"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      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        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      for char in word:
            UpperCAmelCase__								=  ord(_UpperCamelCase							)
            if not _is_chinese_char(_UpperCamelCase							):
                  return 0
      return 1




def        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  set()

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




def        lowerCAmelCase_			(					__A,  __A							)					->    Tuple:







      '''simple docstring'''


      if not chinese_word_set:
            return bert_tokens
      UpperCAmelCase__								=  max([len(_UpperCamelCase							) for w in chinese_word_set]							)

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




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  []

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

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

      UpperCAmelCase__								=  []
      for input_ids, chinese_word in zip(_UpperCamelCase,  _UpperCamelCase							):
            UpperCAmelCase__								=  []
            for id in input_ids:
                  UpperCAmelCase__								=  bert_tokenizer._convert_id_to_token(_UpperCamelCase							)
                  input_tokens.append(_UpperCamelCase							)
            UpperCAmelCase__								=  add_sub_symbol(_UpperCamelCase,  _UpperCamelCase							)
            UpperCAmelCase__								=  []
            # 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] == "##":
                        UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


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

      UpperCAmelCase__								=  prepare_ref(_UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase							)

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


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)




from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[int]              =      'xlm-prophetnet'
   __UpperCAmelCase			:			int              =      ['past_key_values']
   __UpperCAmelCase			:			Optional[int]              =      {
       'num_attention_heads': 'num_encoder_attention_heads',
   }


   def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[Union[str, Callable]] = "gelu"	,  __UpperCAmelCase					:						Optional[int] = 3_0_5_2_2	,  __UpperCAmelCase					:						Optional[int] = 1_0_2_4	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[int] = 5_1_2	,  __UpperCAmelCase					:						Optional[float] = 0.02	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 2	,  __UpperCAmelCase					:						Optional[int] = 3_2	,  __UpperCAmelCase					:						Optional[int] = 1_2_8	,  __UpperCAmelCase					:						Optional[bool] = False	,  __UpperCAmelCase					:						Optional[float] = 0.0	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						Optional[int] = 2	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  num_encoder_layers
         UpperCAmelCase__								=  num_encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  num_decoder_layers
         UpperCAmelCase__								=  num_decoder_attention_heads
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  init_std  # Normal(0, this parameter)
         UpperCAmelCase__								=  activation_function

         # parameters for xlmprophetnet
         UpperCAmelCase__								=  ngram
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  relative_max_distance
         UpperCAmelCase__								=  disable_ngram_loss
         UpperCAmelCase__								=  eps

         # 3 Types of Dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  dropout

         UpperCAmelCase__								=  use_cache

         super().__init__(
             pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  is_encoder_decoder=__UpperCAmelCase	,  add_cross_attention=__UpperCAmelCase	,  decoder_start_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         raise NotImplementedError(
             "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
             " `num_decoder_layers`."		)



