





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 						SCREAMING_SNAKE_CASE__							:
      A_		:							str				=       dataclasses.field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}    )
      A_		:							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__							:
      A_		:							str				=       dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}    )
      A_		:							str				=       dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}    )
      A_		:							Optional[str]				=       dataclasses.field(
          default=A__		, metadata={'help': 'A csv or a json file containing the validation data.'}    )
      A_		:							Optional[str]				=       dataclasses.field(
          default=A__		, metadata={'help': 'The name of the task to train on.'}		, )
      A_		:							Optional[List[str]]				=       dataclasses.field(
          default=A__		, metadata={'help': 'The list of labels for the task.'}    )






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






def lowerCamelCase__       (				snake_case_      :    int			, snake_case_      :    Dict			, snake_case_      :    List[Any]			, snake_case_      :    List[Any]			, snake_case_      :    Optional[int]			, snake_case_      :    Tuple			) ->							Optional[Any]:
   __snake_case							=  datasets.concatenate_datasets([infer_input, infer_output]			, axis=1			)

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

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

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

   __snake_case							=  os.path.join(snake_case__			, f"""train_pseudo.{args.data_file_extension}"""			)
   if args.data_file_extension == "csv":
      dataset.to_csv(snake_case__			, index=snake_case__			)
   else:
      dataset.to_json(snake_case__			)







def lowerCamelCase__       (				snake_case_      :    Union[str, Any]			, snake_case_      :    List[str]			, snake_case_      :    Any			, snake_case_      :    int			, **snake_case_      :    Dict			) ->							Optional[int]:
   __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()

   __snake_case							=  STModelArguments(model_name_or_path=snake_case__			)
   __snake_case							=  STDataArguments(train_file=snake_case__			, infer_file=snake_case__			)
   __snake_case							=  STTrainingArguments(output_dir=snake_case__			)
   __snake_case							=  argparse.Namespace()

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

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

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

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

   for key in data_files:
      __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:
         __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...'''			)
   __snake_case							=  f"""{args.output_dir}/self-train_iter-{{}}""".format
   __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=snake_case__			)
         os.makedirs(snake_case__			, exist_ok=snake_case__			)
   accelerator.wait_for_everyone()

   __snake_case							=  None
   __snake_case							=  None
   __snake_case							=  0
   __snake_case							=  False
   # Show the progress bar
   __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			)			):
      __snake_case							=  data_dir_format(snake_case__			)
      assert os.path.exists(snake_case__			)

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

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

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

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

      __snake_case							=  iteration
      __snake_case							=  data_dir_format(iteration + 1			)

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

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

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

      __snake_case							=  os.path.join(snake_case__			, f"""train_pseudo.{args.data_file_extension}"""			)

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

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

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




"""simple docstring"""




A_				:      Any           =			"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich


