






"""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      UpperCamelCase       ( *_A )     ->       Dict:
				with open(_A  ,				"""r""" ) as fh:
								fcntl.flock(_A  ,				fcntl.LOCK_EX )
								try:
												print(*_A )
								finally:
												fcntl.flock(_A  ,				fcntl.LOCK_UN )


_lowerCAmelCase            =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
_lowerCAmelCase            =	torch.device('cuda', local_rank)
_lowerCAmelCase            =	socket.gethostname()

_lowerCAmelCase            =	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
				_lowerCAmelCase            =	dist.get_rank()
				_lowerCAmelCase            =	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








"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

										super().__init__(
										    __magic_name__			, tokenizer_file=__magic_name__			, do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, **__magic_name__			, )

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






						def      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
										return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
														copyfile(self.vocab_file			, __magic_name__				)

										return (out_vocab_file,)
