



'''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      a      (			*lowerCamelCase__   ):
			'''simple docstring'''





			with open(lowerCamelCase__						,				"""r"""   ) as fh:
						fcntl.flock(lowerCamelCase__						,				fcntl.LOCK_EX   )
						try:
									print(*lowerCamelCase__   )
						finally:
									fcntl.flock(lowerCamelCase__						,				fcntl.LOCK_UN   )


lowerCamelCase							:Tuple       = int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
lowerCamelCase							:int       = torch.device('''cuda''', local_rank)
lowerCamelCase							:Dict       = socket.gethostname()

lowerCamelCase							:Any       = 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							:Optional[Any]       = dist.get_rank()
						lowerCamelCase							:Optional[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




'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
						from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase							:Tuple       = logging.get_logger(__name__)

lowerCamelCase							:Optional[int]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:int       = {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:Tuple       = {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

lowerCamelCase							:str       = '''▁'''







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		str		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ['input_ids', 'attention_mask']


	def __init__(self	,				lowercase	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase=100	,				lowercase=None	,				lowercase = None	,				lowercase=True	,				**lowercase	,				):
				# Add extra_ids to the special token list
				if extra_ids > 0 and additional_special_tokens is None:
							A_     :						Any     =					[F'<extra_id_{i}>' for i in range(lowercase							)]
				elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							A_     :						Tuple     =					len(set(filter(lambda lowercase							: bool("""extra_id""" in str(lowercase							)							)	,				lowercase							)							)							)
							if extra_tokens != extra_ids:
										raise ValueError(
										    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
										    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
										    """ tokens"""							)
				if legacy:
							logger.warning_once(
							    F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
							    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""							)

				A_     :						str     =					legacy
				A_     :						Dict     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=lowercase	,				unk_token=lowercase	,				pad_token=lowercase	,				extra_ids=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				legacy=lowercase	,				**lowercase	,				)

				A_     :						List[str]     =					vocab_file
				A_     :						Tuple     =					extra_ids

				A_     :						Tuple     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)


	@staticmethod
	def    _a     (lowercase	,				lowercase	,				lowercase							):
				if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
							A_     :						Union[str, Any]     =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
							if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
							elif init_max_model_length is None:
										warnings.warn(
										    """This tokenizer was incorrectly instantiated with a model max length of"""
										    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
										    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
										    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
										    F' {pretrained_model_name_or_path} automatically truncating your input to'
										    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
										    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
										    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
										    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""	,				lowercase	,				)

				return max_model_length


	@property
	def    _a     (self							):
				return self.sp_model.get_piece_size() + self._extra_ids


	def    _a     (self							):
				A_     :						Union[str, Any]     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				# normal case: some special tokens
				if token_ids_a is None:
							return ([0] * len(lowercase							)) + [1]
				return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1]


	def    _a     (self							):
				return list(
				    set(filter(lambda lowercase							: bool(re.search(R"""<extra_id_\d+>"""	,				lowercase							)							) is not None	,				self.additional_special_tokens							)							)							)


	def    _a     (self							):
				return [self._convert_token_to_id(lowercase							) for token in self.get_sentinel_tokens()]


	def    _a     (self	,				lowercase							):
				if len(lowercase							) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    """ eos tokens being added."""							)
							return token_ids
				else:
							return token_ids + [self.eos_token_id]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Dict     =					[self.eos_token_id]

				if token_ids_a is None:
							return len(token_ids_a + eos							) * [0]
				return len(token_ids_a + eos + token_ids_a + eos							) * [0]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					self._add_eos_if_not_present(lowercase							)
				if token_ids_a is None:
							return token_ids_a
				else:
							A_     :						List[Any]     =					self._add_eos_if_not_present(lowercase							)
							return token_ids_a + token_ids_a


	def __getstate__(self							):
				A_     :						int     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						Dict     =					{}

				A_     :						Any     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
				# the beginning of the text
				if not self.legacy:
							A_     :						Tuple     =					SPIECE_UNDERLINE + text.replace(lowercase	,				""" """							)
				return super().tokenize(lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				if not self.legacy:
							A_     :						Dict     =					text.startswith(lowercase							)
							if is_first:
										A_     :						str     =					text[1:]

				A_     :						Optional[int]     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)

				if not self.legacy and not is_first and not text.startswith(""" """							) and tokens[0].startswith(lowercase							):
							A_     :						Optional[int]     =					([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
				return tokens


	def    _a     (self	,				lowercase							):
				if token.startswith("""<extra_id_"""							):
							A_     :						Union[str, Any]     =					re.match(R"""<extra_id_(\d+)>"""	,				lowercase							)
							A_     :						str     =					int(match.group(1							)							)
							return self.vocab_size - num - 1
				return self.sp_model.piece_to_id(lowercase							)


	def    _a     (self	,				lowercase							):
				if index < self.sp_model.get_piece_size():
							A_     :						List[Any]     =					self.sp_model.IdToPiece(lowercase							)
				else:
							A_     :						Dict     =					F'<extra_id_{self.vocab_size - 1 - index}>'
				return token


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					[]
				A_     :						int     =					""""""
				A_     :						Any     =					False
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										if not prev_is_special:
													out_string += " "
										out_string += self.sp_model.decode(lowercase							) + token
										A_     :						Dict     =					True
										A_     :						Union[str, Any]     =					[]
							else:
										current_sub_tokens.append(lowercase							)
										A_     :						Optional[Any]     =					False
				out_string += self.sp_model.decode(lowercase							)
				return out_string.strip()


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Optional[Any]     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file	,				lowercase							)
				elif not os.path.isfile(self.vocab_file							):
							with open(lowercase	,				"""wb"""							) as fi:
										A_     :						Union[str, Any]     =					self.sp_model.serialized_model_proto()
										fi.write(lowercase							)

				return (out_vocab_file,)