


"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def _lowerCamelCase			(	_UpperCamelCase     :   Optional[Any]   ):

			'''simple docstring'''




			if (
			    (cp >= 0x4E00 and cp <= 0x9FFF)
			    or (cp >= 0x3400 and cp <= 0x4DBF)  #
			    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
			    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
			    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
			    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
			    or (cp >= 0xF900 and cp <= 0xFAFF)
			    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
			):  #
						return True

			return False


def _lowerCamelCase			(	_UpperCamelCase     :   Optional[Any]   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase     :   Any   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    set()

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


def _lowerCamelCase			(	_UpperCamelCase     :   List[Any]					,				_UpperCamelCase     :   Dict   ):

			'''simple docstring'''




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

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


def _lowerCamelCase			(	_UpperCamelCase     :   List[Any]					,				_UpperCamelCase     :   Union[str, Any]					,				_UpperCamelCase     :   Tuple   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

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

			__lowerCAmelCase       					=    []
			for i in range(0					,				len(_UpperCamelCase   )					,				100   ):
						__lowerCAmelCase       					=    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   )

			__lowerCAmelCase       					=    []
			for input_ids, chinese_word in zip(_UpperCamelCase					,				_UpperCamelCase   ):
						__lowerCAmelCase       					=    []
						for id in input_ids:
									__lowerCAmelCase       					=    bert_tokenizer._convert_id_to_token(_UpperCamelCase   )
									input_tokens.append(_UpperCamelCase   )
						__lowerCAmelCase       					=    add_sub_symbol(_UpperCamelCase					,				_UpperCamelCase   )
						__lowerCAmelCase       					=    []
						# 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] == "##":
												__lowerCAmelCase       					=    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			(	_UpperCamelCase     :   List[str]   ):

			'''simple docstring'''




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

			__lowerCAmelCase       					=    prepare_ref(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )

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


if __name__ == "__main__":
	A  :     List[Any]    					=    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  :     List[Any]    					=    parser.parse_args()
	main(args)








"""simple docstring"""


from itertools import product


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    sides_number
			__lowerCAmelCase       					=    max_face_number * dice_number
			__lowerCAmelCase       					=    [0] * (max_total + 1)

			__lowerCAmelCase       					=    1
			__lowerCAmelCase       					=    range(_UpperCamelCase					,				max_face_number + 1   )
			for dice_numbers in product(_UpperCamelCase					,				repeat=_UpperCamelCase   ):
						__lowerCAmelCase       					=    sum(_UpperCamelCase   )
						totals_frequencies[total] += 1

			return totals_frequencies


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=4					,				dice_number=9   )
			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=6					,				dice_number=6   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    9
			__lowerCAmelCase       					=    4 * 9
			__lowerCAmelCase       					=    6
			for peter_total in range(_UpperCamelCase					,				max_peter_total + 1   ):
						peter_wins_count += peter_totals_frequencies[peter_total] * sum(
						    colin_totals_frequencies[min_colin_total:peter_total]   )

			__lowerCAmelCase       					=    (4**9) * (6**6)
			__lowerCAmelCase       					=    peter_wins_count / total_games_number

			__lowerCAmelCase       					=    round(_UpperCamelCase					,				ndigits=7   )

			return rounded_peter_win_probability


if __name__ == "__main__":
	print(f'''{solution() = }''')




