





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers





def       _UpperCamelCase       (a__				:int    ):






							"""simple docstring"""





							random.seed(a__    )
							np.random.seed(a__    )
							torch.manual_seed(a__    )
							torch.cuda.manual_seed_all(a__    )
							# ^^ safe to call this function even if cuda is not available

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = 0.9999					,	__lowerCAmelCase = 0.0					,	__lowerCAmelCase = 0					,	__lowerCAmelCase = False					,	__lowerCAmelCase = 1.0					,	__lowerCAmelCase = 2 / 3					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	**__lowerCAmelCase					,	):

														if isinstance(__lowerCAmelCase					,	torch.nn.Module					):
																					UpperCamelCase__			       =     (
																					    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
																					    """Please pass the parameters of the module instead."""
																					)
																					deprecate(
																					    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""					,	"""1.0.0"""					,	__lowerCAmelCase					,	standard_warn=__lowerCAmelCase					,	)
																					UpperCamelCase__			       =     parameters.parameters()

																					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																					UpperCamelCase__			       =     True

														if kwargs.get("""max_value"""					,	__lowerCAmelCase					) is not None:
																					UpperCamelCase__			       =     """The `max_value` argument is deprecated. Please use `decay` instead."""
																					deprecate("""max_value"""					,	"""1.0.0"""					,	__lowerCAmelCase					,	standard_warn=__lowerCAmelCase					)
																					UpperCamelCase__			       =     kwargs["""max_value"""]

														if kwargs.get("""min_value"""					,	__lowerCAmelCase					) is not None:
																					UpperCamelCase__			       =     """The `min_value` argument is deprecated. Please use `min_decay` instead."""
																					deprecate("""min_value"""					,	"""1.0.0"""					,	__lowerCAmelCase					,	standard_warn=__lowerCAmelCase					)
																					UpperCamelCase__			       =     kwargs["""min_value"""]

														UpperCamelCase__			       =     list(__lowerCAmelCase					)
														UpperCamelCase__			       =     [p.clone().detach() for p in parameters]

														if kwargs.get("""device"""					,	__lowerCAmelCase					) is not None:
																					UpperCamelCase__			       =     """The `device` argument is deprecated. Please use `to` instead."""
																					deprecate("""device"""					,	"""1.0.0"""					,	__lowerCAmelCase					,	standard_warn=__lowerCAmelCase					)
																					self.to(device=kwargs["""device"""]					)

														UpperCamelCase__			       =     None

														UpperCamelCase__			       =     decay
														UpperCamelCase__			       =     min_decay
														UpperCamelCase__			       =     update_after_step
														UpperCamelCase__			       =     use_ema_warmup
														UpperCamelCase__			       =     inv_gamma
														UpperCamelCase__			       =     power
														UpperCamelCase__			       =     0
														UpperCamelCase__			       =     None  # set in `step()`

														UpperCamelCase__			       =     model_cls
														UpperCamelCase__			       =     model_config






							@classmethod
							def 			_lowerCamelCase						(  cls					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     model_cls.load_config(__lowerCAmelCase					,	return_unused_kwargs=__lowerCAmelCase					)
														UpperCamelCase__			       =     model_cls.from_pretrained(__lowerCAmelCase					)

														UpperCamelCase__			       =     cls(model.parameters()					,	model_cls=__lowerCAmelCase					,	model_config=model.config					)

														ema_model.load_state_dict(__lowerCAmelCase					)
														return ema_model






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														if self.model_cls is None:
																					raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""					)

														if self.model_config is None:
																					raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""					)

														UpperCamelCase__			       =     self.model_cls.from_config(self.model_config					)
														UpperCamelCase__			       =     self.state_dict()
														state_dict.pop("""shadow_params"""					,	__lowerCAmelCase					)

														model.register_to_config(**__lowerCAmelCase					)
														self.copy_to(model.parameters()					)
														model.save_pretrained(__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     max(0					,	optimization_step - self.update_after_step - 1					)

														if step <= 0:
																					return 0.0

														if self.use_ema_warmup:
																					UpperCamelCase__			       =     1 - (1 + step / self.inv_gamma) ** -self.power
														else:
																					UpperCamelCase__			       =     (1 + step) / (10 + step)

