





"""simple docstring"""




import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCamelCase_									=logging.get_logger(__name__)


UpperCamelCase_									=r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""




class 			_a						(				_lowerCAmelCase    ):






				@add_start_docstrings(lowerCAmelCase__							)
				def __call__(			self		:   Union[str, Any],			lowerCAmelCase__		:   torch.LongTensor,			lowerCAmelCase__		:   torch.FloatTensor,			**lowerCAmelCase__		:   Tuple							)     ->				bool:




										'''simple docstring'''



										raise NotImplementedError('''StoppingCriteria needs to be subclassed'''							)




class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Optional[int] = None							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   max_length
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings






				@add_start_docstrings(lowerCAmelCase__							)
				def __call__(			self		:   List[Any],			lowerCAmelCase__		:   torch.LongTensor,			lowerCAmelCase__		:   torch.FloatTensor,			**lowerCAmelCase__		:   Dict							)     ->				bool:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   input_ids.shape[-1]
										_UpperCamelCase      : List[Any]         =   cur_len >= self.max_length
										if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																logger.warning_once(
																    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
																    f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
																    '''exceptions, performance degradation, or nothing at all.'''							)
										return is_done




class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int							)     ->				Optional[int]:




										'''simple docstring'''



										warnings.warn(
										    '''The class `MaxNewTokensCriteria` is deprecated. '''
										    f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
										    '''with `max_length = start_length + max_new_tokens` instead.''',			lowerCAmelCase__,			)
										_UpperCamelCase      : Tuple         =   start_length
										_UpperCamelCase      : Union[str, Any]         =   max_new_tokens
										_UpperCamelCase      : Optional[Any]         =   start_length + max_new_tokens






				@add_start_docstrings(lowerCAmelCase__							)
				def __call__(			self		:   Optional[int],			lowerCAmelCase__		:   torch.LongTensor,			lowerCAmelCase__		:   torch.FloatTensor,			**lowerCAmelCase__		:   Optional[int]							)     ->				bool:




										'''simple docstring'''



										return input_ids.shape[-1] >= self.max_length




class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   str,			lowerCAmelCase__		:   float,			lowerCAmelCase__		:   Optional[float] = None							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   max_time
										_UpperCamelCase      : Optional[int]         =   time.time() if initial_timestamp is None else initial_timestamp






				@add_start_docstrings(lowerCAmelCase__							)
				def __call__(			self		:   Any,			lowerCAmelCase__		:   torch.LongTensor,			lowerCAmelCase__		:   torch.FloatTensor,			**lowerCAmelCase__		:   str							)     ->				bool:




										'''simple docstring'''



										return time.time() - self.initial_timestamp > self.max_time




class 			_a						(				_lowerCAmelCase    ):






				@add_start_docstrings(lowerCAmelCase__							)
				def __call__(			self		:   Any,			lowerCAmelCase__		:   torch.LongTensor,			lowerCAmelCase__		:   torch.FloatTensor,			**lowerCAmelCase__		:   Optional[int]							)     ->				bool:




										'''simple docstring'''



										return any(criteria(lowerCAmelCase__,			lowerCAmelCase__							) for criteria in self							)






				@property
				def     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										for stopping_criterium in self:
																if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																						return stopping_criterium.max_length
																elif isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																						return stopping_criterium.max_length
										return None





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   stopping_criteria.max_length
						_UpperCamelCase      : Tuple         =   deepcopy(_lowercase       )
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''	,						_lowercase       )
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowercase       )       )
						return new_stopping_criteria










"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# 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 >= 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 	a_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [get_chinese_word(_lowercase       ) for r in res]
												ltp_res.extend(_lowercase       )
						assert len(_lowercase       ) == len(_lowercase       )

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

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

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# 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      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

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


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

						UpperCamelCase_									=parser.parse_args()
						main(args)



