'''simple docstring'''






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]				):
     # 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 >= 0X4_E00 and cp <= 0X9_FFF)
         or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
         or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
         or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
         or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
         or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
         or (cp >= 0XF_900 and cp <= 0XF_AFF)
         or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
     ):  #
          return True

     return False





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]				):
     # word like '180' or '身高' or '神'
     for char in word:
          _A    		=  ord(__lowercase				)
          if not _is_chinese_char(__lowercase				):
               return 0
     return 1





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     _A    		=  set()

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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]   ,		__snake_case							:			Any				):
     if not chinese_word_set:
          return bert_tokens
     _A    		=  max([len(__lowercase				) for w in chinese_word_set]				)

     _A    		=  bert_tokens
     _A    		=  0, len(__lowercase				)
     while start < end:
          _A    		=  True
          if is_chinese(bert_word[start]				):
               _A    		=  min(end - start   ,		__lowercase				)
               for i in range(__lowercase   ,		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		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			List[Any]				):
     _A    		=  []

     for i in range(0   ,		len(__lowercase				)   ,		1_0_0				):
          _A    		=  ltp_tokenizer.seg(lines[i : i + 1_0_0]				)[0]
          _A    		=  [get_chinese_word(__lowercase				) for r in res]
          ltp_res.extend(__lowercase				)
     assert len(__lowercase				) == len(__lowercase				)

     _A    		=  []
     for i in range(0   ,		len(__lowercase				)   ,		1_0_0				):
          _A    		=  bert_tokenizer(lines[i : i + 1_0_0]   ,		add_special_tokens=__lowercase   ,		truncation=__lowercase   ,		max_length=5_1_2				)
          bert_res.extend(res['input_ids']				)
     assert len(__lowercase				) == len(__lowercase				)

     _A    		=  []
     for input_ids, chinese_word in zip(__lowercase   ,		__lowercase				):
          _A    		=  []
          for id in input_ids:
               _A    		=  bert_tokenizer._convert_id_to_token(__lowercase				)
               input_tokens.append(__lowercase				)
          _A    		=  add_sub_symbol(__lowercase   ,		__lowercase				)
          _A    		=  []
          # 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] == "##":
                    _A    		=  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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			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(__lowercase				) > 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(__lowercase   ,		__lowercase   ,		__lowercase				)

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


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[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''')

   _UpperCAmelCase    :     Any         =       parser.parse_args()
   main(args)





"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
