


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 				lowerCamelCase__				(	A       :      str					):




						'''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__				(	A       :      List[Any]					):




						'''simple docstring'''






						for char in word:
												UpperCAmelCase					    =     ord(A					)
												if not _is_chinese_char(A					):
																		return 0
						return 1


def 				lowerCamelCase__				(	A       :      int					):




						'''simple docstring'''






						UpperCAmelCase					    =     set()

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


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Optional[int]					):




						'''simple docstring'''






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

						UpperCAmelCase					    =     bert_tokens
						UpperCAmelCase					    =     0, len(A					)
						while start < end:
												UpperCAmelCase					    =     True
												if is_chinese(bert_word[start]					):
																		UpperCAmelCase					    =     min(end - start				,       A					)
																		for i in range(A				,       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 				lowerCamelCase__				(	A       :      Optional[Any]				,       A       :      List[str]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     []

						for i in range(0				,       len(A					)				,       1_00					):
												UpperCAmelCase					    =     ltp_tokenizer.pipeline(lines[i : i + 1_00]				,       tasks=['''cws''']					).cws
												UpperCAmelCase					    =     [get_chinese_word(A					) for r in res]
												ltp_res.extend(A					)
						assert len(A					) == len(A					)

						UpperCAmelCase					    =     []
						for i in range(0				,       len(A					)				,       1_00					):
												UpperCAmelCase					    =     bert_tokenizer(lines[i : i + 1_00]				,       add_special_tokens=A				,       truncation=A				,       max_length=5_12					)
												bert_res.extend(res['''input_ids''']					)
						assert len(A					) == len(A					)

						UpperCAmelCase					    =     []
						for input_ids, chinese_word in zip(A				,       A					):
												UpperCAmelCase					    =     []
												for id in input_ids:
																		UpperCAmelCase					    =     bert_tokenizer._convert_id_to_token(A					)
																		input_tokens.append(A					)
												UpperCAmelCase					    =     add_sub_symbol(A				,       A					)
												UpperCAmelCase					    =     []
												# 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] == "##":
																								UpperCAmelCase					    =     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 				lowerCamelCase__				(	A       :      Optional[int]					):




						'''simple docstring'''






						with open(args.file_name				,       '''r'''				,       encoding='''utf-8'''					) as f:
												UpperCAmelCase					    =     f.readlines()
						UpperCAmelCase					    =     [line.strip() for line in data if len(A					) > 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(A				,       A				,       A					)

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


if __name__ == "__main__":
	_lowercase      :      Union[str, 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""",
	)

	_lowercase      :      Dict            =					parser.parse_args()
	main(args)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





