
"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_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_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										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  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					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__":
				_a								=						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""",
				)

				_a								=						parser.parse_args()
				main(args)







'''simple docstring'''


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase						:							int    							=						logging.getLogger(__name__)






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   42






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   None
							a_										=   None






class     A ( UpperCAmelCase ):
							a_										=   '''train'''
							a_										=   '''dev'''
							a_										=   '''test'''






class     A :





							@staticmethod
							def    snake_case__    (   __a					:				List[Any]  ,		__a					:				Union[Split, str]						)				->						List[InputExample]:
								raise NotImplementedError





							@staticmethod
							def    snake_case__    (   __a					:				str						)				->						List[str]:
								raise NotImplementedError






							@staticmethod
							def    snake_case__    (   __a					:				List[InputExample]  ,		__a					:				List[str]  ,		__a					:				int  ,		__a					:				PreTrainedTokenizer  ,		__a					:				Dict=False  ,		__a					:				int="[CLS]"  ,		__a					:				Dict=1  ,		__a					:				Tuple="[SEP]"  ,		__a					:				Any=False  ,		__a					:				Union[str, Any]=False  ,		__a					:				Any=0  ,		__a					:				Optional[int]=0  ,		__a					:				Tuple=-1_0_0  ,		__a					:				Optional[Any]=0  ,		__a					:				int=True  ,		)				->						List[InputFeatures]:
								__UpperCAmelCase      					=      {label: i for i, label in enumerate(__a						)}

								__UpperCAmelCase      					=      []
								for ex_index, example in enumerate(__a						):
									if ex_index % 1_0_0_0_0 == 0:
										logger.info('''Writing example %d of %d'''  ,		__a  ,		len(__a						)						)

									__UpperCAmelCase      					=      []
									__UpperCAmelCase      					=      []
									for word, label in zip(example.words  ,		example.labels						):
										__UpperCAmelCase      					=      tokenizer.tokenize(__a						)

										# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
										if len(__a						) > 0:
											tokens.extend(__a						)
											# Use the real label id for the first token of the word, and padding ids for the remaining tokens
											label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__a						) - 1)						)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
									__UpperCAmelCase      					=      tokenizer.num_special_tokens_to_add()
									if len(__a						) > max_seq_length - special_tokens_count:
										__UpperCAmelCase      					=      tokens[: (max_seq_length - special_tokens_count)]
										__UpperCAmelCase      					=      label_ids[: (max_seq_length - special_tokens_count)]

									# The convention in BERT is:
									# (a) For sequence pairs:
									#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
									#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
									# (b) For single sequences:
									#  tokens:   [CLS] the dog is hairy . [SEP]
									#  type_ids:   0   0   0   0  0     0   0
									#
									# Where "type_ids" are used to indicate whether this is the first
									# sequence or the second sequence. The embedding vectors for `type=0` and
									# `type=1` were learned during pre-training and are added to the wordpiece
									# embedding vector (and position vector). This is not *strictly* necessary
									# since the [SEP] token unambiguously separates the sequences, but it makes
									# it easier for the model to learn the concept of sequences.
									#
									# For classification tasks, the first vector (corresponding to [CLS]) is
									# used as the "sentence vector". Note that this only makes sense because
									# the entire model is fine-tuned.
									tokens += [sep_token]
									label_ids += [pad_token_label_id]
									if sep_token_extra:
										# roberta uses an extra separator b/w pairs of sentences
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
									__UpperCAmelCase      					=      [sequence_a_segment_id] * len(__a						)

									if cls_token_at_end:
										tokens += [cls_token]
										label_ids += [pad_token_label_id]
										segment_ids += [cls_token_segment_id]
									else:
										__UpperCAmelCase      					=      [cls_token] + tokens
										__UpperCAmelCase      					=      [pad_token_label_id] + label_ids
										__UpperCAmelCase      					=      [cls_token_segment_id] + segment_ids

									__UpperCAmelCase      					=      tokenizer.convert_tokens_to_ids(__a						)

									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									__UpperCAmelCase      					=      [1 if mask_padding_with_zero else 0] * len(__a						)

									# Zero-pad up to the sequence length.
									__UpperCAmelCase      					=      max_seq_length - len(__a						)
									if pad_on_left:
										__UpperCAmelCase      					=      ([pad_token] * padding_length) + input_ids
										__UpperCAmelCase      					=      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
										__UpperCAmelCase      					=      ([pad_token_segment_id] * padding_length) + segment_ids
										__UpperCAmelCase      					=      ([pad_token_label_id] * padding_length) + label_ids
									else:
										input_ids += [pad_token] * padding_length
										input_mask += [0 if mask_padding_with_zero else 1] * padding_length
										segment_ids += [pad_token_segment_id] * padding_length
										label_ids += [pad_token_label_id] * padding_length

									assert len(__a						) == max_seq_length
									assert len(__a						) == max_seq_length
									assert len(__a						) == max_seq_length
									assert len(__a						) == max_seq_length

									if ex_index < 5:
										logger.info('''*** Example ***'''						)
										logger.info('''guid: %s'''  ,		example.guid						)
										logger.info('''tokens: %s'''  ,		''' '''.join([str(__a						) for x in tokens]						)						)
										logger.info('''input_ids: %s'''  ,		''' '''.join([str(__a						) for x in input_ids]						)						)
										logger.info('''input_mask: %s'''  ,		''' '''.join([str(__a						) for x in input_mask]						)						)
										logger.info('''segment_ids: %s'''  ,		''' '''.join([str(__a						) for x in segment_ids]						)						)
										logger.info('''label_ids: %s'''  ,		''' '''.join([str(__a						) for x in label_ids]						)						)

									if "token_type_ids" not in tokenizer.model_input_names:
										__UpperCAmelCase      					=      None

									features.append(
									    InputFeatures(
									        input_ids=__a  ,		attention_mask=__a  ,		token_type_ids=__a  ,		label_ids=__a						)						)
								return features


