"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase_									=logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCamelCase_									={
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase_									={
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase_									=sorted(arg_to_scheduler.keys())
UpperCamelCase_									="""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class 			_a						(				pl.LightningModule    ):






				def __init__(			self		:   int,			lowerCAmelCase__		:   argparse.Namespace,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]="base",			lowerCAmelCase__		:   List[str]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   int=None,			**lowerCAmelCase__		:   Dict,			)     ->				int:




										'''simple docstring'''



										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, Any]         =   0
										_UpperCamelCase      : Union[str, Any]         =   Path(self.hparams.output_dir							)
										_UpperCamelCase      : str         =   self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																_UpperCamelCase      : Tuple         =   AutoConfig.from_pretrained(
																    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,			**({'''num_labels''': num_labels} if num_labels is not None else {}),			cache_dir=lowerCAmelCase__,			**lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : PretrainedConfig         =   config

										_UpperCamelCase      : str         =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
										for p in extra_model_params:
																if getattr(self.hparams,			lowerCAmelCase__,			lowerCAmelCase__							):
																						assert hasattr(self.config,			lowerCAmelCase__							), f"""model config doesn't have a `{p}` attribute"""
																						setattr(self.config,			lowerCAmelCase__,			getattr(self.hparams,			lowerCAmelCase__							)							)

										if tokenizer is None:
																_UpperCamelCase      : Union[str, Any]         =   AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,			cache_dir=lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : PreTrainedTokenizer         =   tokenizer
										_UpperCamelCase      : Dict         =   MODEL_MODES[mode]
										if model is None:
																_UpperCamelCase      : Union[str, Any]         =   self.model_type.from_pretrained(
																    self.hparams.model_name_or_path,			from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path							),			config=self.config,			cache_dir=lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : Any         =   model






				def     snake_case     (			self		:   Any,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.model_type.from_pretrained(*lowerCAmelCase__,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   arg_to_scheduler[self.hparams.lr_scheduler]
										_UpperCamelCase      : Union[str, Any]         =   get_schedule_func(
										    self.opt,			num_warmup_steps=self.hparams.warmup_steps,			num_training_steps=self.total_steps()							)
										_UpperCamelCase      : Dict         =   {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler






				def     snake_case     (			self		:   Optional[Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.model
										_UpperCamelCase      : str         =   ['''bias''', '''LayerNorm.weight''']
										_UpperCamelCase      : str         =   [
										    {
										        '''params''': [
										            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)
										        ],  # check this named paramters
										        '''weight_decay''': self.hparams.weight_decay,
										    },
										    {
										        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
										        '''weight_decay''': 0.0,
										    },
										]
										if self.hparams.adafactor:
																_UpperCamelCase      : Optional[int]         =   Adafactor(
																    lowerCAmelCase__,			lr=self.hparams.learning_rate,			scale_parameter=lowerCAmelCase__,			relative_step=lowerCAmelCase__							)

										else:
																_UpperCamelCase      : Tuple         =   AdamW(
																    lowerCAmelCase__,			lr=self.hparams.learning_rate,			eps=self.hparams.adam_epsilon							)
										_UpperCamelCase      : Any         =   optimizer

										_UpperCamelCase      : Dict         =   self.get_lr_scheduler()

										return [optimizer], [scheduler]






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int							)     ->				List[Any]:




										'''simple docstring'''



										return self.validation_step(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Optional[int]							)     ->				str:




										'''simple docstring'''



										return self.validation_end(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   max(1,			self.hparams.gpus							)  # TODO: consider num_tpu_cores
										_UpperCamelCase      : List[str]         =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Union[str, Any]							)     ->				List[Any]:




										'''simple docstring'''



										if stage == "test":
																_UpperCamelCase      : Union[str, Any]         =   len(self.test_dataloader().dataset							)
										else:
																_UpperCamelCase      : Dict         =   self.get_dataloader('''train''',			self.hparams.train_batch_size,			shuffle=lowerCAmelCase__							)
																_UpperCamelCase      : Optional[Any]         =   len(self.train_dataloader().dataset							)






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   bool = False							)     ->				int:




										'''simple docstring'''



										raise NotImplementedError('''You must implement this for your task'''							)






				def     snake_case     (			self		:   Optional[int]							)     ->				str:




										'''simple docstring'''



										return self.train_loader






				def     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										return self.get_dataloader('''dev''',			self.hparams.eval_batch_size,			shuffle=lowerCAmelCase__							)






				def     snake_case     (			self		:   List[str]							)     ->				List[Any]:




										'''simple docstring'''



										return self.get_dataloader('''test''',			self.hparams.eval_batch_size,			shuffle=lowerCAmelCase__							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										return os.path.join(
										    self.hparams.data_dir,			'''cached_{}_{}_{}'''.format(
										        lowerCAmelCase__,			list(filter(lowerCAmelCase__,			self.hparams.model_name_or_path.split('''/'''							)							)							).pop(),			str(self.hparams.max_seq_length							),			),			)






