
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 		UpperCAmelCase     (      a_		)						->      List[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     (      a_		)						->      Optional[Any]:







				"""simple docstring"""



				for char in word:
								__A       =					ord(a_		)
								if not _is_chinese_char(a_		):
												return 0
				return 1


def 		UpperCAmelCase     (      a_		)						->      Tuple:







				"""simple docstring"""



				__A       =					set()

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


def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[Any]:







				"""simple docstring"""



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

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


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				__A       =					[]

				for i in range(0						,  len(a_		)						,  1_0_0		):
								__A       =					ltp_tokenizer.pipeline(lines[i : i + 1_0_0]						,  tasks=["cws"]		).cws
								__A       =					[get_chinese_word(a_		) for r in res]
								ltp_res.extend(a_		)
				assert len(a_		) == len(a_		)

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

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

				assert len(a_		) == len(a_		)

				return ref_ids


def 		UpperCAmelCase     (      a_		)						->      Tuple:







				"""simple docstring"""



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

				__A       =					prepare_ref(a_						,  a_						,  a_		)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:List[Any]									=							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',
					)

					SCREAMING_SNAKE_CASE				:Optional[int]									=							parser.parse_args()
					main(args)








from ... import PretrainedConfig


UpperCAmelCase__							  =						{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      __snake_case	    =    '''nezha'''







      def __init__( self :		Union[str, Any]					,   __UpperCAmelCase :		List[str]=21_128					,   __UpperCAmelCase :		Any=768					,   __UpperCAmelCase :		int=12					,   __UpperCAmelCase :		Optional[int]=12					,   __UpperCAmelCase :		Tuple=3_072					,   __UpperCAmelCase :		Union[str, Any]="gelu"					,   __UpperCAmelCase :		List[str]=0.1					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		str=512					,   __UpperCAmelCase :		List[Any]=64					,   __UpperCAmelCase :		Tuple=2					,   __UpperCAmelCase :		List[Any]=0.02					,   __UpperCAmelCase :		List[str]=1e-1_2					,   __UpperCAmelCase :		int=0.1					,   __UpperCAmelCase :		Dict=0					,   __UpperCAmelCase :		Any=2					,   __UpperCAmelCase :		Optional[Any]=3					,   __UpperCAmelCase :		int=True					,   **__UpperCAmelCase :		Optional[Any]					,   )       ->Any:




       """simple docstring"""


       super().__init__(pad_token_id=__UpperCAmelCase					,   bos_token_id=__UpperCAmelCase					,   eos_token_id=__UpperCAmelCase					,   **__UpperCAmelCase    )

       a						    = vocab_size
       a						    = hidden_size
       a						    = num_hidden_layers
       a						    = num_attention_heads
       a						    = hidden_act
       a						    = intermediate_size
       a						    = hidden_dropout_prob
       a						    = attention_probs_dropout_prob
       a						    = max_position_embeddings
       a						    = max_relative_position
       a						    = type_vocab_size
       a						    = initializer_range
       a						    = layer_norm_eps
       a						    = classifier_dropout
       a						    = use_cache


