






'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def     A__      (					__lowerCAmelCase		:      Union[str, 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_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     A__      (					__lowerCAmelCase		:      str    ):
						# word like '180' or '身高' or '神'
						for char in word:
												lowerCamelCase__								=	ord(__UpperCamelCase    )
												if not _is_chinese_char(__UpperCamelCase    ):
																		return 0
						return 1
def     A__      (					__lowerCAmelCase		:      List[str]    ):
						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     A__      (					__lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      set()    ):
						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     A__      (					__lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      LTP ,     __lowerCAmelCase		:      BertTokenizer    ):
						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     A__      (					__lowerCAmelCase		:      Dict    ):
						# 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:
												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__":
							UpperCamelCase     :   Optional[int]	      =		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',
							)

							UpperCamelCase     :   List[Any]	      =		parser.parse_args()
							main(args)





'''simple docstring'''
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip





