

'''simple docstring'''




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


_lowerCAmelCase         =						logging.getLogger(__name__)

_lowerCAmelCase         =						"pytorch_model.bin"






@dataclasses.dataclass
class   _SCREAMING_SNAKE_CASE   :
 __SCREAMING_SNAKE_CASE					:str      	=					dataclasses.field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					dataclasses.field(
     default=__a		,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}		,)






@dataclasses.dataclass
class   _SCREAMING_SNAKE_CASE   :
 __SCREAMING_SNAKE_CASE					:str      	=					dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}     )
 __SCREAMING_SNAKE_CASE					:str      	=					dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					dataclasses.field(
     default=__a		,metadata={"""help""": """A csv or a json file containing the validation data."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					dataclasses.field(
     default=__a		,metadata={"""help""": """The name of the task to train on."""}		,)
 __SCREAMING_SNAKE_CASE					:Optional[List[str]]      	=					dataclasses.field(
     default=__a		,metadata={"""help""": """The list of labels for the task."""}     )






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




def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    datasets.concatenate_datasets([infer_input, infer_output]		,						axis=1			)

     if args.do_filter_by_confidence:
          __magic_name__						       =    dataset.filter(lambda a			: 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(eval_result * len(a			)			)
          print(a			)
          __magic_name__						       =    dataset.sort('''probability'''		,						reverse=a			)
          __magic_name__						       =    dataset.select(range(a			)			)

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

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						**a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    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__						       =    STModelArguments(model_name_or_path=a			)
     __magic_name__						       =    STDataArguments(train_file=a		,						infer_file=a			)
     __magic_name__						       =    STTrainingArguments(output_dir=a			)
     __magic_name__						       =    argparse.Namespace()

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

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

    # Sanity checks
     __magic_name__						       =    {}
     __magic_name__						       =    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__						       =    args.train_file
     __magic_name__						       =    args.infer_file

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

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

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

     __magic_name__						       =    None
     __magic_name__						       =    None
     __magic_name__						       =    0
     __magic_name__						       =    False
     # Show the progress bar
     __magic_name__						       =    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__						       =    data_dir_format(a			)
          assert os.path.exists(a			)

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

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

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

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

          __magic_name__						       =    iteration
          __magic_name__						       =    data_dir_format(iteration + 1			)

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

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

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

          __magic_name__						       =    os.path.join(a		,						F'''train_pseudo.{args.data_file_extension}'''			)

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

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

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









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






