






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

if TYPE_CHECKING:
							import pyspark




@dataclass
class 		UpperCamelCase__     (datasets.BuilderConfig      ):




		'''simple docstring'''





		_UpperCamelCase						=       None



def     A__      (					__lowerCAmelCase		:      "pyspark.sql.DataFrame" ,     __lowerCAmelCase		:      List[int] ,     ):
						import pyspark

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

						return generate_fn




class 		UpperCamelCase__     (_BaseExamplesIterable      ):




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase=None ,):
								lowerCamelCase__								=	df
								lowerCamelCase__								=	partition_order or range(self.df.rdd.getNumPartitions()							)
								lowerCamelCase__								=	_generate_iterable_examples(self.df ,self.partition_order							)






		def __iter__(   self							):
								yield from self.generate_examples_fn()






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	list(range(self.df.rdd.getNumPartitions()							)							)
								generator.shuffle(_UpperCamelCase							)
								return SparkExamplesIterable(self.df ,partition_order=_UpperCamelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.split_shard_indices_by_worker(_UpperCamelCase ,_UpperCamelCase							)
								return SparkExamplesIterable(self.df ,partition_order=_UpperCamelCase							)






		@property
		def       UpperCamelCase_       (   self							):
								return len(self.partition_order							)




class 		UpperCamelCase__     (datasets.DatasetBuilder      ):




		'''simple docstring'''





		_UpperCamelCase						=       SparkConfig






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

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

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






		def       UpperCamelCase_       (   self							):
								# Returns the path of the created file.
								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=_UpperCamelCase							)
														lowerCamelCase__								=	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:
														lowerCamelCase__								=	(
														    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							):
								return datasets.DatasetInfo(features=self.config.features							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							)]






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								import pyspark

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

								lowerCamelCase__								=	self.df.count()
								lowerCamelCase__								=	df_num_rows if df_num_rows <= 1_00 else 1_00
								# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
								lowerCamelCase__								=	(
								    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
								)
								lowerCamelCase__								=	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__								=	min(_UpperCamelCase ,int(approx_total_size / max_shard_size							)							)
														lowerCamelCase__								=	self.df.repartition(_UpperCamelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								import pyspark

								lowerCamelCase__								=	ParquetWriter if file_format == """parquet""" else ArrowWriter
								lowerCamelCase__								=	os.path.join(self._working_dir ,os.path.basename(_UpperCamelCase							)							) if self._working_dir else fpath
								lowerCamelCase__								=	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__								=	self.config.features
								lowerCamelCase__								=	self._writer_batch_size
								lowerCamelCase__								=	self._fs.storage_options

								def write_arrow(_lowerCAmelCase							):
														# Within the same SparkContext, no two task attempts will share the same attempt ID.
														lowerCamelCase__								=	pyspark.TaskContext().taskAttemptId()
														lowerCamelCase__								=	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"""] ,)
														lowerCamelCase__								=	0
														lowerCamelCase__								=	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 ,)
														lowerCamelCase__								=	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:
																										lowerCamelCase__							,      lowerCamelCase__								=	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__								=	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 ,)
																				lowerCamelCase__								=	pa.Table.from_batches([batch]							)
																				writer.write_table(_UpperCamelCase							)

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

								lowerCamelCase__								=	(
								    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 ,_lowerCAmelCase ,_lowerCAmelCase = "arrow" ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):
								self._validate_cache_dir()

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

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

								lowerCamelCase__								=	0
								lowerCamelCase__								=	0
								lowerCamelCase__								=	0
								lowerCamelCase__								=	[]
								lowerCamelCase__								=	[]

								for task_id, content in self._prepare_split_single(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							):
														(
														    (
														    lowerCamelCase__				
														)							,      (
														    lowerCamelCase__				
														)							,      (
														    lowerCamelCase__				
														)							,      (
														    lowerCamelCase__				
														)							,      				
														)								=	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							)

								lowerCamelCase__								=	total_num_examples
								lowerCamelCase__								=	total_num_bytes

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

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

														lowerCamelCase__								=	[]
														lowerCamelCase__								=	0
														for i in range(len(_UpperCamelCase							)							):
																				lowerCamelCase__							,      lowerCamelCase__								=	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 _lowerCAmelCase							: _rename_shard(*_UpperCamelCase							)							).collect()
								else:
														# don't use any pattern
														lowerCamelCase__								=	0
														lowerCamelCase__								=	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 ,_lowerCAmelCase ,):
								return SparkExamplesIterable(self.df							)





'''simple docstring'''
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip





