Any:
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN	)]
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		Tuple:
						import pyspark

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

						__A=	self.df.count()
						__A=	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.
						__A=	(
						    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
						)
						__A=	approx_bytes_per_row * df_num_rows
						if approx_total_size > max_shard_size:
									# Make sure there is at least one row per partition.
									__A=	min(lowerCAmelCase_			,    int(approx_total_size / max_shard_size	)	)
									__A=	self.df.repartition(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			int			,    )      ->		Iterable[Tuple[int, bool, Union[int, tuple]]]:
						import pyspark

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

						def write_arrow(lowerCAmelCase_							:			List[Any]	):
									# Within the same SparkContext, no two task attempts will share the same attempt ID.
									__A=	pyspark.TaskContext().taskAttemptId()
									__A=	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']			,    )
									__A=	0
									__A=	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_			,    )
									__A=	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:
															__A,	__A=	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
															__A=	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_			,    )
												__A=	pa.Table.from_batches([batch]	)
												writer.write_table(lowerCAmelCase_	)

									if writer._num_bytes > 0:
												__A,	__A=	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_	)	):
															__A=	os.path.join(os.path.dirname(lowerCAmelCase_	)			,    os.path.basename(lowerCAmelCase_	)	)
															shutil.move(lowerCAmelCase_			,    lowerCAmelCase_	)

						__A=	(
						    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 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			"datasets.SplitGenerator"			,    lowerCAmelCase_							:			str = "arrow"			,    lowerCAmelCase_							:			Optional[Union[str, int]] = None			,    lowerCAmelCase_							:			Optional[int] = None			,    **lowerCAmelCase_							:			Tuple			,    )      ->		Any:
						self._validate_cache_dir()

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

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

						__A=	0
						__A=	0
						__A=	0
						__A=	[]
						__A=	[]

						for task_id, content in self._prepare_split_single(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_	):
									(
									    (
									    __A
									),	(
									    __A
									),	(
									    __A
									),	(
									    __A
									),	
									)=	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_	)

						__A=	total_num_examples
						__A=	total_num_bytes

						# should rename everything at the end
						logger.debug(F"""Renaming {total_shards} shards."""	)
						if total_shards > 1:
									__A=	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.
									__A=	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}"""	)			,    )

									__A=	[]
									__A=	0
									for i in range(len(lowerCAmelCase_	)	):
												__A,	__A=	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
									__A=	0
									__A=	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 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			"datasets.SplitGenerator"			,    )      ->		SparkExamplesIterable:
						return SparkExamplesIterable(self.df	)







'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



