






import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


SCREAMING_SNAKE_CASE__      :			Optional[int]							=		logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		"""pytorch_model.bin"""
@dataclasses.dataclass
class 						lowerCamelCase_				:
				a__					       =  dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}			,				)







@dataclasses.dataclass
class 						lowerCamelCase_				:
				a__					       =  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
				a__					       =  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''The name of the task to train on.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''The list of labels for the task.'''} )







@dataclasses.dataclass
class 						lowerCamelCase_				:
				a__					       =  dataclasses.field(
				    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
				a__					       =  dataclasses.field(
				    default='''accuracy'''			,				metadata={'''help''': '''The evaluation metric used for the task.'''} )
				a__					       =  dataclasses.field(
				    default='''no'''			,				metadata={
				        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
				    }			,				)
				a__					       =  dataclasses.field(
				    default=10			,				metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}			,				)
				a__					       =  dataclasses.field(
				    default=0.0			,				metadata={
				        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
				    }			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}			,				)
				a__					       =  dataclasses.field(
				    default=0.0			,				metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}			,				)
				a__					       =  dataclasses.field(
				    default=1_00			,				metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Random seed for initialization.'''}			,				)







def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      datasets.concatenate_datasets([infer_input, infer_output],  axis=1 )

				if args.do_filter_by_confidence:
								__magic_name__		:Tuple        =      dataset.filter(lambda snake_case : example["probability"] > args.confidence_threshold )

				if args.do_filter_by_val_performance:
								assert eval_result >= 0.0 and eval_result <= 1.0
								__magic_name__		:int        =      int(eval_result * len(snake_case ) )
								print(snake_case )
								__magic_name__		:str        =      dataset.sort('''probability''',  reverse=snake_case )
								__magic_name__		:Any        =      dataset.select(range(snake_case ) )

				__magic_name__		:Dict        =      dataset.remove_columns(['''label''', '''probability'''] )
				__magic_name__		:List[Any]        =      dataset.rename_column('''prediction''',  '''label''' )
				__magic_name__		:List[str]        =      dataset.map(lambda snake_case : {"label": idalabel[example["label"]]} )
				__magic_name__		:int        =      dataset.shuffle(seed=args.seed )

				__magic_name__		:List[str]        =      os.path.join(snake_case,  f'''train_pseudo.{args.data_file_extension}''' )
				if args.data_file_extension == "csv":
								dataset.to_csv(snake_case,  index=snake_case )
				else:
								dataset.to_json(snake_case )

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  **snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      Accelerator()
				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',  datefmt='''%m/%d/%Y %H:%M:%S''',  level=logging.INFO,  )
				logger.info(accelerator.state )

				# Setup logging, we only want one process per machine to log things on the
				# screen. accelerator.is_local_main_process is only True for one process per
				# machine.
				logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )

				if accelerator.is_local_main_process:
								datasets.utils.logging.set_verbosity_warning()
								transformers.utils.logging.set_verbosity_info()
				else:
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()

				__magic_name__		:str        =      STModelArguments(model_name_or_path=snake_case )
				__magic_name__		:Union[str, Any]        =      STDataArguments(train_file=snake_case,  infer_file=snake_case )
				__magic_name__		:str        =      STTrainingArguments(output_dir=snake_case )
				__magic_name__		:Union[str, Any]        =      argparse.Namespace()

				for arg_class in (model_args, data_args, training_args):
								for key, value in vars(snake_case ).items():
												setattr(snake_case,  snake_case,  snake_case )

				for key, value in kwargs.items():
								if hasattr(snake_case,  snake_case ):
												setattr(snake_case,  snake_case,  snake_case )

    # Sanity checks
				__magic_name__		:Optional[int]        =      {}
				__magic_name__		:List[str]        =      None

				# You need to provide the training data and the data to predict on
				assert args.train_file is not None
				assert args.infer_file is not None
				__magic_name__		:Tuple        =      args.train_file
				__magic_name__		:List[str]        =      args.infer_file

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								__magic_name__		:Optional[Any]        =      args.eval_file

				for key in data_files:
								__magic_name__		:Dict        =      data_files[key].split('''.''' )[-1]
								assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
								if args.data_file_extension is None:
												__magic_name__		:Optional[int]        =      extension
								else:
												assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

				assert (
				    args.eval_metric in datasets.list_metrics()
				), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

				# If passed along, set the training seed now.
				if args.seed is not None:
								set_seed(args.seed )

				logger.info('''Creating the initial data directory for self-training...''' )
				__magic_name__		:List[str]        =      f'''{args.output_dir}/self-train_iter-{{}}'''.format
				__magic_name__		:List[Any]        =      data_dir_format(0 )

				if accelerator.is_main_process:
								if args.output_dir is not None:
												os.makedirs(args.output_dir,  exist_ok=snake_case )
												os.makedirs(snake_case,  exist_ok=snake_case )
				accelerator.wait_for_everyone()

				__magic_name__		:Optional[Any]        =      None
				__magic_name__		:Any        =      None
				__magic_name__		:Dict        =      0
				__magic_name__		:Tuple        =      False
				# Show the progress bar
				__magic_name__		:Dict        =      tqdm(range(args.max_selftrain_iterations ),  disable=not accelerator.is_local_main_process )

				# Self-train
				for iteration in range(0,  int(args.max_selftrain_iterations ) ):
								__magic_name__		:str        =      data_dir_format(snake_case )
								assert os.path.exists(snake_case )

								# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
								# iteration > 0
								__magic_name__		:List[Any]        =      os.path.join(snake_case,  '''stage-1''' )
								__magic_name__		:Any        =      {
								    '''accelerator''': accelerator,
								    '''model_name_or_path''': args.model_name_or_path,
								    '''cache_dir''': args.cache_dir,
								    '''do_train''': True,
								    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
								    '''do_eval''': True if args.eval_file is not None else False,
								    '''eval_file''': data_files['''eval'''],
								    '''do_predict''': True,
								    '''infer_file''': data_files['''infer'''],
								    '''task_name''': args.task_name,
								    '''label_list''': args.label_list,
								    '''output_dir''': current_output_dir,
								    '''eval_metric''': args.eval_metric,
								    '''evaluation_strategy''': args.evaluation_strategy,
								    '''early_stopping_patience''': args.early_stopping_patience,
								    '''early_stopping_threshold''': args.early_stopping_threshold,
								    '''seed''': args.seed,
								}
								# Add additional training arguments
								for key, value in kwargs.items():
												if key not in arguments_dict and not hasattr(snake_case,  snake_case ):
																arguments_dict.update({key: value} )

								__magic_name__		:Any        =      os.path.join(snake_case,  '''best-checkpoint''',  snake_case )
								if os.path.exists(snake_case ):
												logger.info(
												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''',  snake_case,  snake_case,  )
								else:
												logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''',  snake_case )
												finetune(**snake_case )
												accelerator.wait_for_everyone()
												assert os.path.exists(snake_case )
												logger.info('''Self-training job completed: iteration: %d, stage: 1.''',  snake_case )

								if iteration > 0 and args.finetune_on_labeled_data:
												# Stage 2 (optional): fine-tuning on the original labeled data
												__magic_name__		:str        =      os.path.join(snake_case,  '''best-checkpoint''' )
												__magic_name__		:List[Any]        =      os.path.join(snake_case,  '''stage-2''' )
												# Update arguments_dict
												__magic_name__		:List[Any]        =      model_path
												__magic_name__		:Any        =      data_files['''train''']
												__magic_name__		:Tuple        =      current_output_dir

												__magic_name__		:List[Any]        =      os.path.join(snake_case,  '''best-checkpoint''',  snake_case )
												if os.path.exists(snake_case ):
																logger.info(
																    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''',  snake_case,  snake_case,  )
												else:
																logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''',  snake_case )
																finetune(**snake_case )
																accelerator.wait_for_everyone()
																assert os.path.exists(snake_case )
																logger.info('''Self-training job completed: iteration: %d, stage: 2.''',  snake_case )

								__magic_name__		:List[Any]        =      iteration
								__magic_name__		:Tuple        =      data_dir_format(iteration + 1 )

								__magic_name__		:Optional[int]        =      AutoConfig.from_pretrained(os.path.join(snake_case,  '''best-checkpoint''' ) )
								__magic_name__		:Union[str, Any]        =      config.idalabel
								__magic_name__		:int        =      os.path.join(snake_case,  '''eval_results_best-checkpoint.json''' )
								__magic_name__		:List[Any]        =      os.path.join(snake_case,  '''test_results_best-checkpoint.json''' )
								assert os.path.exists(snake_case )

								with open(snake_case,  '''r''' ) as f:
												__magic_name__		:List[str]        =      float(json.load(snake_case )[args.eval_metric] )
								__magic_name__		:Union[str, Any]        =      os.path.join(snake_case,  '''infer_output_best-checkpoint.csv''' )
								assert os.path.exists(snake_case )
								# Loading the dataset from local csv or json files.
								__magic_name__		:List[Any]        =      load_dataset(args.data_file_extension,  data_files={'''data''': data_files['''infer''']} )['''data''']
								__magic_name__		:Optional[Any]        =      load_dataset('''csv''',  data_files={'''data''': infer_output_file} )['''data''']

								if accelerator.is_main_process:
												os.makedirs(snake_case,  exist_ok=snake_case )
												shutil.copy(snake_case,  os.path.join(snake_case,  f'''eval_results_iter-{iteration}.json''' ) )
												if os.path.exists(snake_case ):
																shutil.copy(snake_case,  os.path.join(snake_case,  f'''test_results_iter-{iteration}.json''' ) )
												create_pseudo_labeled_data(snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case )
								accelerator.wait_for_everyone()

								__magic_name__		:Tuple        =      os.path.join(snake_case,  f'''train_pseudo.{args.data_file_extension}''' )

								if args.evaluation_strategy != IntervalStrategy.NO.value:
												__magic_name__		:int        =      eval_result

												if best_iteration is None:
																__magic_name__		:List[str]        =      new_iteration
																__magic_name__		:List[str]        =      new_eval_result
												else:
																if new_eval_result - best_eval_result > args.early_stopping_threshold:
																				__magic_name__		:Any        =      new_iteration
																				__magic_name__		:Dict        =      new_eval_result
																				__magic_name__		:str        =      0
																else:
																				if new_eval_result == best_eval_result:
																								__magic_name__		:Tuple        =      new_iteration
																								__magic_name__		:List[str]        =      new_eval_result
																				early_stopping_patience_counter += 1

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				__magic_name__		:Tuple        =      True

								progress_bar.update(1 )

								if should_training_stop:
												break

				if best_iteration is not None:
								# Save the best iteration
								logger.info('''Best iteration: %d''',  snake_case )
								logger.info('''Best evaluation result: %s = %f''',  args.eval_metric,  snake_case )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(snake_case,  f'''eval_results_iter-{iteration}.json''' ),  os.path.join(snake_case,  '''eval_results_best-iteration.json''' ),  )
				else:
								# Assume that the last iteration is the best
								logger.info('''Best iteration: %d''',  args.max_selftrain_iterations - 1 )
								logger.info('''Best evaluation result: %s = %f''',  args.eval_metric,  snake_case )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(snake_case,  f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ),  os.path.join(snake_case,  '''eval_results_best-iteration.json''' ),  )














import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def      __lowercase					( snake_case ):







				"""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      __lowercase					( snake_case ):







				"""simple docstring"""






				for char in word:
								__magic_name__		:Union[str, Any]        =      ord(snake_case )
								if not _is_chinese_char(snake_case ):
												return 0
				return 1

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      set()

				for token in tokens:
								__magic_name__		:Dict        =      len(snake_case ) > 1 and is_chinese(snake_case )
								if chinese_word:
												word_set.add(snake_case )
				__magic_name__		:str        =      list(snake_case )
				return word_list

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if not chinese_word_set:
								return bert_tokens
				__magic_name__		:int        =      max([len(snake_case ) for w in chinese_word_set] )

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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      []

				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:List[str]        =      ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
								__magic_name__		:List[str]        =      [get_chinese_word(snake_case ) for r in res]
								ltp_res.extend(snake_case )
				assert len(snake_case ) == len(snake_case )

				__magic_name__		:Union[str, Any]        =      []
				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:str        =      bert_tokenizer(lines[i : i + 1_0_0],  add_special_tokens=snake_case,  truncation=snake_case,  max_length=5_1_2 )
								bert_res.extend(res['''input_ids'''] )
				assert len(snake_case ) == len(snake_case )

				__magic_name__		:Union[str, Any]        =      []
				for input_ids, chinese_word in zip(snake_case,  snake_case ):
								__magic_name__		:Any        =      []
								for id in input_ids:
												__magic_name__		:List[Any]        =      bert_tokenizer._convert_id_to_token(snake_case )
												input_tokens.append(snake_case )
								__magic_name__		:Dict        =      add_sub_symbol(snake_case,  snake_case )
								__magic_name__		:Optional[Any]        =      []
								# 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] == "##":
																__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






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

				__magic_name__		:Optional[int]        =      prepare_ref(snake_case,  snake_case,  snake_case )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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""")

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					main(args)