				@pl.utilities.rank_zero_only
				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Dict[str, Any]							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.output_dir.joinpath('''best_tfmr'''							)
										_UpperCamelCase      : Any         =   self.step_count
										self.model.save_pretrained(lowerCAmelCase__							)
										self.tokenizer.save_pretrained(lowerCAmelCase__							)






				@staticmethod
				def     snake_case     (			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   List[Any]							)     ->				List[str]:




										'''simple docstring'''



										parser.add_argument(
										    '''--model_name_or_path''',			default=lowerCAmelCase__,			type=lowerCAmelCase__,			required=lowerCAmelCase__,			help='''Path to pretrained model or model identifier from huggingface.co/models''',			)
										parser.add_argument(
										    '''--config_name''',			default='''''',			type=lowerCAmelCase__,			help='''Pretrained config name or path if not the same as model_name'''							)
										parser.add_argument(
										    '''--tokenizer_name''',			default=lowerCAmelCase__,			type=lowerCAmelCase__,			help='''Pretrained tokenizer name or path if not the same as model_name''',			)
										parser.add_argument(
										    '''--cache_dir''',			default=str(Path(lowerCAmelCase__							).parent / '''test_run''' / '''cache'''							),			type=lowerCAmelCase__,			help='''Where do you want to store the pre-trained models downloaded from huggingface.co''',			)
										parser.add_argument(
										    '''--encoder_layerdrop''',			type=lowerCAmelCase__,			help='''Encoder layer dropout probability (Optional). Goes into model.config''',			)
										parser.add_argument(
										    '''--decoder_layerdrop''',			type=lowerCAmelCase__,			help='''Decoder layer dropout probability (Optional). Goes into model.config''',			)
										parser.add_argument(
										    '''--dropout''',			type=lowerCAmelCase__,			help='''Dropout probability (Optional). Goes into model.config''',			)
										parser.add_argument(
										    '''--attention_dropout''',			type=lowerCAmelCase__,			help='''Attention dropout probability (Optional). Goes into model.config''',			)
										parser.add_argument('''--learning_rate''',			default=5e-5,			type=lowerCAmelCase__,			help='''The initial learning rate for Adam.'''							)
										parser.add_argument(
										    '''--lr_scheduler''',			default='''linear''',			choices=lowerCAmelCase__,			metavar=lowerCAmelCase__,			type=lowerCAmelCase__,			help='''Learning rate scheduler''',			)
										parser.add_argument('''--weight_decay''',			default=0.0,			type=lowerCAmelCase__,			help='''Weight decay if we apply some.'''							)
										parser.add_argument('''--adam_epsilon''',			default=1e-8,			type=lowerCAmelCase__,			help='''Epsilon for Adam optimizer.'''							)
										parser.add_argument('''--warmup_steps''',			default=0,			type=lowerCAmelCase__,			help='''Linear warmup over warmup_steps.'''							)
										parser.add_argument('''--num_workers''',			default=4,			type=lowerCAmelCase__,			help='''kwarg passed to DataLoader'''							)
										parser.add_argument('''--num_train_epochs''',			dest='''max_epochs''',			default=3,			type=lowerCAmelCase__							)
										parser.add_argument('''--train_batch_size''',			default=3_2,			type=lowerCAmelCase__							)
										parser.add_argument('''--eval_batch_size''',			default=3_2,			type=lowerCAmelCase__							)
										parser.add_argument('''--adafactor''',			action='''store_true'''							)




class 			_a						(				pl.Callback    ):






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Any							)     ->				Optional[Any]:




										'''simple docstring'''



										if (
										    trainer.is_global_zero and trainer.global_rank == 0
										):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class 			_a						(				pl.Callback    ):






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[int]							)     ->				List[Any]:




										'''simple docstring'''



										for name, param in pl_module.model.rag.named_parameters():
																if param.grad is None:
																						print(lowerCAmelCase__							)




class 			_a						(				pl.Callback    ):






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   trainer.lr_schedulers[0]['''scheduler''']
										_UpperCamelCase      : Tuple         =   {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
										pl_module.logger.log_metrics(lowerCAmelCase__							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   pl.Trainer,			lowerCAmelCase__		:   pl.LightningModule							)     ->				Optional[Any]:




										'''simple docstring'''



										rank_zero_info('''***** Validation results *****'''							)
										_UpperCamelCase      : Dict         =   trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase__							):
																if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__,			str(metrics[key]							)							)							)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   pl.Trainer,			lowerCAmelCase__		:   pl.LightningModule							)     ->				int:




