






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


__snake_case												=  logging.getLogger(__name__)

__snake_case												=  '''pytorch_model.bin'''







@dataclasses.dataclass
class 			UpperCAmelCase_						:



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







@dataclasses.dataclass
class 			UpperCAmelCase_						:



      """simple docstring"""
      UpperCamelCase_				:					List[str]           =dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
      UpperCamelCase_				:					Dict           =dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
      UpperCamelCase_				:					Union[str, Any]           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'A csv or a json file containing the validation data.'} )
      UpperCamelCase_				:					Tuple           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'The name of the task to train on.'},	)
      UpperCamelCase_				:					List[str]           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'The list of labels for the task.'} )







@dataclasses.dataclass
class 			UpperCAmelCase_						:



      """simple docstring"""
      UpperCamelCase_				:					List[str]           =dataclasses.field(
          metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
      UpperCamelCase_				:					List[Any]           =dataclasses.field(
          default='accuracy',	metadata={'help': 'The evaluation metric used for the task.'} )
      UpperCamelCase_				:					Union[str, Any]           =dataclasses.field(
          default='no',	metadata={
              'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
          },	)
      UpperCamelCase_				:					int           =dataclasses.field(
          default=10,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'},	)
      UpperCamelCase_				:					List[Any]           =dataclasses.field(
          default=0.0,	metadata={
              'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
          },	)
      UpperCamelCase_				:					Tuple           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'},	)
      UpperCamelCase_				:					int           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'},	)
      UpperCamelCase_				:					str           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'},	)
      UpperCamelCase_				:					Union[str, Any]           =dataclasses.field(
          default=0.0,	metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'},	)
      UpperCamelCase_				:					Union[str, Any]           =dataclasses.field(
          default=100,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'},	)
      UpperCamelCase_				:					Any           =dataclasses.field(
          default=UpperCamelCase_,	metadata={'help': 'Random seed for initialization.'},	)







def   _A   (				SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	Any     ,  SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :Tuple								=   datasets.concatenate_datasets([infer_input, infer_output]     ,  axis=1       )

    if args.do_filter_by_confidence:
        UpperCamelCase       :Union[str, Any]								=   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
        UpperCamelCase       :Any								=   int(eval_result * len(__UpperCamelCase       )       )
        print(__UpperCamelCase       )
        UpperCamelCase       :Union[str, Any]								=   dataset.sort('''probability'''     ,  reverse=__UpperCamelCase       )
        UpperCamelCase       :List[str]								=   dataset.select(range(__UpperCamelCase       )       )

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

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



def   _A   (				SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	Dict     ,  **SCREAMING_SNAKE_CASE__  :	Dict       ):
    UpperCamelCase       :Optional[Any]								=   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()

    UpperCamelCase       :int								=   STModelArguments(model_name_or_path=__UpperCamelCase       )
    UpperCamelCase       :Any								=   STDataArguments(train_file=__UpperCamelCase     ,  infer_file=__UpperCamelCase       )
    UpperCamelCase       :List[Any]								=   STTrainingArguments(output_dir=__UpperCamelCase       )
    UpperCamelCase       :Dict								=   argparse.Namespace()

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

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

    # Sanity checks
    UpperCamelCase       :str								=   {}
    UpperCamelCase       :Optional[int]								=   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
    UpperCamelCase       :Any								=   args.train_file
    UpperCamelCase       :int								=   args.infer_file

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

    for key in data_files:
        UpperCamelCase       :Any								=   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:
            UpperCamelCase       :Any								=   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...'''       )
    UpperCamelCase       :Dict								=   F'''{args.output_dir}/self-train_iter-{{}}'''.format
    UpperCamelCase       :Optional[int]								=   data_dir_format(0       )

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

    UpperCamelCase       :List[str]								=   None
    UpperCamelCase       :Optional[Any]								=   None
    UpperCamelCase       :int								=   0
    UpperCamelCase       :str								=   False
    # Show the progress bar
    UpperCamelCase       :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       )       ):
        UpperCamelCase       :Any								=   data_dir_format(__UpperCamelCase       )
        assert os.path.exists(__UpperCamelCase       )

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        UpperCamelCase       :Optional[Any]								=   os.path.join(__UpperCamelCase     ,  '''stage-1'''       )
        UpperCamelCase       :int								=   {
            '''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(__UpperCamelCase     ,  __UpperCamelCase       ):
                arguments_dict.update({key: value}       )

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

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

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

        UpperCamelCase       :Union[str, Any]								=   iteration
        UpperCamelCase       :Tuple								=   data_dir_format(iteration + 1       )

        UpperCamelCase       :Dict								=   AutoConfig.from_pretrained(os.path.join(__UpperCamelCase     ,  '''best-checkpoint'''       )       )
        UpperCamelCase       :int								=   config.idalabel
        UpperCamelCase       :str								=   os.path.join(__UpperCamelCase     ,  '''eval_results_best-checkpoint.json'''       )
        UpperCamelCase       :str								=   os.path.join(__UpperCamelCase     ,  '''test_results_best-checkpoint.json'''       )
        assert os.path.exists(__UpperCamelCase       )

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

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

        UpperCamelCase       :Any								=   os.path.join(__UpperCamelCase     ,  F'''train_pseudo.{args.data_file_extension}'''       )

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            UpperCamelCase       :Optional[int]								=   eval_result

            if best_iteration is None:
                UpperCamelCase       :Optional[int]								=   new_iteration
                UpperCamelCase       :Any								=   new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    UpperCamelCase       :Optional[Any]								=   new_iteration
                    UpperCamelCase       :Dict								=   new_eval_result
                    UpperCamelCase       :Any								=   0
                else:
                    if new_eval_result == best_eval_result:
                        UpperCamelCase       :Optional[int]								=   new_iteration
                        UpperCamelCase       :Dict								=   new_eval_result
                    early_stopping_patience_counter += 1

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





