






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	UpperCamelCase	(							snake_case__  : Any	):




     '''simple docstring'''






     if (
         (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
         or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
         or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
         or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
         or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
         or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
         or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
         or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
     ):  #
          return True

     return False






def 	UpperCamelCase	(							snake_case__  : str	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    set()

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






def 	UpperCamelCase	(							snake_case__  : List[str]						,snake_case__  : set()	):




     '''simple docstring'''






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

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






def 	UpperCamelCase	(							snake_case__  : List[str]						,snake_case__  : LTP						,snake_case__  : BertTokenizer	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    []

     for i in range(0						,len(snake_case__	)						,100	):
          __snake_case      :List[Any] 							=    ltp_tokenizer.pipeline(lines[i : i + 100]						,tasks=["""cws"""]	).cws
          __snake_case      :Optional[int] 							=    [get_chinese_word(snake_case__	) for r in res]
          ltp_res.extend(snake_case__	)
     assert len(snake_case__	) == len(snake_case__	)

     __snake_case      :Any 							=    []
     for i in range(0						,len(snake_case__	)						,100	):
          __snake_case      :Optional[Any] 							=    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__	)

     __snake_case      :List[Any] 							=    []
     for input_ids, chinese_word in zip(snake_case__						,snake_case__	):
          __snake_case      :List[Any] 							=    []
          for id in input_ids:
               __snake_case      :List[str] 							=    bert_tokenizer._convert_id_to_token(snake_case__	)
               input_tokens.append(snake_case__	)
          __snake_case      :Optional[int] 							=    add_sub_symbol(snake_case__						,snake_case__	)
          __snake_case      :List[str] 							=    []
          # 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] == "##":
                    __snake_case      :Tuple 							=    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 	UpperCamelCase	(							snake_case__  : List[str]	):




     '''simple docstring'''






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

     __snake_case      :Dict 							=    prepare_ref(snake_case__						,snake_case__						,snake_case__	)

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


if __name__ == "__main__":
     lowerCamelCase__       		=  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""",
     )

     lowerCamelCase__       		=  parser.parse_args()
     main(args)










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			"focalnet"



       def __init__(				self      ,    a__=2_24      ,    a__=4      ,    a__=3      ,    a__=96      ,    a__=False      ,    a__=[1_92, 3_84, 7_68, 7_68]      ,    a__=[2, 2, 6, 2]      ,    a__=[2, 2, 2, 2]      ,    a__=[3, 3, 3, 3]      ,    a__="gelu"      ,    a__=4.0      ,    a__=0.0      ,    a__=0.1      ,    a__=False      ,    a__=1e-4      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=0.02      ,    a__=1e-5      ,    a__=32      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Tuple:






            '''simple docstring'''





            super().__init__(**a__     )

            __snake_case      :Optional[Any] 							=    image_size
            __snake_case      :Dict 							=    patch_size
            __snake_case      :Tuple 							=    num_channels
            __snake_case      :List[str] 							=    embed_dim
            __snake_case      :Any 							=    use_conv_embed
            __snake_case      :List[Any] 							=    hidden_sizes
            __snake_case      :str 							=    depths
            __snake_case      :List[Any] 							=    focal_levels
            __snake_case      :int 							=    focal_windows
            __snake_case      :Any 							=    hidden_act
            __snake_case      :List[str] 							=    mlp_ratio
            __snake_case      :str 							=    hidden_dropout_prob
            __snake_case      :List[str] 							=    drop_path_rate
            __snake_case      :int 							=    use_layerscale
            __snake_case      :int 							=    layerscale_value
            __snake_case      :Tuple 							=    use_post_layernorm
            __snake_case      :Dict 							=    use_post_layernorm_in_modulation
            __snake_case      :Union[str, Any] 							=    normalize_modulator
            __snake_case      :List[str] 							=    initializer_range
            __snake_case      :Optional[Any] 							=    layer_norm_eps
            __snake_case      :Tuple 							=    encoder_stride
            __snake_case      :List[str] 							=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,    len(self.depths     ) + 1     )]
            __snake_case							,			__snake_case      :str 							=    get_aligned_output_features_output_indices(
                out_features=a__      ,    out_indices=a__      ,    stage_names=self.stage_names     )


