
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    for param, grad_param in zip(model_a.parameters()			,		model_b.parameters()		):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad			,		grad_param.grad		) is False
            ), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad			,		grad_param.grad		) is True
            ), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__=True		)							->							List[Any]:
    """simple docstring"""

    model.train()
    snake_case								= model(A__		)
    snake_case								= F.mse_loss(A__			,		target.to(output.device		)		)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(A__		)



def 						lowercase_      (       A__			,		A__=False		)							->							List[str]:
    """simple docstring"""

    set_seed(42		)
    snake_case								= RegressionModel()
    snake_case								= deepcopy(A__		)
    snake_case								= RegressionDataset(length=80		)
    snake_case								= DataLoader(A__			,		batch_size=16		)
    model.to(accelerator.device		)
    if sched:
        snake_case								= AdamW(params=model.parameters()			,		lr=1e-3		)
        snake_case								= AdamW(params=ddp_model.parameters()			,		lr=1e-3		)
        snake_case								= LambdaLR(A__			,		lr_lambda=lambda A__		: epoch**0.65		)
        snake_case								= LambdaLR(A__			,		lr_lambda=lambda A__		: epoch**0.65		)
    # Make a copy of `model`
    if sched:
        snake_case					,							snake_case					,							snake_case					,							snake_case								= accelerator.prepare(A__			,		A__			,		A__			,		A__		)
    else:
        snake_case					,							snake_case								= accelerator.prepare(A__			,		A__		)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader



def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    snake_case					,							snake_case					,							snake_case								= get_training_setup(A__		)
    # Use a single batch
    snake_case					,							snake_case								= next(iter(A__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        snake_case					,							snake_case								= accelerator.gather((ddp_input, ddp_target)		)
        snake_case					,							snake_case								= input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(A__			,		A__			,		A__			,		A__		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(A__		):
                step_model(A__			,		A__			,		A__			,		A__		)
        else:
            # Sync grads
            step_model(A__			,		A__			,		A__			,		A__		)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(A__			,		A__			,		A__			,		A__		)
        for param, ddp_param in zip(model.parameters()			,		ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad			,		ddp_param.grad		), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration		)
        snake_case								= ddp_input[torch.randperm(len(A__		)		)]



def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    snake_case					,							snake_case					,							snake_case								= get_training_setup(A__		)
    # Use a single batch
    snake_case					,							snake_case								= next(iter(A__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        snake_case					,							snake_case								= accelerator.gather((ddp_input, ddp_target)		)
        snake_case					,							snake_case								= input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(A__			,		A__			,		A__			,		A__		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(A__		):
                step_model(A__			,		A__			,		A__			,		A__		)
        else:
            # Sync grads
            step_model(A__			,		A__			,		A__			,		A__		)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()			,		ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad			,		ddp_param.grad		) is False
                ), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad			,		ddp_param.grad		) is True
                ), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration		)
        snake_case								= ddp_input[torch.randperm(len(A__		)		)]



def 						lowercase_      (       A__=False			,		A__=False		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= Accelerator(
        split_batches=A__			,		dispatch_batches=A__			,		gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    snake_case					,							snake_case					,							snake_case								= get_training_setup(A__		)
    for iteration, batch in enumerate(A__		):
        snake_case					,							snake_case								= batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case					,							snake_case								= accelerator.gather((ddp_input, ddp_target)		)
        snake_case					,							snake_case								= input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(A__			,		A__			,		A__			,		A__			,		A__		)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(A__		):
            step_model(A__			,		A__			,		A__			,		A__		)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()			,		ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(A__		) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad			,		ddp_param.grad		) is True
                ), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad			,		ddp_param.grad		) is False
                ), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration		)
        snake_case								= ddp_input[torch.randperm(len(A__		)		)]
    GradientState._reset_state()



def 						lowercase_      (       A__=False			,		A__=False		)							->							str:
    """simple docstring"""

    snake_case								= Accelerator(
        split_batches=A__			,		dispatch_batches=A__			,		gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case								= get_training_setup(A__			,		A__		)
    for iteration, batch in enumerate(A__		):
        snake_case					,							snake_case								= batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case					,							snake_case								= accelerator.gather((ddp_input, ddp_target)		)
        snake_case					,							snake_case								= input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(A__			,		A__			,		A__			,		A__			,		A__		)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A__		)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes		):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(A__		):
            step_model(A__			,		A__			,		A__			,		A__		)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
        snake_case								= (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A__		))
        if accelerator.num_processes > 1:
            check_model_parameters(A__			,		A__			,		A__			,		A__		)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration		)
    GradientState._reset_state()



def 						lowercase_      (       )							->							int:
    """simple docstring"""

    snake_case								= Accelerator()

    snake_case								= RegressionDataset(length=80		)
    snake_case								= DataLoader(A__			,		batch_size=16		)
    snake_case								= RegressionDataset(length=96		)
    snake_case								= DataLoader(A__			,		batch_size=16		)
    snake_case					,							snake_case								= accelerator.prepare(A__			,		A__		)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(A__		):
        assert id(accelerator.gradient_state.active_dataloader		) == id(A__		)
        if iteration < len(A__		) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(A__		):
                    assert id(accelerator.gradient_state.active_dataloader		) == id(A__		)
                    if batch_num < len(A__		) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None



def 						lowercase_      (       )							->							Any:
    """simple docstring"""

    snake_case								= Accelerator()
    snake_case								= accelerator.state
    if state.local_process_index == 0:
        print("**Test `accumulate` gradient accumulation with dataloader break**"		)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print("**Test NOOP `no_sync` context manager**"		)
        test_noop_sync(A__		)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print("**Test Distributed `no_sync` context manager**"		)
        test_distributed_sync(A__		)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        "**Test `accumulate` gradient accumulation, "			,		F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'			,		)
                test_gradient_accumulation(A__			,		A__		)

    # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version("<"			,		"2.0"		) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                "**Test `accumulate` gradient accumulation with optimizer and scheduler, "			,		"`split_batches=False`, `dispatch_batches=False`**"			,		)
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            "**Test `accumulate` gradient accumulation with optimizer and scheduler, "			,		F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'			,		)
                    test_gradient_accumulation_with_opt_and_scheduler(A__			,		A__		)



def 						lowercase_      (       A__		)							->							Union[str, Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()



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


_A   =			logging.getLogger(__name__)

_A   =			"pytorch_model.bin"







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







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







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



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

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

    if args.do_filter_by_confidence:
        snake_case								= dataset.filter(lambda A__		: 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(A__		)		)
        print(A__		)
        snake_case								= dataset.sort("probability"			,		reverse=A__		)
        snake_case								= dataset.select(range(A__		)		)

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

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





def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		**A__		)							->							List[Any]:
    """simple docstring"""

    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=A__		)
    snake_case								= STDataArguments(train_file=A__			,		infer_file=A__		)
    snake_case								= STTrainingArguments(output_dir=A__		)
    snake_case								= argparse.Namespace()

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

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

    # 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=A__		)
            os.makedirs(A__			,		exist_ok=A__		)
    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(A__		)
        assert os.path.exists(A__		)

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

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

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

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

        snake_case								= iteration
        snake_case								= data_dir_format(iteration + 1		)

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

        with open(A__			,		"r"		) as f:
            snake_case								= float(json.load(A__		)[args.eval_metric]		)
        snake_case								= os.path.join(A__			,		"infer_output_best-checkpoint.csv"		)
        assert os.path.exists(A__		)
        # 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(A__			,		exist_ok=A__		)
            shutil.copy(A__			,		os.path.join(A__			,		F'eval_results_iter-{iteration}.json'		)		)
            if os.path.exists(A__		):
                shutil.copy(A__			,		os.path.join(A__			,		F'test_results_iter-{iteration}.json'		)		)
            create_pseudo_labeled_data(A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
        accelerator.wait_for_everyone()

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

