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    :				Any							     =	logging.getLogger(__name__)

__lowerCamelCase    :				Optional[Any]							     =	'''pytorch_model.bin'''







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







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







@dataclasses.dataclass
class __snake_case       :
   lowerCAmelCase_														=							dataclasses.field(
       metadata={"help": "The output directory where the model predictions and checkpoints will be written."}	)
   lowerCAmelCase_														=							dataclasses.field(
       default="accuracy"     ,	metadata={"help": "The evaluation metric used for the task."}	)
   lowerCAmelCase_														=							dataclasses.field(
       default="no"     ,	metadata={
           "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
       }     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=10     ,	metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=0.0     ,	metadata={
           "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
       }     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=lowerCamelCase_     ,	metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=lowerCamelCase_     ,	metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=lowerCamelCase_     ,	metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=0.0     ,	metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=1_00     ,	metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,	)
   lowerCAmelCase_														=							dataclasses.field(
       default=lowerCamelCase_     ,	metadata={"help": "Random seed for initialization."}     ,	)





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Any				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[int]			)							->      Tuple:







  """simple docstring"""





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

  if args.do_filter_by_confidence:
    SCREAMING_SNAKE_CASE__    							=   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
    SCREAMING_SNAKE_CASE__    							=   int(eval_result * len(__UpperCamelCase			)			)
    print(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   dataset.sort("""probability"""				,  reverse=__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   dataset.select(range(__UpperCamelCase			)			)

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

  SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any				,  __UpperCamelCase					: int				,  **__UpperCamelCase					: int			)							->      Tuple:







  """simple docstring"""





  SCREAMING_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()

  SCREAMING_SNAKE_CASE__    							=   STModelArguments(model_name_or_path=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   STDataArguments(train_file=__UpperCamelCase				,  infer_file=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   STTrainingArguments(output_dir=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   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
  SCREAMING_SNAKE_CASE__    							=   {}
  SCREAMING_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
  SCREAMING_SNAKE_CASE__    							=   args.train_file
  SCREAMING_SNAKE_CASE__    							=   args.infer_file

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

  for key in data_files:
    SCREAMING_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:
      SCREAMING_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..."""			)
  SCREAMING_SNAKE_CASE__    							=   f"""{args.output_dir}/self-train_iter-{{}}""".format
  SCREAMING_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=__UpperCamelCase			)
      os.makedirs(__UpperCamelCase				,  exist_ok=__UpperCamelCase			)
  accelerator.wait_for_everyone()

  SCREAMING_SNAKE_CASE__    							=   None
  SCREAMING_SNAKE_CASE__    							=   None
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   False
  # Show the progress bar
  SCREAMING_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			)			):
    SCREAMING_SNAKE_CASE__    							=   data_dir_format(__UpperCamelCase			)
    assert os.path.exists(__UpperCamelCase			)

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

    SCREAMING_SNAKE_CASE__    							=   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
      SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """best-checkpoint"""			)
      SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """stage-2"""			)
      # Update arguments_dict
      SCREAMING_SNAKE_CASE__    							=   model_path
      SCREAMING_SNAKE_CASE__    							=   data_files["""train"""]
      SCREAMING_SNAKE_CASE__    							=   current_output_dir

      SCREAMING_SNAKE_CASE__    							=   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			)

    SCREAMING_SNAKE_CASE__    							=   iteration
    SCREAMING_SNAKE_CASE__    							=   data_dir_format(iteration + 1			)

    SCREAMING_SNAKE_CASE__    							=   AutoConfig.from_pretrained(os.path.join(__UpperCamelCase				,  """best-checkpoint"""			)			)
    SCREAMING_SNAKE_CASE__    							=   config.idalabel
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """eval_results_best-checkpoint.json"""			)
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """test_results_best-checkpoint.json"""			)
    assert os.path.exists(__UpperCamelCase			)

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

    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  f"""train_pseudo.{args.data_file_extension}"""			)

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

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          SCREAMING_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"""				,  __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"""			)				,  )

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
