
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 		lowercase_    (     A__	)							->							Dict:
				"""simple docstring"""

				if (
				    (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
				    or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
				    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
				    or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
				    or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
				    or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
				    or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
				    or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
				):  #
								return True

				return False



def 		lowercase_    (     A__	)							->							Union[str, Any]:
				"""simple docstring"""

				for char in word:
								snake_case				  =      ord(_A	)
								if not _is_chinese_char(_A	):
												return 0
				return 1



def 		lowercase_    (     A__	)							->							Tuple:
				"""simple docstring"""

				snake_case				  =      set()

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



def 		lowercase_    (     A__				,	A__	)							->							List[str]:
				"""simple docstring"""

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

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[Any]:
				"""simple docstring"""

				snake_case				  =      []

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

				snake_case				  =      []
				for i in range(0				,	len(_A	)				,	100	):
								snake_case				  =      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	)

				snake_case				  =      []
				for input_ids, chinese_word in zip(_A				,	_A	):
								snake_case				  =      []
								for id in input_ids:
												snake_case				  =      bert_tokenizer._convert_id_to_token(_A	)
												input_tokens.append(_A	)
								snake_case				  =      add_sub_symbol(_A				,	_A	)
								snake_case				  =      []
								# 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] == "##":
																snake_case				  =      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 		lowercase_    (     A__	)							->							int:
				"""simple docstring"""

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

				snake_case				  =      prepare_ref(_A				,	_A				,	_A	)

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


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

			_A						      =				parser.parse_args()
			main(args)



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

