



"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def __lowercase			(							_a    ):





    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 __lowercase			(							_a    ):





    for char in word:
        snake_case_ :     Optional[Any]				=	ord(_UpperCAmelCase    )
        if not _is_chinese_char(_UpperCAmelCase    ):
            return 0
    return 1




def __lowercase			(							_a    ):
    snake_case_ :     Any				=	set()

    for token in tokens:
        snake_case_ :     Dict				=	len(_UpperCAmelCase    ) > 1 and is_chinese(_UpperCAmelCase    )
        if chinese_word:
            word_set.add(_UpperCAmelCase    )
    snake_case_ :     List[str]				=	list(_UpperCAmelCase    )
    return word_list




def __lowercase			(							_a		,			_a    ):





    if not chinese_word_set:
        return bert_tokens
    snake_case_ :     int				=	max([len(_UpperCAmelCase    ) for w in chinese_word_set]    )

    snake_case_ :     str				=	bert_tokens
    snake_case_ :     Optional[Any]				=	0, len(_UpperCAmelCase    )
    while start < end:
        snake_case_ :     Dict				=	True
        if is_chinese(bert_word[start]    ):
            snake_case_ :     Tuple				=	min(end - start		,			_UpperCAmelCase    )
            for i in range(_UpperCAmelCase		,			1		,			-1    ):
                snake_case_ :     Union[str, Any]				=	''''''.join(bert_word[start : start + i]    )
                if whole_word in chinese_word_set:
                    for j in range(start + 1		,			start + i    ):
                        snake_case_ :     Union[str, Any]				=	'''##''' + bert_word[j]
                    snake_case_ :     int				=	start + i
                    snake_case_ :     Tuple				=	False
                    break
        if single_word:
            start += 1
    return bert_word




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     str				=	[]

    for i in range(0		,			len(_UpperCAmelCase    )		,			100    ):
        snake_case_ :     Dict				=	ltp_tokenizer.seg(lines[i : i + 100]    )[0]
        snake_case_ :     Union[str, Any]				=	[get_chinese_word(_UpperCAmelCase    ) for r in res]
        ltp_res.extend(_UpperCAmelCase    )
    assert len(_UpperCAmelCase    ) == len(_UpperCAmelCase    )

    snake_case_ :     Optional[Any]				=	[]
    for i in range(0		,			len(_UpperCAmelCase    )		,			100    ):
        snake_case_ :     int				=	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    )

    snake_case_ :     List[str]				=	[]
    for input_ids, chinese_word in zip(_UpperCAmelCase		,			_UpperCAmelCase    ):
        snake_case_ :     List[str]				=	[]
        for id in input_ids:
            snake_case_ :     str				=	bert_tokenizer._convert_id_to_token(_UpperCAmelCase    )
            input_tokens.append(_UpperCAmelCase    )
        snake_case_ :     Any				=	add_sub_symbol(_UpperCAmelCase		,			_UpperCAmelCase    )
        snake_case_ :     Tuple				=	[]
        # 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] == "##":
                snake_case_ :     Any				=	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 __lowercase			(							_a    ):





    with open(args.file_name		,			'''r'''		,			encoding='''utf-8'''    ) as f:
        snake_case_ :     Union[str, Any]				=	f.readlines()
    snake_case_ :     str				=	[line.strip() for line in data if len(_UpperCAmelCase    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    snake_case_ :     Any				=	LTP(args.ltp    )  # faster in GPU device
    snake_case_ :     str				=	BertTokenizer.from_pretrained(args.bert    )

    snake_case_ :     Any				=	prepare_ref(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase    )

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


if __name__ == "__main__":
     lowercase__    :  str							    =	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''')

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










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
				from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A   			=logging.get_logger(__name__)

__A   			={
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """deberta-v2"""




			def __init__(   self							: Optional[int]       ,       a_							: List[str]=12_81_00       ,       a_							: Optional[Any]=15_36       ,       a_							: Optional[Any]=24       ,       a_							: List[Any]=24       ,       a_							: Optional[int]=61_44       ,       a_							: List[Any]="gelu"       ,       a_							: Any=0.1       ,       a_							: Tuple=0.1       ,       a_							: Optional[Any]=5_12       ,       a_							: Tuple=0       ,       a_							: Dict=0.0_2       ,       a_							: Optional[Any]=1e-7       ,       a_							: List[str]=False       ,       a_							: List[Any]=-1       ,       a_							: List[str]=0       ,       a_							: Optional[Any]=True       ,       a_							: List[Any]=None       ,       a_							: Optional[int]=0       ,       a_							: Tuple="gelu"       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )

					__UpperCAmelCase    :     Any													=	hidden_size
					__UpperCAmelCase    :     Any													=	num_hidden_layers
					__UpperCAmelCase    :     List[Any]													=	num_attention_heads
					__UpperCAmelCase    :     Union[str, Any]													=	intermediate_size
					__UpperCAmelCase    :     List[str]													=	hidden_act
					__UpperCAmelCase    :     int													=	hidden_dropout_prob
					__UpperCAmelCase    :     List[str]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     str													=	max_position_embeddings
					__UpperCAmelCase    :     int													=	type_vocab_size
					__UpperCAmelCase    :     Tuple													=	initializer_range
					__UpperCAmelCase    :     Optional[int]													=	relative_attention
					__UpperCAmelCase    :     int													=	max_relative_positions
					__UpperCAmelCase    :     Any													=	pad_token_id
					__UpperCAmelCase    :     int													=	position_biased_input

					# Backwards compatibility
					if type(a_ ) == str:
							__UpperCAmelCase    :     Optional[Any]													=	[x.strip() for x in pos_att_type.lower().split('''|''' )]

					__UpperCAmelCase    :     Tuple													=	pos_att_type
					__UpperCAmelCase    :     int													=	vocab_size
					__UpperCAmelCase    :     Optional[Any]													=	layer_norm_eps

					__UpperCAmelCase    :     str													=	kwargs.get('''pooler_hidden_size'''       ,       a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	pooler_dropout
					__UpperCAmelCase    :     Tuple													=	pooler_hidden_act

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					if self.task == "multiple-choice":
							__UpperCAmelCase    :     List[Any]													=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
							__UpperCAmelCase    :     int													=	{0: '''batch''', 1: '''sequence'''}
					if self._config.type_vocab_size > 0:
							return OrderedDict(
							    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)] )
					else:
							return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)] )




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return 12




			def  snake_case__     (   self							: Any       ,       a_							: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: bool = False       ,       a_							: Optional["TensorType"] = None       ,       a_							: int = 3       ,       a_							: int = 40       ,       a_							: int = 40       ,       a_							: "PreTrainedTokenizerBase" = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	super().generate_dummy_inputs(preprocessor=a_       ,       framework=a_ )
					if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
					return dummy_inputs





