


'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def    lowerCamelCase__   ( A     :    List[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     :    str  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    List[str]  ):




						'''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[str]   ,     A     :    set()  ):




						'''simple docstring'''






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

						UpperCAmelCase         =			bert_tokens
						UpperCAmelCase       ,       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     :    List[str]   ,     A     :    LTP   ,     A     :    BertTokenizer  ):




						'''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     :    List[str]  ):




						'''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    :    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""",
	)

	_lowercase    :    Optional[int]			     =  parser.parse_args()
	main(args)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