														UpperCamelCase__			       =     min(__lowerCAmelCase					,	self.decay					)
														# make sure decay is not smaller than min_decay
														UpperCamelCase__			       =     max(__lowerCAmelCase					,	self.min_decay					)
														return cur_decay_value






							@torch.no_grad()
							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														if isinstance(__lowerCAmelCase					,	torch.nn.Module					):
																					UpperCamelCase__			       =     (
																					    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
																					    """Please pass the parameters of the module instead."""
																					)
																					deprecate(
																					    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""					,	"""1.0.0"""					,	__lowerCAmelCase					,	standard_warn=__lowerCAmelCase					,	)
																					UpperCamelCase__			       =     parameters.parameters()

														UpperCamelCase__			       =     list(__lowerCAmelCase					)

														self.optimization_step += 1

														# Compute the decay factor for the exponential moving average.
														UpperCamelCase__			       =     self.get_decay(self.optimization_step					)
														UpperCamelCase__			       =     decay
														UpperCamelCase__			       =     1 - decay

														UpperCamelCase__			       =     contextlib.nullcontext
														if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																					import deepspeed

														for s_param, param in zip(self.shadow_params					,	__lowerCAmelCase					):
																					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																												UpperCamelCase__			       =     deepspeed.zero.GatheredParameters(__lowerCAmelCase					,	modifier_rank=__lowerCAmelCase					)

																					with context_manager():
																												if param.requires_grad:
																																			s_param.sub_(one_minus_decay * (s_param - param)					)
																												else:
																																			s_param.copy_(__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     list(__lowerCAmelCase					)
														for s_param, param in zip(self.shadow_params					,	__lowerCAmelCase					):
																					param.data.copy_(s_param.to(param.device					).data					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					):
														UpperCamelCase__			       =     [
														    p.to(device=__lowerCAmelCase					,	dtype=__lowerCAmelCase					) if p.is_floating_point() else p.to(device=__lowerCAmelCase					)
														    for p in self.shadow_params
														]






							def 			_lowerCamelCase						(  self					):
														return {
														    "decay": self.decay,
														    "min_decay": self.min_decay,
														    "optimization_step": self.optimization_step,
														    "update_after_step": self.update_after_step,
														    "use_ema_warmup": self.use_ema_warmup,
														    "inv_gamma": self.inv_gamma,
														    "power": self.power,
														    "shadow_params": self.shadow_params,
														}






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     [param.detach().cpu().clone() for param in parameters]






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														if self.temp_stored_params is None:
																					raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""					)
														for c_param, param in zip(self.temp_stored_params					,	__lowerCAmelCase					):
																					param.data.copy_(c_param.data					)

														# Better memory-wise.
														UpperCamelCase__			       =     None







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     copy.deepcopy(__lowerCAmelCase					)

														UpperCamelCase__			       =     state_dict.get("""decay"""					,	self.decay					)
														if self.decay < 0.0 or self.decay > 1.0:
																					raise ValueError("""Decay must be between 0 and 1"""					)

														UpperCamelCase__			       =     state_dict.get("""min_decay"""					,	self.min_decay					)
														if not isinstance(self.min_decay					,	__lowerCAmelCase					):
																					raise ValueError("""Invalid min_decay"""					)

														UpperCamelCase__			       =     state_dict.get("""optimization_step"""					,	self.optimization_step					)
														if not isinstance(self.optimization_step					,	__lowerCAmelCase					):
																					raise ValueError("""Invalid optimization_step"""					)

														UpperCamelCase__			       =     state_dict.get("""update_after_step"""					,	self.update_after_step					)
														if not isinstance(self.update_after_step					,	__lowerCAmelCase					):
																					raise ValueError("""Invalid update_after_step"""					)

