




'''simple docstring'''


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ 		=      logging.get_logger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							str			):
  __a			=					RobertaPreLayerNormConfig.from_pretrained(
      a_				,					architectures=['RobertaPreLayerNormForMaskedLM']			)

  # convert state_dict
  __a			=					torch.load(hf_hub_download(repo_id=a_				,					filename='pytorch_model.bin'			)			)
  __a			=					{}
  for tensor_key, tensor_value in original_state_dict.items():
    # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
    if tensor_key.startswith('roberta.'			):
      __a			=					'roberta_prelayernorm.' + tensor_key[len('roberta.'			) :]

    # The original implementation contains weights which are not used, remove them from the state_dict
    if tensor_key.endswith('.self.LayerNorm.weight'			) or tensor_key.endswith('.self.LayerNorm.bias'			):
      continue

    __a			=					tensor_value

  __a			=					RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=a_				,					config=a_				,					state_dict=a_			)
  model.save_pretrained(a_			)

  # convert tokenizer
  __a			=					AutoTokenizer.from_pretrained(a_			)
  tokenizer.save_pretrained(a_			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint-repo",
      default=None,
      type=str,
      required=True,
      help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  UpperCAmelCase_ 		=      parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # 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 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # word like '180' or '身高' or '神'
  for char in word:
    __a			=					ord(a_			)
    if not _is_chinese_char(a_			):
      return 0
  return 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

  for token in tokens:
    __a			=					len(a_			) > 1 and is_chinese(a_			)
    if chinese_word:
      word_set.add(a_			)
  __a			=					list(a_			)
  return word_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

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

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

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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(a_			) > 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(a_				,					a_				,					a_			)

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


if __name__ == "__main__":
  UpperCAmelCase_ 		=      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")

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