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset






					class     A ( UpperCAmelCase ):
												a_										=   42
												a_										=   nn.CrossEntropyLoss().ignore_index





												def __init__(   self					:				List[Any]  ,		__a					:				TokenClassificationTask  ,		__a					:				str  ,		__a					:				PreTrainedTokenizer  ,		__a					:				List[str]  ,		__a					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Dict=False  ,		__a					:				Split = Split.train  ,		)				->						Optional[int]:
													# Load data features from cache or dataset file
													__UpperCAmelCase      					=      os.path.join(
													    __a  ,		'''cached_{}_{}_{}'''.format(mode.value  ,		tokenizer.__class__.__name__  ,		str(__a						)						)  ,		)

													# Make sure only the first process in distributed training processes the dataset,
													# and the others will use the cache.
													__UpperCAmelCase      					=      cached_features_file + '''.lock'''
													with FileLock(__a						):
														if os.path.exists(__a						) and not overwrite_cache:
															logger.info(f"""Loading features from cached file {cached_features_file}"""						)
															__UpperCAmelCase      					=      torch.load(__a						)
														else:
															logger.info(f"""Creating features from dataset file at {data_dir}"""						)
															__UpperCAmelCase      					=      token_classification_task.read_examples_from_file(__a  ,		__a						)
															# TODO clean up all this to leverage built-in features of tokenizers
															__UpperCAmelCase      					=      token_classification_task.convert_examples_to_features(
															    __a  ,		__a  ,		__a  ,		__a  ,		cls_token_at_end=bool(model_type in ['''xlnet''']						)  ,		cls_token=tokenizer.cls_token  ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0  ,		sep_token=tokenizer.sep_token  ,		sep_token_extra=__a  ,		pad_on_left=bool(tokenizer.padding_side == '''left'''						)  ,		pad_token=tokenizer.pad_token_id  ,		pad_token_segment_id=tokenizer.pad_token_type_id  ,		pad_token_label_id=self.pad_token_label_id  ,		)
															logger.info(f"""Saving features into cached file {cached_features_file}"""						)
															torch.save(self.features  ,		__a						)





												def __len__(   self					:				List[Any]						)				->						Union[str, Any]:
													return len(self.features						)






												def __getitem__(   self					:				int  ,		__a					:				int						)				->						InputFeatures:
													return self.features[i]


if is_tf_available():
					import tensorflow as tf






					class     A :
												a_										=   42
												a_										=   -1_0_0





												def __init__(   self					:				Union[str, Any]  ,		__a					:				TokenClassificationTask  ,		__a					:				str  ,		__a					:				PreTrainedTokenizer  ,		__a					:				List[str]  ,		__a					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Any=False  ,		__a					:				Split = Split.train  ,		)				->						Union[str, Any]:
													__UpperCAmelCase      					=      token_classification_task.read_examples_from_file(__a  ,		__a						)
													# TODO clean up all this to leverage built-in features of tokenizers
													__UpperCAmelCase      					=      token_classification_task.convert_examples_to_features(
													    __a  ,		__a  ,		__a  ,		__a  ,		cls_token_at_end=bool(model_type in ['''xlnet''']						)  ,		cls_token=tokenizer.cls_token  ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0  ,		sep_token=tokenizer.sep_token  ,		sep_token_extra=__a  ,		pad_on_left=bool(tokenizer.padding_side == '''left'''						)  ,		pad_token=tokenizer.pad_token_id  ,		pad_token_segment_id=tokenizer.pad_token_type_id  ,		pad_token_label_id=self.pad_token_label_id  ,		)

													def gen():
														for ex in self.features:
															if ex.token_type_ids is None:
																yield (
																    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																    ex.label_ids,
																)
															else:
																yield (
																    {
																        "input_ids": ex.input_ids,
																        "attention_mask": ex.attention_mask,
																        "token_type_ids": ex.token_type_ids,
																    },
																    ex.label_ids,
																)

													if "token_type_ids" not in tokenizer.model_input_names:
														__UpperCAmelCase      					=      tf.data.Dataset.from_generator(
														    __a  ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)  ,		(
														        {'''input_ids''': tf.TensorShape([None]						), '''attention_mask''': tf.TensorShape([None]						)},
														        tf.TensorShape([None]						),
														    )  ,		)
													else:
														__UpperCAmelCase      					=      tf.data.Dataset.from_generator(
														    __a  ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)  ,		(
														        {
														            '''input_ids''': tf.TensorShape([None]						),
														            '''attention_mask''': tf.TensorShape([None]						),
														            '''token_type_ids''': tf.TensorShape([None]						),
														        },
														        tf.TensorShape([None]						),
														    )  ,		)





												def    snake_case__    (   self					:				Union[str, Any]						)				->						Optional[Any]:
													__UpperCAmelCase      					=      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

													return self.dataset





												def __len__(   self					:				int						)				->						str:
													return len(self.features						)






												def __getitem__(   self					:				int  ,		__a					:				List[Any]						)				->						InputFeatures:
													return self.features[i]
