



"""simple docstring"""






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def 			_lowercase				(  __snake_case	)						->   Optional[int]:
			__lowerCAmelCase      :  Optional[Any]												=       np.inf

			def set_batch_size(__snake_case	) -> None:
						nonlocal batch_size
						if isinstance(__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       min(__snake_case					,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
						elif isinstance(__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       min(__snake_case					,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
						elif isinstance(__snake_case					,__snake_case	) and feature.dtype == "binary":
									__lowerCAmelCase      :  Tuple												=       min(__snake_case					,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

			_visit(__snake_case					,__snake_case	)

			return None if batch_size is np.inf else batch_size



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		NestedDataStructureLike[PathLike]       ,			_SCREAMING_SNAKE_CASE:		Optional[NamedSplit] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[Features] = None       ,			_SCREAMING_SNAKE_CASE:		str = None       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			**_SCREAMING_SNAKE_CASE:		Any       ,			)				->       Dict:

				"""simple docstring"""

				super().__init__(
				    _SCREAMING_SNAKE_CASE       ,			split=_SCREAMING_SNAKE_CASE       ,			features=_SCREAMING_SNAKE_CASE       ,			cache_dir=_SCREAMING_SNAKE_CASE       ,			keep_in_memory=_SCREAMING_SNAKE_CASE       ,			streaming=_SCREAMING_SNAKE_CASE       ,			num_proc=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)
				__lowerCAmelCase      :  Tuple												=       path_or_paths if isinstance(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
				__lowerCAmelCase      :  Any												=       _PACKAGED_DATASETS_MODULES["parquet"][1]
				__lowerCAmelCase      :  int												=       Parquet(
				    cache_dir=_SCREAMING_SNAKE_CASE       ,			data_files=_SCREAMING_SNAKE_CASE       ,			features=_SCREAMING_SNAKE_CASE       ,			hash=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[str]:

				"""simple docstring"""

				if self.streaming:
							__lowerCAmelCase      :  Tuple												=       self.builder.as_streaming_dataset(split=self.split)
				# Build regular (map-style) dataset
				else:
							__lowerCAmelCase      :  Union[str, Any]												=       None
							__lowerCAmelCase      :  int												=       None
							__lowerCAmelCase      :  Optional[int]												=       None
							__lowerCAmelCase      :  Union[str, Any]												=       None

							self.builder.download_and_prepare(
							    download_config=_SCREAMING_SNAKE_CASE       ,			download_mode=_SCREAMING_SNAKE_CASE       ,			verification_mode=_SCREAMING_SNAKE_CASE       ,			base_path=_SCREAMING_SNAKE_CASE       ,			num_proc=self.num_proc       ,			)
							__lowerCAmelCase      :  List[str]												=       self.builder.as_dataset(
							    split=self.split       ,			verification_mode=_SCREAMING_SNAKE_CASE       ,			in_memory=self.keep_in_memory)
				return dataset






class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Dataset       ,			_SCREAMING_SNAKE_CASE:		Union[PathLike, BinaryIO]       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			**_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       dataset
				__lowerCAmelCase      :  Optional[int]												=       path_or_buf
				__lowerCAmelCase      :  List[Any]												=       batch_size or get_writer_batch_size(dataset.features)
				__lowerCAmelCase      :  Dict												=       parquet_writer_kwargs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

				if isinstance(self.path_or_buf       ,			(str, bytes, os.PathLike)):
							with open(self.path_or_buf       ,			"wb+") as buffer:
										__lowerCAmelCase      :  Tuple												=       self._write(file_obj=_SCREAMING_SNAKE_CASE       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			**self.parquet_writer_kwargs)
				else:
							__lowerCAmelCase      :  Tuple												=       self._write(file_obj=self.path_or_buf       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			**self.parquet_writer_kwargs)
				return written







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		BinaryIO       ,			_SCREAMING_SNAKE_CASE:		int       ,			**_SCREAMING_SNAKE_CASE:		Optional[int])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       0
				__lowerCAmelCase      :  List[Any]												=       parquet_writer_kwargs.pop("path_or_buf"       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       self.dataset.features.arrow_schema

				__lowerCAmelCase      :  str												=       pq.ParquetWriter(_SCREAMING_SNAKE_CASE       ,			schema=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)

				for offset in logging.tqdm(
				    range(0       ,			len(self.dataset)       ,			_SCREAMING_SNAKE_CASE)       ,			unit="ba"       ,			disable=not logging.is_progress_bar_enabled()       ,			desc="Creating parquet from Arrow format"       ,			):
							__lowerCAmelCase      :  List[str]												=       query_table(
							    table=self.dataset._data       ,			key=slice(_SCREAMING_SNAKE_CASE       ,			offset + batch_size)       ,			indices=self.dataset._indices if self.dataset._indices is not None else None       ,			)
							writer.write_table(_SCREAMING_SNAKE_CASE)
							written += batch.nbytes
				writer.close()
				return written




"""simple docstring"""






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def 			_lowercase				(  *__snake_case	)						->   Optional[Any]:
			with open(__snake_case					,"r"	) as fh:
						fcntl.flock(__snake_case					,fcntl.LOCK_EX	)
						try:
									print(*__snake_case	)
						finally:
									fcntl.flock(__snake_case					,fcntl.LOCK_UN	)


__snake_case							:   List[Any]      =    int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__snake_case							:   List[str]      =    torch.device('cuda', local_rank)
__snake_case							:   Optional[Any]      =    socket.gethostname()

__snake_case							:   str      =    F"""[{hostname}-{local_rank}]"""

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__snake_case							:   Tuple      =    dist.get_rank()
	__snake_case							:   Any      =    dist.get_world_size()

	printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

	dist.barrier()
	if rank == 0:
		printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
	printflock(F"""{gpu} is broken""")
	raise