




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple      )     ->       Tuple:





				"""simple docstring"""


				if (
				    (cp >= 0x4_e00 and cp <= 0x9_fff)
				    or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
				    or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
				    or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
				    or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
				    or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
				    or (cp >= 0xf_900 and cp <= 0xf_aff)
				    or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
				):  #
								return True

				return False







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       List[str]:





				"""simple docstring"""


				for char in word:
								__lowerCamelCase		       =    ord(UpperCamelCase__      )
								if not _is_chinese_char(UpperCamelCase__      ):
												return 0
				return 1







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str]      )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    set()

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      set()      )     ->       Tuple:





				"""simple docstring"""


				if not chinese_word_set:
								return bert_tokens
				__lowerCamelCase		       =    max([len(UpperCamelCase__      ) for w in chinese_word_set]      )

				__lowerCamelCase		       =    bert_tokens
				__lowerCamelCase       ,						__lowerCamelCase		       =    0, len(UpperCamelCase__      )
				while start < end:
								__lowerCamelCase		       =    True
								if is_chinese(bert_word[start]      ):
												__lowerCamelCase		       =    min(end - start ,     UpperCamelCase__      )
												for i in range(UpperCamelCase__ ,     1 ,     -1      ):
																__lowerCamelCase		       =    ''.join(bert_word[start : start + i]      )
																if whole_word in chinese_word_set:
																				for j in range(start + 1 ,     start + i      ):
																								__lowerCamelCase		       =    '##' + bert_word[j]
																				__lowerCamelCase		       =    start + i
																				__lowerCamelCase		       =    False
																				break
								if single_word:
												start += 1
				return bert_word







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      LTP ,     UpperCamelCase__				:      BertTokenizer      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    []

				for i in range(0 ,     len(UpperCamelCase__      ) ,     100      ):
								__lowerCamelCase		       =    ltp_tokenizer.seg(lines[i : i + 100]      )[0]
								__lowerCamelCase		       =    [get_chinese_word(UpperCamelCase__      ) for r in res]
								ltp_res.extend(UpperCamelCase__      )
				assert len(UpperCamelCase__      ) == len(UpperCamelCase__      )

				__lowerCamelCase		       =    []
				for i in range(0 ,     len(UpperCamelCase__      ) ,     100      ):
								__lowerCamelCase		       =    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__      )

				__lowerCamelCase		       =    []
				for input_ids, chinese_word in zip(UpperCamelCase__ ,     UpperCamelCase__      ):
								__lowerCamelCase		       =    []
								for id in input_ids:
												__lowerCamelCase		       =    bert_tokenizer._convert_id_to_token(UpperCamelCase__      )
												input_tokens.append(UpperCamelCase__      )
								__lowerCamelCase		       =    add_sub_symbol(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    []
								# 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] == "##":
																__lowerCamelCase		       =    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 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


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

				__lowerCamelCase		       =    prepare_ref(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

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


if __name__ == "__main__":
					__A        =    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")

					__A        =    parser.parse_args()
					main(args)









'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



