
"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 							A		(       snake_case   :Union[str, Any]	)   ->			int:
 # This defines a "chinese character" as anything in the CJK Unicode block:
 #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
 #
 # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
 # despite its name. The modern Korean Hangul alphabet is a different block,
 # as is Japanese Hiragana and Katakana. Those alphabets are used to write
 # space-separated words, so they are not treated specially and handled
 # like the all of the other languages.
 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 							A		(       snake_case   :str	)   ->			List[str]:
 # word like '180' or '身高' or '神'
 for char in word:
  __UpperCamelCase       						= ord(snake_case	)
  if not _is_chinese_char(snake_case	):
   return 0
 return 1






def 							A		(       snake_case   :List[str]	)   ->			Optional[Any]:
 __UpperCamelCase       						= set()

 for token in tokens:
  __UpperCamelCase       						= len(snake_case	) > 1 and is_chinese(snake_case	)
  if chinese_word:
   word_set.add(snake_case	)
 __UpperCamelCase       						= list(snake_case	)
 return word_list






def 							A		(       snake_case   :List[str]       ,      snake_case   :set()	)   ->			List[str]:
 if not chinese_word_set:
  return bert_tokens
 __UpperCamelCase       						= max([len(snake_case	) for w in chinese_word_set]	)

 __UpperCamelCase       						= bert_tokens
 __UpperCamelCase						,			__UpperCamelCase       						= 0, len(snake_case	)
 while start < end:
  __UpperCamelCase       						= True
  if is_chinese(bert_word[start]	):
   __UpperCamelCase       						= min(end - start       ,      snake_case	)
   for i in range(snake_case       ,      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 							A		(       snake_case   :List[str]       ,      snake_case   :LTP       ,      snake_case   :BertTokenizer	)   ->			List[Any]:
 __UpperCamelCase       						= []

 for i in range(0       ,      len(snake_case	)       ,      1_0_0	):
  __UpperCamelCase       						= ltp_tokenizer.seg(lines[i : i + 1_0_0]	)[0]
  __UpperCamelCase       						= [get_chinese_word(snake_case	) for r in res]
  ltp_res.extend(snake_case	)
 assert len(snake_case	) == len(snake_case	)

 __UpperCamelCase       						= []
 for i in range(0       ,      len(snake_case	)       ,      1_0_0	):
  __UpperCamelCase       						= bert_tokenizer(lines[i : i + 1_0_0]       ,      add_special_tokens=snake_case       ,      truncation=snake_case       ,      max_length=5_1_2	)
  bert_res.extend(res['input_ids']	)
 assert len(snake_case	) == len(snake_case	)

 __UpperCamelCase       						= []
 for input_ids, chinese_word in zip(snake_case       ,      snake_case	):
  __UpperCamelCase       						= []
  for id in input_ids:
   __UpperCamelCase       						= bert_tokenizer._convert_id_to_token(snake_case	)
   input_tokens.append(snake_case	)
  __UpperCamelCase       						= add_sub_symbol(snake_case       ,      snake_case	)
  __UpperCamelCase       						= []
  # 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] == "##":
    __UpperCamelCase       						= 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 							A		(       snake_case   :List[Any]	)   ->			str:
 # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
 # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
 with open(args.file_name       ,      'r'       ,      encoding='utf-8'	) as f:
  __UpperCamelCase       						= f.readlines()
 __UpperCamelCase       						= [line.strip() for line in data if len(snake_case	) > 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(snake_case       ,      snake_case       ,      snake_case	)

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


if __name__ == "__main__":
      UpperCamelCase			:       List[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")

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





"""simple docstring"""

from typing import List

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


UpperCamelCase			:       Tuple									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"efficientformer"





     def __init__(			self			,  __UpperCAmelCase = [3, 2, 6, 4]			,  __UpperCAmelCase = [48, 96, 224, 448]			,  __UpperCAmelCase = [True, True, True, True]			,  __UpperCAmelCase = 448			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 7			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 8			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 16			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = 1E-12			,  __UpperCAmelCase = 224			,  __UpperCAmelCase = 1E-05			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= mlp_expansion_ratio
      __UpperCamelCase       						= downsamples
      __UpperCamelCase       						= dim
      __UpperCamelCase       						= key_dim
      __UpperCamelCase       						= attention_ratio
      __UpperCamelCase       						= resolution
      __UpperCamelCase       						= pool_size
      __UpperCamelCase       						= downsample_patch_size
      __UpperCamelCase       						= downsample_stride
      __UpperCamelCase       						= downsample_pad
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= num_metaad_blocks
      __UpperCamelCase       						= distillation
      __UpperCamelCase       						= use_layer_scale
      __UpperCamelCase       						= layer_scale_init_value
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= batch_norm_eps



