
"""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


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

A__   :					List[str]							=   'pytorch_model.bin'


@dataclasses.dataclass
class 			__magic_name__       :
       UpperCamelCase_   =		dataclasses.field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
       UpperCamelCase_   =		dataclasses.field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}   ,					)


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


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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[Any]       =     datasets.concatenate_datasets([infer_input, infer_output] ,				axis=1						)

  if args.do_filter_by_confidence:
    _lowercase:			Union[str, Any]       =     dataset.filter(lambda _UpperCamelCase						: example["probability"] > args.confidence_threshold						)

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    _lowercase:			List[str]       =     int(eval_result * len(_UpperCamelCase						)						)
    print(_UpperCamelCase						)
    _lowercase:			Optional[Any]       =     dataset.sort('''probability''' ,				reverse=_UpperCamelCase						)
    _lowercase:			str       =     dataset.select(range(_UpperCamelCase						)						)

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

  _lowercase:			List[Any]       =     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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				**_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			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()

  _lowercase:			int       =     STModelArguments(model_name_or_path=_UpperCamelCase						)
  _lowercase:			Tuple       =     STDataArguments(train_file=_UpperCamelCase ,				infer_file=_UpperCamelCase						)
  _lowercase:			Any       =     STTrainingArguments(output_dir=_UpperCamelCase						)
  _lowercase:			Union[str, Any]       =     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
  _lowercase:			Optional[Any]       =     {}
  _lowercase:			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
  _lowercase:			List[Any]       =     args.train_file
  _lowercase:			Optional[Any]       =     args.infer_file

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

  for key in data_files:
    _lowercase:			str       =     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:
      _lowercase:			Union[str, 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...'''						)
  _lowercase:			Dict       =     f'''{args.output_dir}/self-train_iter-{{}}'''.format
  _lowercase:			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()

  _lowercase:			Optional[Any]       =     None
  _lowercase:			List[Any]       =     None
  _lowercase:			Tuple       =     0
  _lowercase:			Dict       =     False
  # Show the progress bar
  _lowercase:			int       =     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						)						):
    _lowercase:			Dict       =     data_dir_format(_UpperCamelCase						)
    assert os.path.exists(_UpperCamelCase						)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    _lowercase:			str       =     os.path.join(_UpperCamelCase ,				'''stage-1'''						)
    _lowercase:			Dict       =     {
        '''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}						)

    _lowercase:			str       =     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
      _lowercase:			List[str]       =     os.path.join(_UpperCamelCase ,				'''best-checkpoint'''						)
      _lowercase:			Union[str, Any]       =     os.path.join(_UpperCamelCase ,				'''stage-2'''						)
      # Update arguments_dict
      _lowercase:			Tuple       =     model_path
      _lowercase:			int       =     data_files['''train''']
      _lowercase:			Optional[Any]       =     current_output_dir

      _lowercase:			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						)

    _lowercase:			List[str]       =     iteration
    _lowercase:			List[Any]       =     data_dir_format(iteration + 1						)

    _lowercase:			Any       =     AutoConfig.from_pretrained(os.path.join(_UpperCamelCase ,				'''best-checkpoint'''						)						)
    _lowercase:			int       =     config.idalabel
    _lowercase:			Any       =     os.path.join(_UpperCamelCase ,				'''eval_results_best-checkpoint.json'''						)
    _lowercase:			Optional[int]       =     os.path.join(_UpperCamelCase ,				'''test_results_best-checkpoint.json'''						)
    assert os.path.exists(_UpperCamelCase						)

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

    _lowercase:			int       =     os.path.join(_UpperCamelCase ,				f'''train_pseudo.{args.data_file_extension}'''						)

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

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          _lowercase:			int       =     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'''						) ,				)


"""simple docstring"""




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


A__   :					Any							=   'hf-internal-testing/tiny-random-bert'
A__   :					int							=   os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
A__   :					Dict							=   '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'


class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     cached_file(A_ ,				A_       )
         # Should have downloaded the file in here
         self.assertTrue(os.path.isdir(A_       )       )
         # Cache should contain at least those three subfolders:
         for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(A_ ,				A_       )       )       )
         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			Tuple       =     f.read()
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
         self.assertTrue(os.path.isfile(A_       )       )

         # File is cached at the same place the second time.
         _lowercase:			List[Any]       =     cached_file(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )

         # Using a specific revision to test the full commit hash.
         _lowercase:			Union[str, Any]       =     cached_file(A_ ,				A_ ,				revision='''9b8c223'''       )
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           _lowercase:			Union[str, Any]       =     cached_file('''tiny-random-bert''' ,				A_       )

         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           _lowercase:			List[Any]       =     cached_file(A_ ,				A_ ,				revision='''aaaa'''       )

         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			int       =     cached_file(A_ ,				'''conf'''       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			Dict       =     cached_file(A_ ,				'''conf'''       )

         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			int       =     f.read()
         self.assertTrue(os.path.isfile(os.path.join(A_ ,				'''.no_exist''' ,				A_ ,				'''conf'''       )       )       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				local_files_only=A_ ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[Any]       =     mock.Mock()
         _lowercase:			Optional[Any]       =     500
         _lowercase:			Optional[Any]       =     {}
         _lowercase:			Dict       =     HTTPError
         _lowercase:			Optional[Any]       =     {}

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('''requests.Session.request''' ,				return_value=A_       ) as mock_head:
           _lowercase:			Tuple       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_connection_errors=A_       )
           self.assertIsNone(A_       )
           # This check we did call the fake head request
           mock_head.assert_called()
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         self.assertIsNone(get_file_from_repo('''bert-base-cased''' ,				'''ahah.txt'''       )       )

         # The function raises if the repository does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           get_file_from_repo('''bert-base-case''' ,				A_       )

         # The function raises if the revision does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           get_file_from_repo('''bert-base-cased''' ,				A_ ,				revision='''ahaha'''       )

         _lowercase:			Optional[int]       =     get_file_from_repo('''bert-base-cased''' ,				A_       )
         # The name is the cached name which is not very easy to test, so instead we load the content.
         _lowercase:			Union[str, Any]       =     json.loads(open(A_ ,				'''r'''       ).read()       )
         self.assertEqual(config['''hidden_size'''] ,				768       )



       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         with tempfile.TemporaryDirectory() as tmp_dir:
           _lowercase:			List[Any]       =     Path(A_       ) / '''a.txt'''
           filename.touch()
           self.assertEqual(get_file_from_repo(A_ ,				'''a.txt'''       ) ,				str(A_       )       )

           self.assertIsNone(get_file_from_repo(A_ ,				'''b.txt'''       )       )