										'''simple docstring'''



										rank_zero_info('''***** Test results *****'''							)
										_UpperCamelCase      : Optional[Any]         =   trainer.callback_metrics
										# Log and save results to file
										_UpperCamelCase      : Optional[int]         =   os.path.join(pl_module.hparams.output_dir,			'''test_results.txt'''							)
										with open(lowerCAmelCase__,			'''w'''							) as writer:
																for key in sorted(lowerCAmelCase__							):
																						if key not in ["log", "progress_bar"]:
																												rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__,			str(metrics[key]							)							)							)
																												writer.write('''{} = {}\n'''.format(lowerCAmelCase__,			str(metrics[key]							)							)							)





def 	a_    (							_lowercase	,						_lowercase       ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir'''	,						default=str(Path(_lowercase       ).parent / '''test_run''' / '''model_checkpoints'''       )	,						type=_lowercase	,						help='''The output directory where the model predictions and checkpoints will be written.'''	,						)
						parser.add_argument(
						    '''--fp16'''	,						action='''store_true'''	,						help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''	,						)

						parser.add_argument(
						    '''--fp16_opt_level'''	,						type=_lowercase	,						default='''O2'''	,						help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    )	,						)
						parser.add_argument('''--n_tpu_cores'''	,						dest='''tpu_cores'''	,						type=_lowercase       )
						parser.add_argument('''--max_grad_norm'''	,						dest='''gradient_clip_val'''	,						default=1.0	,						type=_lowercase	,						help='''Max gradient norm'''       )
						parser.add_argument('''--do_train'''	,						action='''store_true'''	,						help='''Whether to run training.'''       )
						parser.add_argument('''--do_predict'''	,						action='''store_true'''	,						help='''Whether to run predictions on the test set.'''       )
						parser.add_argument(
						    '''--gradient_accumulation_steps'''	,						dest='''accumulate_grad_batches'''	,						type=_lowercase	,						default=1	,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,						)
						parser.add_argument('''--seed'''	,						type=_lowercase	,						default=42	,						help='''random seed for initialization'''       )
						parser.add_argument(
						    '''--data_dir'''	,						default=str(Path(_lowercase       ).parent / '''test_run''' / '''dummy-train-data'''       )	,						type=_lowercase	,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,						)






def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=True	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=None	,						**_lowercase	,						):
						pl.seed_everything(args.seed       )

						# init model
						_UpperCamelCase      : List[Any]         =   Path(model.hparams.output_dir       )
						odir.mkdir(exist_ok=_lowercase       )

						# add custom checkpoints
						if checkpoint_callback is None:
												_UpperCamelCase      : Union[str, Any]         =   pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir	,						prefix='''checkpoint'''	,						monitor='''val_loss'''	,						mode='''min'''	,						save_top_k=1       )
						if early_stopping_callback:
												extra_callbacks.append(_lowercase       )
						if logging_callback is None:
												_UpperCamelCase      : Optional[Any]         =   LoggingCallback()

						_UpperCamelCase      : int         =   {}

						if args.fpaa:
												_UpperCamelCase      : str         =   16

						if args.gpus > 1:
												_UpperCamelCase      : Dict         =   '''auto'''
												_UpperCamelCase      : List[Any]         =   '''ddp'''

						_UpperCamelCase      : List[Any]         =   args.accumulate_grad_batches
						_UpperCamelCase      : str         =   None
						_UpperCamelCase      : Union[str, Any]         =   '''auto'''

						_UpperCamelCase      : Optional[Any]         =   pl.Trainer.from_argparse_args(
						    _lowercase	,						weights_summary=_lowercase	,						callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]	,						logger=_lowercase	,						val_check_interval=1	,						num_sanity_val_steps=2	,						**_lowercase	,						)

						if args.do_train:
												trainer.fit(_lowercase       )

						else:
												print('''RAG modeling tests with new set functions successfuly executed!'''       )
						return trainer










"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0X4E00 and cp <= 0X9FFF)
						    or (cp >= 0X3400 and cp <= 0X4DBF)  #
						    or (cp >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False





def 	a_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [get_chinese_word(_lowercase       ) for r in res]
												ltp_res.extend(_lowercase       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Dict         =   []
						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   bert_tokenizer(lines[i : i + 100]	,						add_special_tokens=_lowercase	,						truncation=_lowercase	,						max_length=512       )
												bert_res.extend(res['''input_ids''']       )
						assert len(_lowercase       ) == len(_lowercase       )

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

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
						# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
						with open(args.file_name	,						'''r'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

						with open(args.save_path	,						'''w'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : List[Any]         =   [json.dumps(_lowercase       ) + '''\n''' for ref in ref_ids]
												f.writelines(_lowercase       )


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

						UpperCamelCase_									=parser.parse_args()
						main(args)



