





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  :    Union[str, Any]						    =  logging.getLogger(__name__)

_snake_case  :    Tuple						    =  'pytorch_model.bin'



@dataclasses.dataclass
class 				_UpperCAmelCase    :

  """simple docstring"""


  a_        = dataclasses.field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}    )
  a_        = 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"""


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





@dataclasses.dataclass
class 				_UpperCAmelCase    :

  """simple docstring"""


  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=_UpperCamelCase		,    metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}		,    )
  a_        = dataclasses.field(
      default=_UpperCamelCase		,    metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}		,    )
  a_        = dataclasses.field(
      default=_UpperCamelCase		,    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=_UpperCamelCase		,    metadata={"""help""": """Random seed for initialization."""}		,    )





def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			List[Any]	):
   __lowerCAmelCase								=	datasets.concatenate_datasets([infer_input, infer_output],	axis=1	)

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

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

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

   __lowerCAmelCase								=	os.path.join(lowerCAmelCase_,	F"""train_pseudo.{args.data_file_extension}"""	)
   if args.data_file_extension == "csv":
      dataset.to_csv(lowerCAmelCase_,	index=lowerCAmelCase_	)
   else:
      dataset.to_json(lowerCAmelCase_	)




def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int],	**lowerCAmelCase_      :			Optional[Any]	):
   __lowerCAmelCase								=	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()

   __lowerCAmelCase								=	STModelArguments(model_name_or_path=lowerCAmelCase_	)
   __lowerCAmelCase								=	STDataArguments(train_file=lowerCAmelCase_,	infer_file=lowerCAmelCase_	)
   __lowerCAmelCase								=	STTrainingArguments(output_dir=lowerCAmelCase_	)
   __lowerCAmelCase								=	argparse.Namespace()

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

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

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

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

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

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

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

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

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

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

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

      __lowerCAmelCase								=	iteration
      __lowerCAmelCase								=	data_dir_format(iteration + 1	)

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

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

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

      __lowerCAmelCase								=	os.path.join(lowerCAmelCase_,	F"""train_pseudo.{args.data_file_extension}"""	)

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

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

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








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

