
__lowerCamelCase							=       """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
   if not is_onnx_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_onnx_objects import *  # noqa F403
else:
   from .pipelines import OnnxRuntimeModel

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_pt_objects import *  # noqa F403
else:
   from .models import (
       AutoencoderKL,
       ControlNetModel,
       ModelMixin,
       PriorTransformer,
       TaFilmDecoder,
       TransformeraDModel,
       UNetaDModel,
       UNetaDConditionModel,
       UNetaDModel,
       UNetaDConditionModel,
       VQModel,
   )
   from .optimization import (
       get_constant_schedule,
       get_constant_schedule_with_warmup,
       get_cosine_schedule_with_warmup,
       get_cosine_with_hard_restarts_schedule_with_warmup,
       get_linear_schedule_with_warmup,
       get_polynomial_decay_schedule_with_warmup,
       get_scheduler,
   )
   from .pipelines import (
       AudioPipelineOutput,
       ConsistencyModelPipeline,
       DanceDiffusionPipeline,
       DDIMPipeline,
       DDPMPipeline,
       DiffusionPipeline,
       DiTPipeline,
       ImagePipelineOutput,
       KarrasVePipeline,
       LDMPipeline,
       LDMSuperResolutionPipeline,
       PNDMPipeline,
       RePaintPipeline,
       ScoreSdeVePipeline,
   )
   from .schedulers import (
       CMStochasticIterativeScheduler,
       DDIMInverseScheduler,
       DDIMParallelScheduler,
       DDIMScheduler,
       DDPMParallelScheduler,
       DDPMScheduler,
       DEISMultistepScheduler,
       DPMSolverMultistepInverseScheduler,
       DPMSolverMultistepScheduler,
       DPMSolverSinglestepScheduler,
       EulerAncestralDiscreteScheduler,
       EulerDiscreteScheduler,
       HeunDiscreteScheduler,
       IPNDMScheduler,
       KarrasVeScheduler,
       KDPMaAncestralDiscreteScheduler,
       KDPMaDiscreteScheduler,
       PNDMScheduler,
       RePaintScheduler,
       SchedulerMixin,
       ScoreSdeVeScheduler,
       UnCLIPScheduler,
       UniPCMultistepScheduler,
       VQDiffusionScheduler,
   )
   from .training_utils import EMAModel

try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .schedulers import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .schedulers import DPMSolverSDEScheduler

try:
   if not (is_torch_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .pipelines import (
       AltDiffusionImgaImgPipeline,
       AltDiffusionPipeline,
       AudioLDMPipeline,
       CycleDiffusionPipeline,
       IFImgaImgPipeline,
       IFImgaImgSuperResolutionPipeline,
       IFInpaintingPipeline,
       IFInpaintingSuperResolutionPipeline,
       IFPipeline,
       IFSuperResolutionPipeline,
       ImageTextPipelineOutput,
       KandinskyImgaImgPipeline,
       KandinskyInpaintPipeline,
       KandinskyPipeline,
       KandinskyPriorPipeline,
       KandinskyVaaControlnetImgaImgPipeline,
       KandinskyVaaControlnetPipeline,
       KandinskyVaaImgaImgPipeline,
       KandinskyVaaInpaintPipeline,
       KandinskyVaaPipeline,
       KandinskyVaaPriorEmbaEmbPipeline,
       KandinskyVaaPriorPipeline,
       LDMTextToImagePipeline,
       PaintByExamplePipeline,
       SemanticStableDiffusionPipeline,
       ShapEImgaImgPipeline,
       ShapEPipeline,
       StableDiffusionAttendAndExcitePipeline,
       StableDiffusionControlNetImgaImgPipeline,
       StableDiffusionControlNetInpaintPipeline,
       StableDiffusionControlNetPipeline,
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionImageVariationPipeline,
       StableDiffusionImgaImgPipeline,
       StableDiffusionInpaintPipeline,
       StableDiffusionInpaintPipelineLegacy,
       StableDiffusionInstructPixaPixPipeline,
       StableDiffusionLatentUpscalePipeline,
       StableDiffusionLDMaDPipeline,
       StableDiffusionModelEditingPipeline,
       StableDiffusionPanoramaPipeline,
       StableDiffusionParadigmsPipeline,
       StableDiffusionPipeline,
       StableDiffusionPipelineSafe,
       StableDiffusionPixaPixZeroPipeline,
       StableDiffusionSAGPipeline,
       StableDiffusionUpscalePipeline,
       StableUnCLIPImgaImgPipeline,
       StableUnCLIPPipeline,
       TextToVideoSDPipeline,
       TextToVideoZeroPipeline,
       UnCLIPImageVariationPipeline,
       UnCLIPPipeline,
       UniDiffuserModel,
       UniDiffuserPipeline,
       UniDiffuserTextDecoder,
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
       VideoToVideoSDPipeline,
       VQDiffusionPipeline,
   )

