

"""simple docstring"""






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		"sew-d"

  def __init__(		self    ,_A=32    ,_A=768    ,_A=12    ,_A=12    ,_A=3072    ,_A=2    ,_A=512    ,_A=256    ,_A=True    ,_A=True    ,_A=("p2c", "c2p")    ,_A="layer_norm"    ,_A="gelu_python"    ,_A=0.1    ,_A=0.1    ,_A=0.1    ,_A=0.0    ,_A=0.1    ,_A=0.0_2    ,_A=1E-7    ,_A=1E-5    ,_A="group"    ,_A="gelu"    ,_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,_A=False    ,_A=128    ,_A=16    ,_A=True    ,_A=0.0_5    ,_A=10    ,_A=2    ,_A=0.0    ,_A=10    ,_A=0    ,_A="mean"    ,_A=False    ,_A=False    ,_A=256    ,_A=0    ,_A=1    ,_A=2    ,**_A    ,):
      '''simple docstring'''

      super().__init__(**_A    ,pad_token_id=_A    ,bos_token_id=_A    ,eos_token_id=_A      )
      _lowerCAmelCase       :   int  =    hidden_size
      _lowerCAmelCase       :   Any  =    feat_extract_norm
      _lowerCAmelCase       :   Optional[int]  =    feat_extract_activation
      _lowerCAmelCase       :   Optional[int]  =    list(_A      )
      _lowerCAmelCase       :   List[Any]  =    list(_A      )
      _lowerCAmelCase       :   Tuple  =    list(_A      )
      _lowerCAmelCase       :   Dict  =    conv_bias
      _lowerCAmelCase       :   Optional[Any]  =    num_conv_pos_embeddings
      _lowerCAmelCase       :   str  =    num_conv_pos_embedding_groups
      _lowerCAmelCase       :   Optional[Any]  =    len(self.conv_dim      )
      _lowerCAmelCase       :   Union[str, Any]  =    num_hidden_layers
      _lowerCAmelCase       :   Optional[int]  =    intermediate_size
      _lowerCAmelCase       :   Dict  =    squeeze_factor
      _lowerCAmelCase       :   Any  =    max_position_embeddings
      _lowerCAmelCase       :   Optional[int]  =    position_buckets
      _lowerCAmelCase       :   Union[str, Any]  =    share_att_key
      _lowerCAmelCase       :   Tuple  =    relative_attention
      _lowerCAmelCase       :   int  =    norm_rel_ebd
      _lowerCAmelCase       :   str  =    list(_A      )
      _lowerCAmelCase       :   str  =    hidden_act
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   List[Any]  =    hidden_dropout
      _lowerCAmelCase       :   Optional[Any]  =    attention_dropout
      _lowerCAmelCase       :   List[str]  =    activation_dropout
      _lowerCAmelCase       :   int  =    feat_proj_dropout
      _lowerCAmelCase       :   Union[str, Any]  =    final_dropout
      _lowerCAmelCase       :   Any  =    layer_norm_eps
      _lowerCAmelCase       :   str  =    feature_layer_norm_eps
      _lowerCAmelCase       :   List[str]  =    initializer_range
      _lowerCAmelCase       :   Any  =    vocab_size

      if (
          (len(self.conv_stride      ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
          or (len(self.conv_dim      ) != self.num_feat_extract_layers)
      ):
          raise ValueError(
              'Configuration for convolutional layers is incorrect.'
              'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
              F"""but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)"""
              F"""= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`."""      )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowerCAmelCase       :   Tuple  =    apply_spec_augment
      _lowerCAmelCase       :   List[Any]  =    mask_time_prob
      _lowerCAmelCase       :   str  =    mask_time_length
      _lowerCAmelCase       :   Union[str, Any]  =    mask_time_min_masks
      _lowerCAmelCase       :   Any  =    mask_feature_prob
      _lowerCAmelCase       :   List[str]  =    mask_feature_length
      _lowerCAmelCase       :   List[Any]  =    mask_feature_min_masks

      # ctc loss
      _lowerCAmelCase       :   Optional[Any]  =    ctc_loss_reduction
      _lowerCAmelCase       :   List[str]  =    ctc_zero_infinity

      # sequence classification
      _lowerCAmelCase       :   Union[str, Any]  =    use_weighted_layer_sum
      _lowerCAmelCase       :   str  =    classifier_proj_size







  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return functools.reduce(operator.mul    ,self.conv_stride    ,1      )








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''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__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

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

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

    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    return ref_ids

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