														UpperCamelCase__			       =     state_dict.get("""use_ema_warmup"""					,	self.use_ema_warmup					)
														if not isinstance(self.use_ema_warmup					,	__lowerCAmelCase					):
																					raise ValueError("""Invalid use_ema_warmup"""					)

														UpperCamelCase__			       =     state_dict.get("""inv_gamma"""					,	self.inv_gamma					)
														if not isinstance(self.inv_gamma					,	(float, int)					):
																					raise ValueError("""Invalid inv_gamma"""					)

														UpperCamelCase__			       =     state_dict.get("""power"""					,	self.power					)
														if not isinstance(self.power					,	(float, int)					):
																					raise ValueError("""Invalid power"""					)

														UpperCamelCase__			       =     state_dict.get("""shadow_params"""					,	__lowerCAmelCase					)
														if shadow_params is not None:
																					UpperCamelCase__			       =     shadow_params
																					if not isinstance(self.shadow_params					,	__lowerCAmelCase					):
																												raise ValueError("""shadow_params must be a list"""					)
																					if not all(isinstance(__lowerCAmelCase					,	torch.Tensor					) for p in self.shadow_params					):
																												raise ValueError("""shadow_params must all be Tensors"""					)











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


UpperCamelCase__     =	logging.getLogger(__name__)

UpperCamelCase__     =	"pytorch_model.bin"

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






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






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






def       _UpperCamelCase       (a__				:List[str]				,     a__				:str				,     a__				:Any				,     a__				:Any				,     a__				:List[str]				,     a__				:Union[str, Any]    ):






							"""simple docstring"""





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

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

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

							UpperCamelCase__			       =     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       _UpperCamelCase       (a__				:Union[str, Any]				,     a__				:Any				,     a__				:Optional[int]				,     a__				:Union[str, Any]				,     **a__				:Union[str, Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     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()

							UpperCamelCase__			       =     STModelArguments(model_name_or_path=a__    )
							UpperCamelCase__			       =     STDataArguments(train_file=a__				,     infer_file=a__    )
							UpperCamelCase__			       =     STTrainingArguments(output_dir=a__    )
							UpperCamelCase__			       =     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
							UpperCamelCase__			       =     {}
							UpperCamelCase__			       =     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
							UpperCamelCase__			       =     args.train_file
							UpperCamelCase__			       =     args.infer_file

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

							for key in data_files:
														UpperCamelCase__			       =     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:
																					UpperCamelCase__			       =     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..."""    )
							UpperCamelCase__			       =     f"""{args.output_dir}/self-train_iter-{{}}""".format
							UpperCamelCase__			       =     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()

							UpperCamelCase__			       =     None
							UpperCamelCase__			       =     None
							UpperCamelCase__			       =     0
							UpperCamelCase__			       =     False
							# Show the progress bar
							UpperCamelCase__			       =     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    )    ):
														UpperCamelCase__			       =     data_dir_format(a__    )
														assert os.path.exists(a__    )

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														UpperCamelCase__			       =     os.path.join(a__				,     """stage-1"""    )
														UpperCamelCase__			       =     {
														    """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}    )

														UpperCamelCase__			       =     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
																					UpperCamelCase__			       =     os.path.join(a__				,     """best-checkpoint"""    )
																					UpperCamelCase__			       =     os.path.join(a__				,     """stage-2"""    )
																					# Update arguments_dict
																					UpperCamelCase__			       =     model_path
																					UpperCamelCase__			       =     data_files["""train"""]
																					UpperCamelCase__			       =     current_output_dir

																					UpperCamelCase__			       =     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__    )

														UpperCamelCase__			       =     iteration
														UpperCamelCase__			       =     data_dir_format(iteration + 1    )

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

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

														UpperCamelCase__			       =     os.path.join(a__				,     f"""train_pseudo.{args.data_file_extension}"""    )

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

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

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			UpperCamelCase__			       =     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"""    )				,     )




