






'''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


_A     :      Dict   					=datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
			import pyspark

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      )				->	Tuple:
							import pyspark

							def generate_fn():
														lowerCamelCase__ :     List[str]	  =	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__ :     str	  =	partition_df.collect()
																					lowerCamelCase__ :     Optional[Any]	  =	0
																					for row in rows:
																												yield f'''{partition_id}_{row_id}''', row.asDict()
																												row_id += 1

							return generate_fn

class _lowercase     (    _BaseExamplesIterable		):




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    "pyspark.sql.DataFrame"      ,							UpperCamelCase__:    Dict=None      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	df
														lowerCamelCase__ :     List[str]	  =	partition_order or range(self.df.rdd.getNumPartitions()					)
														lowerCamelCase__ :     int	  =	_generate_iterable_examples(self.df      ,							self.partition_order					)




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




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    np.random.Generator					):
														lowerCamelCase__ :     Any	  =	list(range(self.df.rdd.getNumPartitions()					)					)
														generator.shuffle(UpperCamelCase__					)
														return SparkExamplesIterable(self.df      ,							partition_order=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     List[str]	  =	self.split_shard_indices_by_worker(UpperCamelCase__      ,							UpperCamelCase__					)
														return SparkExamplesIterable(self.df      ,							partition_order=UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														return len(self.partition_order					)



class _lowercase     (    datasets.DatasetBuilder		):
							a    				=	SparkConfig




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

														lowerCamelCase__ :     str	  =	pyspark.sql.SparkSession.builder.getOrCreate()
														lowerCamelCase__ :     Union[str, Any]	  =	df
														lowerCamelCase__ :     Optional[Any]	  =	working_dir

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




							def 		lowerCamelCase_    (							self:    List[str]					):
														# Returns the path of the created file.
														def create_cache_and_write_probe(UpperCamelCase__:    List[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=UpperCamelCase__					)
																					lowerCamelCase__ :     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(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__ :     Any	  =	(
																					    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 		lowerCamelCase_    (							self:    int					):
														return datasets.DatasetInfo(features=self.config.features					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    datasets.download.download_manager.DownloadManager					):
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    Dict					):
														import pyspark

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

														lowerCamelCase__ :     List[Any]	  =	self.df.count()
														lowerCamelCase__ :     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.
														lowerCamelCase__ :     Optional[int]	  =	(
														    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__ :     str	  =	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__ :     Optional[Any]	  =	min(UpperCamelCase__      ,							int(approx_total_size / max_shard_size					)					)
																					lowerCamelCase__ :     int	  =	self.df.repartition(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    int      ,							):
														import pyspark

														lowerCamelCase__ :     Optional[Any]	  =	ParquetWriter if file_format == """parquet""" else ArrowWriter
														lowerCamelCase__ :     Optional[Any]	  =	os.path.join(self._working_dir      ,							os.path.basename(UpperCamelCase__					)					) if self._working_dir else fpath
														lowerCamelCase__ :     Tuple	  =	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__ :     int	  =	self.config.features
														lowerCamelCase__ :     Tuple	  =	self._writer_batch_size
														lowerCamelCase__ :     List[Any]	  =	self._fs.storage_options

														def write_arrow(UpperCamelCase__:    Optional[int]					):
																					# Within the same SparkContext, no two task attempts will share the same attempt ID.
																					lowerCamelCase__ :     str	  =	pyspark.TaskContext().taskAttemptId()
																					lowerCamelCase__ :     Optional[int]	  =	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__ :     Dict	  =	0
																					lowerCamelCase__ :     Optional[int]	  =	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__ :     Optional[Any]	  =	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__ :     Dict	  =	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[Any]	  =	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__ :     List[str]	  =	pa.Table.from_batches([batch]					)
																												writer.write_table(UpperCamelCase__					)

																					if writer._num_bytes > 0:
																												lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	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__ :     List[str]	  =	os.path.join(os.path.dirname(UpperCamelCase__					)      ,							os.path.basename(UpperCamelCase__					)					)
																																			shutil.move(UpperCamelCase__      ,							UpperCamelCase__					)

														lowerCamelCase__ :     Union[str, Any]	  =	(
														    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 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    "datasets.SplitGenerator"      ,							UpperCamelCase__:    str = "arrow"      ,							UpperCamelCase__:    Optional[Union[str, int]] = None      ,							UpperCamelCase__:    Optional[int] = None      ,							**UpperCamelCase__:    str      ,							):
														self._validate_cache_dir()

														lowerCamelCase__ :     List[Any]	  =	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
														self._repartition_df_if_needed(UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	not is_remote_filesystem(self._fs					)
														lowerCamelCase__ :     List[Any]	  =	os.path.join if is_local else posixpath.join

														lowerCamelCase__ :     Dict	  =	"""-TTTTT-SSSSS-of-NNNNN"""
														lowerCamelCase__ :     List[Any]	  =	F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
														lowerCamelCase__ :     Tuple	  =	path_join(self._output_dir      ,							UpperCamelCase__					)

														lowerCamelCase__ :     Optional[int]	  =	0
														lowerCamelCase__ :     List[Any]	  =	0
														lowerCamelCase__ :     Any	  =	0
														lowerCamelCase__ :     List[Any]	  =	[]
														lowerCamelCase__ :     Any	  =	[]

														for task_id, content in self._prepare_split_single(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					):
																					(
																					    (
																					    lowerCamelCase__
																					)  ,		(
																					    lowerCamelCase__
																					)  ,		(
																					    lowerCamelCase__
																					)  ,		(
																					    lowerCamelCase__
																					)  ,		
																					) :     int	  =	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__ :     Optional[Any]	  =	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__ :     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__ :     List[str]	  =	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}'''					)      ,							)

																					lowerCamelCase__ :     Any	  =	[]
																					lowerCamelCase__ :     List[Any]	  =	0
																					for i in range(len(UpperCamelCase__					)					):
																												lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	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
																					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(UpperCamelCase__      ,							""""""					)      ,							)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    "datasets.SplitGenerator"      ,							):
														return SparkExamplesIterable(self.df					)









'''simple docstring'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

