import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		if (
		    (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
		    or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
		    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
		    or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
		    or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
		    or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
		    or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
		    or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
		):  #
				return True

		return False




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		for char in word:
				SCREAMING_SNAKE_CASE_  :		Any				      =    ord(lowerCAmelCase	)
				if not _is_chinese_char(lowerCAmelCase	):
						return 0
		return 1




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    set()

		for token in tokens:
				SCREAMING_SNAKE_CASE_  :		int				      =    len(lowerCAmelCase	) > 1 and is_chinese(lowerCAmelCase	)
				if chinese_word:
						word_set.add(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    list(lowerCAmelCase	)
		return word_list




def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   set()	):
		"""simple docstring"""







		if not chinese_word_set:
				return bert_tokens
		SCREAMING_SNAKE_CASE_  :		str				      =    max([len(lowerCAmelCase	) for w in chinese_word_set]	)

		SCREAMING_SNAKE_CASE_  :		int				      =    bert_tokens
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    0, len(lowerCAmelCase	)
		while start < end:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    True
				if is_chinese(bert_word[start]	):
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    min(end - start   , lowerCAmelCase	)
						for i in range(lowerCAmelCase   , 1   , -1	):
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    "".join(bert_word[start : start + i]	)
								if whole_word in chinese_word_set:
										for j in range(start + 1   , start + i	):
												SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "##" + bert_word[j]
										SCREAMING_SNAKE_CASE_  :		int				      =    start + i
										SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    False
										break
				if single_word:
						start += 1
		return bert_word




def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   LTP   , lowerCAmelCase							:   BertTokenizer	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    []

		for i in range(0   , len(lowerCAmelCase	)   , 1_0_0	):
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    ltp_tokenizer.seg(lines[i : i + 1_0_0]	)[0]
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [get_chinese_word(lowerCAmelCase	) for r in res]
				ltp_res.extend(lowerCAmelCase	)
		assert len(lowerCAmelCase	) == len(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    []
		for i in range(0   , len(lowerCAmelCase	)   , 1_0_0	):
				SCREAMING_SNAKE_CASE_  :		str				      =    bert_tokenizer(lines[i : i + 1_0_0]   , add_special_tokens=lowerCAmelCase   , truncation=lowerCAmelCase   , max_length=5_1_2	)
				bert_res.extend(res["input_ids"]	)
		assert len(lowerCAmelCase	) == len(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		int				      =    []
		for input_ids, chinese_word in zip(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		str				      =    []
				for id in input_ids:
						SCREAMING_SNAKE_CASE_  :		Tuple				      =    bert_tokenizer._convert_id_to_token(lowerCAmelCase	)
						input_tokens.append(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    add_sub_symbol(lowerCAmelCase   , lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    []
				# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
				for i, token in enumerate(lowerCAmelCase	):
						if token[:2] == "##":
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    token[2:]
								# save chinese tokens' pos
								if len(lowerCAmelCase	) == 1 and _is_chinese_char(ord(lowerCAmelCase	)	):
										ref_id.append(lowerCAmelCase	)
				ref_ids.append(lowerCAmelCase	)

		assert len(lowerCAmelCase	) == len(lowerCAmelCase	)

		return ref_ids




def       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		with open(args.file_name   , "r"   , encoding="utf-8"	) as f:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    f.readlines()
		SCREAMING_SNAKE_CASE_  :		int				      =    [line.strip() for line in data if len(lowerCAmelCase	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    LTP(args.ltp	)  # faster in GPU device
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    BertTokenizer.from_pretrained(args.bert	)

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    prepare_ref(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)

		with open(args.save_path   , "w"   , encoding="utf-8"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [json.dumps(lowerCAmelCase	) + "\n" for ref in ref_ids]
				f.writelines(lowerCAmelCase	)


if __name__ == "__main__":
		__lowerCamelCase					:	Any       =  argparse.ArgumentParser(description='''prepare_chinese_ref''')
		parser.add_argument(
		    '''--file_name''',
		    type=str,
		    default='''./resources/chinese-demo.txt''',
		    help='''file need process, same as training data in lm''',
		)
		parser.add_argument(
		    '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
		)
		parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
		parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

		__lowerCamelCase					:	int       =  parser.parse_args()
		main(args)




'''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   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


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

__lowercase	:						List[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
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, 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



