





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple  )   ->    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 >= 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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str  )   ->    Tuple:
  # word like '180' or '身高' or '神'
  for char in word:
    _lowercase						    =			ord(snake_case__  )
    if not _is_chinese_char(snake_case__  ):
      return 0
  return 1

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]  )   ->    str:
  _lowercase						    =			set()

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:set()  )   ->    Dict:
  if not chinese_word_set:
    return bert_tokens
  _lowercase						    =			max([len(snake_case__  ) for w in chinese_word_set]  )

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:LTP      ,				snake_case__	:BertTokenizer  )   ->    Optional[Any]:
  _lowercase						    =			[]

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

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

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

  assert len(snake_case__  ) == len(snake_case__  )

  return ref_ids

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int  )   ->    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:
    _lowercase						    =			f.readlines()
  _lowercase						    =			[line.strip() for line in data if len(snake_case__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  _lowercase						    =			LTP(args.ltp  )  # faster in GPU device
  _lowercase						    =			BertTokenizer.from_pretrained(args.bert  )

  _lowercase						    =			prepare_ref(snake_case__      ,				snake_case__      ,				snake_case__  )

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


if __name__ == "__main__":
      snake_case								=							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""",
      )

      snake_case								=							parser.parse_args()
      main(args)







"""simple docstring"""







import math
import random


def      UpperCamelCase       ( _A  ,				_A = False )     ->       float:
				if deriv:
								return value * (1 - value)
				return 1 / (1 + math.exp(-value ))


# Initial Value
_lowerCAmelCase            =	0.02


def      UpperCamelCase       ( _A  ,				_A )     ->       float:
				lowercase    :      Any       = float(2 * (random.randint(1  ,				100 )) - 1 )

				for _ in range(_A ):
								# Forward propagation
								lowercase    :      Dict       = sigmoid_function(INITIAL_VALUE * weight )
								# How much did we miss?
								lowercase    :      Any       = (expected / 100) - layer_a
								# Error delta
								lowercase    :      Dict       = layer_1_error * sigmoid_function(_A  ,				_A )
								# Update weight
								weight += INITIAL_VALUE * layer_1_delta

				return layer_a * 100


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	int(input('Expected value: '))
				_lowerCAmelCase            =	int(input('Number of propagations: '))
				print(forward_propagation(expected, number_propagations))
