






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				# 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 >= 0X4E_00 and cp <= 0X9F_FF)
				    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
				    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
				    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
				    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
				    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
				    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
				    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
				):  #
								return True

				return False






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				# word like '180' or '身高' or '神'
				for char in word:
								lowercase__  					=     ord(lowerCamelCase_							)
								if not _is_chinese_char(lowerCamelCase_							):
												return 0
				return 1






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     set()

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






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if not chinese_word_set:
								return bert_tokens
				lowercase__  					=     max([len(lowerCamelCase_							) for w in chinese_word_set]							)

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






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []

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

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

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

				assert len(lowerCamelCase_							) == len(lowerCamelCase_							)

				return ref_ids






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				# 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:
								lowercase__  					=     f.readlines()
				lowercase__  					=     [line.strip() for line in data if len(lowerCamelCase_							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				lowercase__  					=     LTP(args.ltp							)  # faster in GPU device
				lowercase__  					=     BertTokenizer.from_pretrained(args.bert							)

				lowercase__  					=     prepare_ref(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)

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


if __name__ == "__main__":
							A__      :     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',
							)

							A__      :     Optional[Any]       =					parser.parse_args()
							main(args)












from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

								lowercase__  					=     bertabert.config.encoder.vocab_size
								lowercase__  					=     tokenizer.sep_token_id
								lowercase__  					=     tokenizer.cls_token_id
								lowercase__  					=     128

								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''train[:1%]'''    )
								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''validation[:1%]'''    )

								lowercase__  					=     train_dataset.select(range(32    )    )
								lowercase__  					=     val_dataset.select(range(16    )    )

								lowercase__  					=     4

								def _map_to_encoder_decoder_inputs(lowerCamelCase		:       str    ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												lowercase__  					=     tokenizer(batch['''article'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=512    )
												lowercase__  					=     tokenizer(batch['''highlights'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=128    )
												lowercase__  					=     inputs.input_ids
												lowercase__  					=     inputs.attention_mask

												lowercase__  					=     outputs.input_ids
												lowercase__  					=     outputs.input_ids.copy()
												lowercase__  					=     [
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase__  					=     outputs.attention_mask

												assert all(len(lowerCamelCase    ) == 512 for x in inputs.input_ids    )
												assert all(len(lowerCamelCase    ) == 128 for x in outputs.input_ids    )

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

								# map train dataset
								lowercase__  					=     train_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								train_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								# same for validation dataset
								lowercase__  					=     val_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								val_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




