



"""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


SCREAMING_SNAKE_CASE_:Tuple					=						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_:Tuple					=						"""pytorch_model.bin"""






@dataclasses.dataclass
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






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






@dataclasses.dataclass
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






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






@dataclasses.dataclass
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	List[str]										=       datasets.concatenate_datasets([infer_input, infer_output]				,					axis=1		)

    if args.do_filter_by_confidence:
        A			:	Optional[Any]										=       dataset.filter(lambda _lowerCAmelCase		: example["probability"] > args.confidence_threshold		)

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        A			:	Tuple										=       int(eval_result * len(_SCREAMING_SNAKE_CASE		)		)
        print(_SCREAMING_SNAKE_CASE		)
        A			:	Optional[Any]										=       dataset.sort("""probability"""				,					reverse=_SCREAMING_SNAKE_CASE		)
        A			:	Dict										=       dataset.select(range(_SCREAMING_SNAKE_CASE		)		)

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

    A			:	int										=       os.path.join(_SCREAMING_SNAKE_CASE				,					f'''train_pseudo.{args.data_file_extension}'''		)
    if args.data_file_extension == "csv":
        dataset.to_csv(_SCREAMING_SNAKE_CASE				,					index=_SCREAMING_SNAKE_CASE		)
    else:
        dataset.to_json(_SCREAMING_SNAKE_CASE		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					**_lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	Dict										=       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()

    A			:	Optional[Any]										=       STModelArguments(model_name_or_path=_SCREAMING_SNAKE_CASE		)
    A			:	Optional[int]										=       STDataArguments(train_file=_SCREAMING_SNAKE_CASE				,					infer_file=_SCREAMING_SNAKE_CASE		)
    A			:	Union[str, Any]										=       STTrainingArguments(output_dir=_SCREAMING_SNAKE_CASE		)
    A			:	str										=       argparse.Namespace()

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

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

    # Sanity checks
    A			:	str										=       {}
    A			:	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
    A			:	Tuple										=       args.train_file
    A			:	List[str]										=       args.infer_file

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

    for key in data_files:
        A			:	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:
            A			:	List[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..."""		)
    A			:	Optional[int]										=       f'''{args.output_dir}/self-train_iter-{{}}'''.format
    A			:	Any										=       data_dir_format(0		)

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

    A			:	int										=       None
    A			:	Any										=       None
    A			:	List[str]										=       0
    A			:	Optional[Any]										=       False
    # Show the progress bar
    A			:	Optional[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		)		):
        A			:	List[Any]										=       data_dir_format(_SCREAMING_SNAKE_CASE		)
        assert os.path.exists(_SCREAMING_SNAKE_CASE		)

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        A			:	str										=       os.path.join(_SCREAMING_SNAKE_CASE				,					"""stage-1"""		)
        A			:	Optional[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(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE		):
                arguments_dict.update({key: value}		)

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

        if iteration > 0 and args.finetune_on_labeled_data:
            # Stage 2 (optional): fine-tuning on the original labeled data
            A			:	Any										=       os.path.join(_SCREAMING_SNAKE_CASE				,					"""best-checkpoint"""		)
            A			:	List[str]										=       os.path.join(_SCREAMING_SNAKE_CASE				,					"""stage-2"""		)
            # Update arguments_dict
            A			:	Optional[int]										=       model_path
            A			:	List[str]										=       data_files["train"]
            A			:	str										=       current_output_dir

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

        A			:	int										=       iteration
        A			:	int										=       data_dir_format(iteration + 1		)

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

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

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

        A			:	Dict										=       os.path.join(_SCREAMING_SNAKE_CASE				,					f'''train_pseudo.{args.data_file_extension}'''		)

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

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

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







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


