
'''simple docstring'''




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    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 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    for char in word:
        A       :   Optional[int]					=						ord(snake_case__						)
        if not _is_chinese_char(snake_case__						):
            return 0
    return 1







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						set()

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Tuple					=						[]

    for i in range(0		,				len(snake_case__						)		,				100						):
        A       :   Optional[Any]					=						ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        A       :   List[str]					=						[get_chinese_word(snake_case__						) for r in res]
        ltp_res.extend(snake_case__						)
    assert len(snake_case__						) == len(snake_case__						)

    A       :   Any					=						[]
    for i in range(0		,				len(snake_case__						)		,				100						):
        A       :   Optional[int]					=						bert_tokenizer(lines[i : i + 100]		,				add_special_tokens=snake_case__		,				truncation=snake_case__		,				max_length=512						)
        bert_res.extend(res['''input_ids''']						)
    assert len(snake_case__						) == len(snake_case__						)

    A       :   List[str]					=						[]
    for input_ids, chinese_word in zip(snake_case__		,				snake_case__						):
        A       :   List[str]					=						[]
        for id in input_ids:
            A       :   Any					=						bert_tokenizer._convert_id_to_token(snake_case__						)
            input_tokens.append(snake_case__						)
        A       :   Dict					=						add_sub_symbol(snake_case__		,				snake_case__						)
        A       :   Union[str, Any]					=						[]
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(snake_case__						):
            if token[:2] == "##":
                A       :   Dict					=						token[2:]
                # save chinese tokens' pos
                if len(snake_case__						) == 1 and _is_chinese_char(ord(snake_case__						)						):
                    ref_id.append(snake_case__						)
        ref_ids.append(snake_case__						)

    assert len(snake_case__						) == len(snake_case__						)

    return ref_ids







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



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

    A       :   Union[str, Any]					=						prepare_ref(snake_case__		,				snake_case__		,				snake_case__						)

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


if __name__ == "__main__":
      lowercase			:				Any    		=			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			:				Union[str, Any]    		=			parser.parse_args()
      main(args)









"""simple docstring"""






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


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

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

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={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							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs




