


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


UpperCAmelCase      =   logging.getLogger(__name__)

UpperCAmelCase      =   """pytorch_model.bin"""







@dataclasses.dataclass
class lowerCAmelCase_						:

 '''simple docstring'''

 __snake_case												=  dataclasses.field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}	)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} ,						)







@dataclasses.dataclass
class lowerCAmelCase_						:

 '''simple docstring'''

 __snake_case												=  dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}	)
 __snake_case												=  dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}	)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "A csv or a json file containing the validation data."}	)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "The name of the task to train on."} ,						)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "The list of labels for the task."}	)







@dataclasses.dataclass
class lowerCAmelCase_						:

 '''simple docstring'''

 __snake_case												=  dataclasses.field(
     metadata={"help": "The output directory where the model predictions and checkpoints will be written."}	)
 __snake_case												=  dataclasses.field(
     default="accuracy" ,						metadata={"help": "The evaluation metric used for the task."}	)
 __snake_case												=  dataclasses.field(
     default="no" ,						metadata={
         "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
     } ,						)
 __snake_case												=  dataclasses.field(
     default=1_0 ,						metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,						)
 __snake_case												=  dataclasses.field(
     default=0.0 ,						metadata={
         "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
     } ,						)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} ,						)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} ,						)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} ,						)
 __snake_case												=  dataclasses.field(
     default=0.0 ,						metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} ,						)
 __snake_case												=  dataclasses.field(
     default=1_0_0 ,						metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,						)
 __snake_case												=  dataclasses.field(
     default=lowerCamelCase__ ,						metadata={"help": "Random seed for initialization."} ,						)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )

      if args.do_filter_by_confidence:
            snake_case_	=				dataset.filter(lambda SCREAMING_SNAKE_CASE : example["probability"] > args.confidence_threshold )

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

      snake_case_	=				dataset.remove_columns(['''label''', '''probability'''] )
      snake_case_	=				dataset.rename_column('''prediction''' , '''label''' )
      snake_case_	=				dataset.map(lambda SCREAMING_SNAKE_CASE : {"label": idalabel[example["label"]]} )
      snake_case_	=				dataset.shuffle(seed=args.seed )

      snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , f'''train_pseudo.{args.data_file_extension}''' )
      if args.data_file_extension == "csv":
            dataset.to_csv(SCREAMING_SNAKE_CASE , index=SCREAMING_SNAKE_CASE )
      else:
            dataset.to_json(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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()

      snake_case_	=				STModelArguments(model_name_or_path=SCREAMING_SNAKE_CASE )
      snake_case_	=				STDataArguments(train_file=SCREAMING_SNAKE_CASE , infer_file=SCREAMING_SNAKE_CASE )
      snake_case_	=				STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE )
      snake_case_	=				argparse.Namespace()

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

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

    # Sanity checks
      snake_case_	=				{}
      snake_case_	=				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
      snake_case_	=				args.train_file
      snake_case_	=				args.infer_file

      if args.evaluation_strategy != IntervalStrategy.NO.value:
            assert args.eval_file is not None
            snake_case_	=				args.eval_file

      for key in data_files:
            snake_case_	=				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:
                  snake_case_	=				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...''' )
      snake_case_	=				f'''{args.output_dir}/self-train_iter-{{}}'''.format
      snake_case_	=				data_dir_format(0 )

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

      snake_case_	=				None
      snake_case_	=				None
      snake_case_	=				0
      snake_case_	=				False
      # Show the progress bar
      snake_case_	=				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 ) ):
            snake_case_	=				data_dir_format(SCREAMING_SNAKE_CASE )
            assert os.path.exists(SCREAMING_SNAKE_CASE )

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , '''stage-1''' )
            snake_case_	=				{
                '''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(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
                        arguments_dict.update({key: value} )

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

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

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

            snake_case_	=				iteration
            snake_case_	=				data_dir_format(iteration + 1 )

            snake_case_	=				AutoConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE , '''best-checkpoint''' ) )
            snake_case_	=				config.idalabel
            snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , '''eval_results_best-checkpoint.json''' )
            snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , '''test_results_best-checkpoint.json''' )
            assert os.path.exists(SCREAMING_SNAKE_CASE )

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

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

            snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , f'''train_pseudo.{args.data_file_extension}''' )

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  snake_case_	=				eval_result

                  if best_iteration is None:
                        snake_case_	=				new_iteration
                        snake_case_	=				new_eval_result
                  else:
                        if new_eval_result - best_eval_result > args.early_stopping_threshold:
                              snake_case_	=				new_iteration
                              snake_case_	=				new_eval_result
                              snake_case_	=				0
                        else:
                              if new_eval_result == best_eval_result:
                                    snake_case_	=				new_iteration
                                    snake_case_	=				new_eval_result
                              early_stopping_patience_counter += 1

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              snake_case_	=				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''' , SCREAMING_SNAKE_CASE )
            logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , SCREAMING_SNAKE_CASE )
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(SCREAMING_SNAKE_CASE , f'''eval_results_iter-{iteration}.json''' ) , os.path.join(SCREAMING_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 , SCREAMING_SNAKE_CASE )
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(SCREAMING_SNAKE_CASE , f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ) , os.path.join(SCREAMING_SNAKE_CASE , '''eval_results_best-iteration.json''' ) , )



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """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   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      for char in word:
            snake_case_	=				ord(SCREAMING_SNAKE_CASE )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE ):
                  return 0
      return 1






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				set()

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				[]

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

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

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

      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

      return ref_ids






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

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

      snake_case_	=				prepare_ref(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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


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)