


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      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."})
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(
         default=a     ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}     ,    )







@dataclasses.dataclass
class      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(metadata={"help": "A csv or a json file containing the training data."})
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."})
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(
         default=a     ,    metadata={"help": "A csv or a json file containing the validation data."})
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(
         default=a     ,    metadata={"help": "The name of the task to train on."}     ,    )
     __SCREAMING_SNAKE_CASE  			= dataclasses.field(
         default=a     ,    metadata={"help": "The list of labels for the task."})







@dataclasses.dataclass
class      lowerCAmelCase__       :




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




def 							_UpperCAmelCase				(UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  List[str]						):
       _A      :       Tuple				    = datasets.concatenate_datasets([infer_input, infer_output]				,							axis=1						)

       if args.do_filter_by_confidence:
              _A      :       List[Any]				    = dataset.filter(lambda UpperCamelCase__						: example["probability"] > args.confidence_threshold						)

       if args.do_filter_by_val_performance:
              assert eval_result >= 0.0 and eval_result <= 1.0
              _A      :       str				    = int(eval_result * len(UpperCamelCase__						)						)
              print(UpperCamelCase__						)
              _A      :       Optional[Any]				    = dataset.sort("probability"				,							reverse=UpperCamelCase__						)
              _A      :       Dict				    = dataset.select(range(UpperCamelCase__						)						)

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

       _A      :       List[Any]				    = os.path.join(UpperCamelCase__				,							f"train_pseudo.{args.data_file_extension}"						)
       if args.data_file_extension == "csv":
              dataset.to_csv(UpperCamelCase__				,							index=UpperCamelCase__						)
       else:
              dataset.to_json(UpperCamelCase__						)






def 							_UpperCAmelCase				(UpperCamelCase__     :  Any				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Any				,							**UpperCamelCase__     :  str						):
       _A      :       Any				    = Accelerator()
       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,							datefmt="%m/%d/%Y %H:%M:%S"				,							level=logging.INFO				,							)
       logger.info(accelerator.state						)

       # Setup logging, we only want one process per machine to log things on the
       # screen. accelerator.is_local_main_process is only True for one process per
       # machine.
       logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR						)

       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_info()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()

       _A      :       Optional[int]				    = STModelArguments(model_name_or_path=UpperCamelCase__						)
       _A      :       Optional[int]				    = STDataArguments(train_file=UpperCamelCase__				,							infer_file=UpperCamelCase__						)
       _A      :       Tuple				    = STTrainingArguments(output_dir=UpperCamelCase__						)
       _A      :       Optional[int]				    = argparse.Namespace()

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

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

    # Sanity checks
       _A      :       List[Any]				    = {}
       _A      :       List[Any]				    = None

       # You need to provide the training data and the data to predict on
       assert args.train_file is not None
       assert args.infer_file is not None
       _A      :       Dict				    = args.train_file
       _A      :       Any				    = args.infer_file

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

       for key in data_files:
              _A      :       int				    = data_files[key].split("."						)[-1]
              assert extension in ["csv", "json"], f"`{key}_file` should be a csv or a json file."
              if args.data_file_extension is None:
                     _A      :       List[str]				    = extension
              else:
                     assert extension == args.data_file_extension, f"`{key}_file` should be a {args.data_file_extension} file`."

       assert (
           args.eval_metric in datasets.list_metrics()
       ), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

       # If passed along, set the training seed now.
       if args.seed is not None:
              set_seed(args.seed						)

       logger.info("Creating the initial data directory for self-training..."						)
       _A      :       Union[str, Any]				    = f"{args.output_dir}/self-train_iter-{{}}".format
       _A      :       List[str]				    = data_dir_format(0						)

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

       _A      :       List[str]				    = None
       _A      :       int				    = None
       _A      :       str				    = 0
       _A      :       Tuple				    = False
       # Show the progress bar
       _A      :       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						)						):
              _A      :       Any				    = data_dir_format(UpperCamelCase__						)
              assert os.path.exists(UpperCamelCase__						)

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              _A      :       List[Any]				    = os.path.join(UpperCamelCase__				,							"stage-1"						)
              _A      :       Optional[int]				    = {
                  "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(UpperCamelCase__				,							UpperCamelCase__						):
                            arguments_dict.update({key: value}						)

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

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

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

              _A      :       int				    = iteration
              _A      :       List[str]				    = data_dir_format(iteration + 1						)

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

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

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

              _A      :       Optional[int]				    = os.path.join(UpperCamelCase__				,							f"train_pseudo.{args.data_file_extension}"						)

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     _A      :       Dict				    = eval_result

                     if best_iteration is None:
                            _A      :       List[str]				    = new_iteration
                            _A      :       str				    = new_eval_result
                     else:
                            if new_eval_result - best_eval_result > args.early_stopping_threshold:
                                   _A      :       Dict				    = new_iteration
                                   _A      :       int				    = new_eval_result
                                   _A      :       Optional[Any]				    = 0
                            else:
                                   if new_eval_result == best_eval_result:
                                          _A      :       Optional[Any]				    = new_iteration
                                          _A      :       Tuple				    = new_eval_result
                                   early_stopping_patience_counter += 1

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




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
