




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Dict:
						# 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 >= 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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCAmelCase      =			ord(snake_case       )
												if not _is_chinese_char(snake_case       ):
																		return 0
						return 1




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			set()

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						if not chinese_word_set:
												return bert_tokens
						_UpperCAmelCase      =			max([len(snake_case       ) for w in chinese_word_set]       )

						_UpperCAmelCase      =			bert_tokens
						_UpperCAmelCase    ,					_UpperCAmelCase      =			0, len(snake_case       )
						while start < end:
												_UpperCAmelCase      =			True
												if is_chinese(bert_word[start]       ):
																		_UpperCAmelCase      =			min(end - start			,     snake_case       )
																		for i in range(snake_case			,     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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			[]

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

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

						_UpperCAmelCase      =			[]
						for input_ids, chinese_word in zip(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			[]
												for id in input_ids:
																		_UpperCAmelCase      =			bert_tokenizer._convert_id_to_token(snake_case       )
																		input_tokens.append(snake_case       )
												_UpperCAmelCase      =			add_sub_symbol(snake_case			,     snake_case       )
												_UpperCAmelCase      =			[]
												# 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] == "##":
																								_UpperCAmelCase      =			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       )						->				Union[str, 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:
												_UpperCAmelCase      =			f.readlines()
						_UpperCAmelCase      =			[line.strip() for line in data if len(snake_case       ) > 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(snake_case			,     snake_case			,     snake_case       )

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


if __name__ == "__main__":
				a													=     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")

				a													=     parser.parse_args()
				main(args)





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)