
import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__lowerCamelCase							=       """sshleifer/mar_enro_6_3_student"""






class   UpperCAmelCase		(						A_ ):



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


          '''simple docstring'''


          super().setUp()

          snake_case       :   List[str]							=      cached_path(
              "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"    ,						extract_compressed_file=snake_case__    ,						)
          snake_case       :   Union[str, Any]							=      f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""



   @slow
   @require_torch_gpu
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	List[str]:


          '''simple docstring'''


          MarianMTModel.from_pretrained(snake_case__ )



   @slow
   @require_torch_gpu
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Tuple							=      {
              "$MAX_LEN": 64,
              "$BS": 64,
              "$GAS": 1,
              "$ENRO_DIR": self.data_dir,
              "facebook/mbart-large-cc25": MARIAN_MODEL,
              # "val_check_interval=0.25": "val_check_interval=1.0",
              "--learning_rate=3e-5": "--learning_rate 3e-4",
              "--num_train_epochs 6": "--num_train_epochs 1",
          }

          # Clean up bash script
          snake_case       :   int							=      (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py" )[1].strip()
          snake_case       :   Optional[int]							=      bash_script.replace("\\\n"    ,						"" ).strip().replace("\"$@\""    ,						"" )
          for k, v in env_vars_to_replace.items():
                 snake_case       :   Optional[int]							=      bash_script.replace(snake_case__    ,						str(snake_case__ ) )
          snake_case       :   int							=      self.get_auto_remove_tmp_dir()

          # bash_script = bash_script.replace("--fp16 ", "")
          snake_case       :   List[str]							=      f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
          # XXX: args.gpus > 1 : handle multi_gpu in the future

          snake_case       :   Optional[Any]							=      ["finetune.py"] + bash_script.split() + args
          with patch.object(snake_case__    ,						"argv"    ,						snake_case__ ):
                 snake_case       :   Dict							=      argparse.ArgumentParser()
                 snake_case       :   List[str]							=      pl.Trainer.add_argparse_args(snake_case__ )
                 snake_case       :   Dict							=      SummarizationModule.add_model_specific_args(snake_case__    ,						os.getcwd() )
                 snake_case       :   Union[str, Any]							=      parser.parse_args()
                 snake_case       :   int							=      main(snake_case__ )

          # Check metrics
          snake_case       :   List[Any]							=      load_json(model.metrics_save_path )
          snake_case       :   Union[str, Any]							=      metrics["val"][0]
          snake_case       :   Dict							=      metrics["val"][-1]
          self.assertEqual(len(metrics["val"] )    ,						(args.max_epochs / args.val_check_interval) )
          assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]    ,						snake_case__ )

          self.assertGreater(last_step_stats["val_avg_gen_time"]    ,						0.01 )
          # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
          self.assertLessEqual(last_step_stats["val_avg_gen_time"]    ,						1.0 )

          # test learning requirements:

          # 1. BLEU improves over the course of training by more than 2 pts
          self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]    ,						2 )

          # 2. BLEU finishes above 17
          self.assertGreater(last_step_stats["val_avg_bleu"]    ,						17 )

          # 3. test BLEU and val BLEU within ~1.1 pt.
          self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"] )    ,						1.1 )

          # check lightning ckpt can be loaded and has a reasonable statedict
          snake_case       :   int							=      os.listdir(snake_case__ )
          snake_case       :   Any							=      [x for x in contents if x.endswith(".ckpt" )][0]
          snake_case       :   List[Any]							=      os.path.join(args.output_dir    ,						snake_case__ )
          snake_case       :   str							=      torch.load(snake_case__    ,						map_location="cpu" )
          snake_case       :   Any							=      "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
          assert expected_key in ckpt["state_dict"]
          assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

          # TODO: turn on args.do_predict when PL bug fixed.
          if args.do_predict:
                 snake_case       :   Any							=      {os.path.basename(snake_case__ ) for p in contents}
                 assert "test_generations.txt" in contents
                 assert "test_results.txt" in contents
                 # assert len(metrics["val"]) ==  desired_n_evals
                 assert len(metrics["test"] ) == 1






class   UpperCAmelCase		(						A_ ):



   @timeout_decorator.timeout(6_00 )
   @slow
   @require_torch_gpu
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Any:


          '''simple docstring'''


          snake_case       :   Any							=      f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
          snake_case       :   List[Any]							=      {
              "--fp16_opt_level=O1": "",
              "$MAX_LEN": 1_28,
              "$BS": 16,
              "$GAS": 1,
              "$ENRO_DIR": data_dir,
              "$m": "sshleifer/student_marian_en_ro_6_1",
              "val_check_interval=0.25": "val_check_interval=1.0",
          }

          # Clean up bash script
          snake_case       :   Dict							=      (
              (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py" )[1].strip()
          )
          snake_case       :   Union[str, Any]							=      bash_script.replace("\\\n"    ,						"" ).strip().replace("\"$@\""    ,						"" )
          snake_case       :   str							=      bash_script.replace("--fp16 "    ,						" " )

          for k, v in env_vars_to_replace.items():
                 snake_case       :   Union[str, Any]							=      bash_script.replace(snake_case__    ,						str(snake_case__ ) )
          snake_case       :   List[str]							=      self.get_auto_remove_tmp_dir()
          snake_case       :   Any							=      bash_script.replace("--fp16"    ,						"" )
          snake_case       :   Tuple							=      6
          snake_case       :   str							=      (
              ["distillation.py"]
              + bash_script.split()
              + [
                  f"""--output_dir={output_dir}""",
                  "--gpus=1",
                  "--learning_rate=1e-3",
                  f"""--num_train_epochs={epochs}""",
                  "--warmup_steps=10",
                  "--val_check_interval=1.0",
                  "--do_predict",
              ]
          )
          with patch.object(snake_case__    ,						"argv"    ,						snake_case__ ):
                 snake_case       :   List[str]							=      argparse.ArgumentParser()
                 snake_case       :   int							=      pl.Trainer.add_argparse_args(snake_case__ )
                 snake_case       :   Tuple							=      SummarizationDistiller.add_model_specific_args(snake_case__    ,						os.getcwd() )
                 snake_case       :   List[Any]							=      parser.parse_args()
                 # assert args.gpus == gpus THIS BREAKS for multi_gpu

                 snake_case       :   Dict							=      distill_main(snake_case__ )

          # Check metrics
          snake_case       :   Tuple							=      load_json(model.metrics_save_path )
          snake_case       :   Optional[int]							=      metrics["val"][0]
          snake_case       :   int							=      metrics["val"][-1]
          assert len(metrics["val"] ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

          assert last_step_stats["val_avg_gen_time"] >= 0.01

          assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
          assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
          assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]    ,						snake_case__ )

          # check lightning ckpt can be loaded and has a reasonable statedict
          snake_case       :   int							=      os.listdir(snake_case__ )
          snake_case       :   str							=      [x for x in contents if x.endswith(".ckpt" )][0]
          snake_case       :   List[Any]							=      os.path.join(args.output_dir    ,						snake_case__ )
          snake_case       :   str							=      torch.load(snake_case__    ,						map_location="cpu" )
          snake_case       :   Dict							=      "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
          assert expected_key in ckpt["state_dict"]
          assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

          # TODO: turn on args.do_predict when PL bug fixed.
          if args.do_predict:
                 snake_case       :   Dict							=      {os.path.basename(snake_case__ ) for p in contents}
                 assert "test_generations.txt" in contents
                 assert "test_results.txt" in contents
                 # assert len(metrics["val"]) ==  desired_n_evals
                 assert len(metrics["test"] ) == 1







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"    )				,					)





