





'''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


__lowerCAmelCase							:	List[Any]			=logging.getLogger(__name__)

__lowerCAmelCase							:	List[str]			="pytorch_model.bin"






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






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






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





def 						UpperCamelCase				(							_lowerCamelCase					:					int						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]       ):
    A__				=			datasets.concatenate_datasets([infer_input, infer_output]						,       axis=1       )

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

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

    A__				=			os.path.join(__lowerCamelCase						,       F"train_pseudo.{args.data_file_extension}"       )
    if args.data_file_extension == "csv":
        dataset.to_csv(__lowerCamelCase						,       index=__lowerCamelCase       )
    else:
        dataset.to_json(__lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Tuple						,       **_lowerCamelCase					:					List[Any]       ):
    A__				=			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__				=			STModelArguments(model_name_or_path=__lowerCamelCase       )
    A__				=			STDataArguments(train_file=__lowerCamelCase						,       infer_file=__lowerCamelCase       )
    A__				=			STTrainingArguments(output_dir=__lowerCamelCase       )
    A__				=			argparse.Namespace()

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

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

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

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

    for key in data_files:
        A__				=			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__				=			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__				=			F"{args.output_dir}/self-train_iter-{{}}".format
    A__				=			data_dir_format(0       )

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

    A__				=			None
    A__				=			None
    A__				=			0
    A__				=			False
    # Show the progress bar
    A__				=			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__				=			data_dir_format(__lowerCamelCase       )
        assert os.path.exists(__lowerCamelCase       )

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

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

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

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

        A__				=			iteration
        A__				=			data_dir_format(iteration + 1       )

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

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

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

        A__				=			os.path.join(__lowerCamelCase						,       F"train_pseudo.{args.data_file_extension}"       )

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

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

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





import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


                 return self._metrics



       monkeypatch.setattr("datasets.inspect.huggingface_hub"				,					HfhMock()    )




@pytest.mark.parametrize(
    "func, args"				,					[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]    )
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       if "tmp_path" in args:
              snake_case       :   str							=      tuple(arg if arg != "tmp_path" else tmp_path for arg in args    )
       with pytest.warns(__lowerCamelCase				,					match="https://huggingface.co/docs/evaluate"    ):
              func(*__lowerCamelCase    )





