'''simple docstring'''







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCamelCase		       =    logging.get_logger(__name__)


@dataclass
class 					A__      :
    lowercase						=		field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()			)}			)
    lowercase						=		field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
    lowercase						=		field(
        default=128						,						metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }						,						)
    lowercase						=		field(
        default=_A						,						metadata={"help": "Overwrite the cached training and evaluation sets"}			)



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           A_						=      self.task_name.lower()


class 					A__      (      _A			):
    lowercase						=		"train"
    lowercase						=		"dev"
    lowercase						=		"test"


class 					A__      (      _A			):
    lowercase						=		42
    lowercase						=		42
    lowercase						=		42



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = Split.train		,				UpperCamelCase__ = None		,				)						->      Any:





           '''simple docstring'''
           warnings.warn(
               """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
               """library. You can have a look at this example script for pointers: """
               """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""		,				UpperCamelCase__		,				)
           A_						=      args
           A_						=      glue_processors[args.task_name]()
           A_						=      glue_output_modes[args.task_name]
           if isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                  try:
                         A_						=      Split[mode]
                  except KeyError:
                         raise KeyError("""mode is not a valid split name"""       )
                         # Load data features from cache or dataset file
           A_						=      os.path.join(
               cache_dir if cache_dir is not None else args.data_dir		,				f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''		,				)
           A_						=      self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  A_					,			A_						=      label_list[2], label_list[1]
           A_						=      label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           A_						=      cached_features_file + """.lock"""
           with FileLock(UpperCamelCase__       ):
                  if os.path.exists(UpperCamelCase__       ) and not args.overwrite_cache:
                         A_						=      time.time()
                         A_						=      torch.load(UpperCamelCase__       )
                         logger.info(
                             f'''Loading features from cached file {cached_features_file} [took %.3f s]'''		,				time.time() - start       )
                  else:
                         logger.info(f'''Creating features from dataset file at {args.data_dir}'''       )

                         if mode == Split.dev:
                                A_						=      self.processor.get_dev_examples(args.data_dir       )
                         elif mode == Split.test:
                                A_						=      self.processor.get_test_examples(args.data_dir       )
                         else:
                                A_						=      self.processor.get_train_examples(args.data_dir       )
                         if limit_length is not None:
                                A_						=      examples[:limit_length]
                         A_						=      glue_convert_examples_to_features(
                             UpperCamelCase__		,				UpperCamelCase__		,				max_length=args.max_seq_length		,				label_list=UpperCamelCase__		,				output_mode=self.output_mode		,				)
                         A_						=      time.time()
                         torch.save(self.features		,				UpperCamelCase__       )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )



    def __len__(					self       )						->      str:





           '''simple docstring'''
           return len(self.features       )



    def __getitem__(					self		,				UpperCamelCase__       )						->      InputFeatures:





           '''simple docstring'''
           return self.features[i]







    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           return self.label_list



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # 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 >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
              return True

       return False
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              A_						=      ord(UpperCAmelCase__				)
              if not _is_chinese_char(UpperCAmelCase__				):
                     return 0
       return 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      set()

       for token in tokens:
              A_						=      len(UpperCAmelCase__				) > 1 and is_chinese(UpperCAmelCase__				)
              if chinese_word:
                     word_set.add(UpperCAmelCase__				)
       A_						=      list(UpperCAmelCase__				)
       return word_list
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if not chinese_word_set:
              return bert_tokens
       A_						=      max([len(UpperCAmelCase__				) for w in chinese_word_set]				)

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       1,       -1				):
                            A_						=      """""".join(bert_word[start : start + i]				)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1,       start + i				):
                                          A_						=      """##""" + bert_word[j]
                                   A_						=      start + i
                                   A_						=      False
                                   break
              if single_word:
                     start += 1
       return bert_word
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       tasks=["""cws"""]				).cws
              A_						=      [get_chinese_word(UpperCAmelCase__				) for r in res]
              ltp_res.extend(UpperCAmelCase__				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              bert_res.extend(res["""input_ids"""]				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

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

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # 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:
              A_						=      f.readlines()
       A_						=      [line.strip() for line in data if len(UpperCAmelCase__				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       A_						=      LTP(args.ltp				)  # faster in GPU device
       A_						=      BertTokenizer.from_pretrained(args.bert				)

       A_						=      prepare_ref(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

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


if __name__ == "__main__":
      __lowerCamelCase		       =    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''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
