






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				# 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 >= 0X20000 and cp <= 0X2A6DF)  #
				    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
				    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
				    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
				    or (cp >= 0XF900 and cp <= 0XFAFF)
				    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
				):  #
								return True

				return False
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				# word like '180' or '身高' or '神'
				for char in word:
								snake_case__									=     ord(_A  )
								if not _is_chinese_char(_A  ):
												return 0
				return 1
def      a_ (      _A  )			->			List[str]:







				"""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      a_ (      _A      ,   _A  )			->			int:







				"""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      a_ (      _A      ,   _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     []

				for i in range(0      ,   len(_A  )      ,   100  ):
								snake_case__									=     ltp_tokenizer.seg(lines[i : i + 100]  )[0]
								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      a_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""
				# 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:
								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__":
							__UpperCamelCase					:		int	  =						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""")

							__UpperCamelCase					:		List[str]	  =						parser.parse_args()
							main(args)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




