




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      :  str      						=						logging.getLogger(__name__)

_lowerCamelCase      :  Tuple      						=						"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=_a						,	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=_a						,	metadata={"help": "A csv or a json file containing the validation data."}		)
				lowerCAmelCase__				=     dataclasses.field(
				    default=_a						,	metadata={"help": "The name of the task to train on."}						,	)
				lowerCAmelCase__				=     dataclasses.field(
				    default=_a						,	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=1_0						,	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=_a						,	metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}						,	)
				lowerCAmelCase__				=     dataclasses.field(
				    default=_a						,	metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}						,	)
				lowerCAmelCase__				=     dataclasses.field(
				    default=_a						,	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_0_0						,	metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}						,	)
				lowerCAmelCase__				=     dataclasses.field(
				    default=_a						,	metadata={"help": "Random seed for initialization."}						,	)






def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      datasets.concatenate_datasets([infer_input, infer_output]      ,    axis=1       )

				if args.do_filter_by_confidence:
								_lowerCAmelCase    :    str										=      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
								_lowerCAmelCase    :    Tuple										=      int(eval_result * len(UpperCamelCase_       )       )
								print(UpperCamelCase_       )
								_lowerCAmelCase    :    Union[str, Any]										=      dataset.sort("""probability"""      ,    reverse=UpperCamelCase_       )
								_lowerCAmelCase    :    Optional[int]										=      dataset.select(range(UpperCamelCase_       )       )

				_lowerCAmelCase    :    Optional[int]										=      dataset.remove_columns(["""label""", """probability"""]       )
				_lowerCAmelCase    :    Dict										=      dataset.rename_column("""prediction"""      ,    """label"""       )
				_lowerCAmelCase    :    List[Any]										=      dataset.map(lambda UpperCamelCase_       : {"label": idalabel[example["label"]]}       )
				_lowerCAmelCase    :    List[Any]										=      dataset.shuffle(seed=args.seed       )

				_lowerCAmelCase    :    int										=      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 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Any      ,    **UpperCamelCase_ : Optional[Any]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      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    :    List[str]										=      STModelArguments(model_name_or_path=UpperCamelCase_       )
				_lowerCAmelCase    :    Optional[int]										=      STDataArguments(train_file=UpperCamelCase_      ,    infer_file=UpperCamelCase_       )
				_lowerCAmelCase    :    str										=      STTrainingArguments(output_dir=UpperCamelCase_       )
				_lowerCAmelCase    :    List[str]										=      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
				_lowerCAmelCase    :    Dict										=      {}
				_lowerCAmelCase    :    Union[str, Any]										=      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    :    int										=      args.train_file
				_lowerCAmelCase    :    List[str]										=      args.infer_file

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

				for key in data_files:
								_lowerCAmelCase    :    List[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:
												_lowerCAmelCase    :    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..."""       )
				_lowerCAmelCase    :    Tuple										=      F"{args.output_dir}/self-train_iter-{{}}".format
				_lowerCAmelCase    :    str										=      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()

				_lowerCAmelCase    :    Any										=      None
				_lowerCAmelCase    :    List[Any]										=      None
				_lowerCAmelCase    :    Tuple										=      0
				_lowerCAmelCase    :    List[str]										=      False
				# Show the progress bar
				_lowerCAmelCase    :    str										=      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    :    str										=      data_dir_format(UpperCamelCase_       )
								assert os.path.exists(UpperCamelCase_       )

								# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
								# iteration > 0
								_lowerCAmelCase    :    Dict										=      os.path.join(UpperCamelCase_      ,    """stage-1"""       )
								_lowerCAmelCase    :    Union[str, Any]										=      {
								    """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}       )

								_lowerCAmelCase    :    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: 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
												_lowerCAmelCase    :    Any										=      os.path.join(UpperCamelCase_      ,    """best-checkpoint"""       )
												_lowerCAmelCase    :    List[Any]										=      os.path.join(UpperCamelCase_      ,    """stage-2"""       )
												# Update arguments_dict
												_lowerCAmelCase    :    List[Any]										=      model_path
												_lowerCAmelCase    :    Optional[int]										=      data_files["""train"""]
												_lowerCAmelCase    :    List[str]										=      current_output_dir

												_lowerCAmelCase    :    Dict										=      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_       )

								_lowerCAmelCase    :    int										=      iteration
								_lowerCAmelCase    :    List[Any]										=      data_dir_format(iteration + 1       )

								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(os.path.join(UpperCamelCase_      ,    """best-checkpoint"""       )       )
								_lowerCAmelCase    :    Optional[Any]										=      config.idalabel
								_lowerCAmelCase    :    Union[str, Any]										=      os.path.join(UpperCamelCase_      ,    """eval_results_best-checkpoint.json"""       )
								_lowerCAmelCase    :    List[Any]										=      os.path.join(UpperCamelCase_      ,    """test_results_best-checkpoint.json"""       )
								assert os.path.exists(UpperCamelCase_       )

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

								_lowerCAmelCase    :    Union[str, Any]										=      os.path.join(UpperCamelCase_      ,    F"train_pseudo.{args.data_file_extension}"       )

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

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

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				_lowerCAmelCase    :    Any										=      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"""       )      ,    )







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      [int(UpperCamelCase_       ) for i in ip_va_address.split("""."""       ) if i.isdigit()]
				return len(UpperCamelCase_       ) == 4 and all(0 <= int(UpperCamelCase_       ) <= 254 for octet in octets       )


if __name__ == "__main__":
	_lowerCamelCase      :  List[str]      						=						input().strip()
	_lowerCamelCase      :  int      						=						"valid" if is_ip_va_address_valid(ip) else "invalid"
	print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')