try:
   if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
   from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
   if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .pipelines import StableDiffusionKDiffusionPipeline

try:
   if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
   from .pipelines import (
       OnnxStableDiffusionImgaImgPipeline,
       OnnxStableDiffusionInpaintPipeline,
       OnnxStableDiffusionInpaintPipelineLegacy,
       OnnxStableDiffusionPipeline,
       OnnxStableDiffusionUpscalePipeline,
       StableDiffusionOnnxPipeline,
   )

try:
   if not (is_torch_available() and is_librosa_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
   from .pipelines import AudioDiffusionPipeline, Mel

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .pipelines import SpectrogramDiffusionPipeline

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_flax_objects import *  # noqa F403
else:
   from .models.controlnet_flax import FlaxControlNetModel
   from .models.modeling_flax_utils import FlaxModelMixin
   from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
   from .models.vae_flax import FlaxAutoencoderKL
   from .pipelines import FlaxDiffusionPipeline
   from .schedulers import (
       FlaxDDIMScheduler,
       FlaxDDPMScheduler,
       FlaxDPMSolverMultistepScheduler,
       FlaxKarrasVeScheduler,
       FlaxLMSDiscreteScheduler,
       FlaxPNDMScheduler,
       FlaxSchedulerMixin,
       FlaxScoreSdeVeScheduler,
   )


try:
   if not (is_flax_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
   from .pipelines import (
       FlaxStableDiffusionControlNetPipeline,
       FlaxStableDiffusionImgaImgPipeline,
       FlaxStableDiffusionInpaintPipeline,
       FlaxStableDiffusionPipeline,
   )

try:
   if not (is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from .utils.dummy_note_seq_objects import *  # noqa F403
else:
   from .pipelines import MidiProcessor







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							=       logging.getLogger(__name__)

__lowerCamelCase							=       """pytorch_model.bin"""






@dataclasses.dataclass
class   UpperCAmelCase		:
   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   UpperCAmelCase		:
   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   UpperCAmelCase		:
   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=1_00       ,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 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       snake_case       :   Tuple							=      datasets.concatenate_datasets([infer_input, infer_output]				,					axis=1    )

       if args.do_filter_by_confidence:
              snake_case       :   Optional[int]							=      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
              snake_case       :   int							=      int(eval_result * len(__lowerCamelCase    )    )
              print(__lowerCamelCase    )
              snake_case       :   List[str]							=      dataset.sort("probability"				,					reverse=__lowerCamelCase    )
              snake_case       :   Tuple							=      dataset.select(range(__lowerCamelCase    )    )

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

       snake_case       :   int							=      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]    ):
       snake_case       :   int							=      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       :   Dict							=      STModelArguments(model_name_or_path=__lowerCamelCase    )
       snake_case       :   Tuple							=      STDataArguments(train_file=__lowerCamelCase				,					infer_file=__lowerCamelCase    )
       snake_case       :   str							=      STTrainingArguments(output_dir=__lowerCamelCase    )
       snake_case       :   int							=      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
       snake_case       :   List[str]							=      {}
       snake_case       :   Optional[int]							=      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       :   str							=      args.train_file
       snake_case       :   Tuple							=      args.infer_file

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

       for key in data_files:
              snake_case       :   List[Any]							=      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       :   Union[str, 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..."    )
       snake_case       :   List[Any]							=      f"""{args.output_dir}/self-train_iter-{{}}""".format
       snake_case       :   Optional[int]							=      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()

       snake_case       :   Dict							=      None
       snake_case       :   Union[str, Any]							=      None
       snake_case       :   Tuple							=      0
       snake_case       :   List[Any]							=      False
       # Show the progress bar
       snake_case       :   List[Any]							=      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       :   str							=      data_dir_format(__lowerCamelCase    )
              assert os.path.exists(__lowerCamelCase    )

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

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

                     snake_case       :   Union[str, Any]							=      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    )

              snake_case       :   int							=      iteration
              snake_case       :   Tuple							=      data_dir_format(iteration + 1    )

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

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

              snake_case       :   str							=      os.path.join(__lowerCamelCase				,					f"""train_pseudo.{args.data_file_extension}"""    )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     snake_case       :   List[Any]							=      eval_result

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

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   snake_case       :   Tuple							=      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"    )				,					)





