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_    :		Union[str, Any]							=	logging.getLogger(__name__)

lowerCamelCase_    :		Optional[int]							=	"pytorch_model.bin"


@dataclasses.dataclass
class 						a__							:
   A__    :       List[Any]        =      dataclasses.field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}		)
   A__    :       Optional[int]        =      dataclasses.field(
       default=__snake_case				,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}				,						)






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






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






def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
     __a		 =       datasets.concatenate_datasets([infer_input, infer_output]				,    axis=1						)

     if args.do_filter_by_confidence:
          __a		 =       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
          __a		 =       int(eval_result * len(_snake_case						)						)
          print(_snake_case						)
          __a		 =       dataset.sort('probability'				,    reverse=_snake_case						)
          __a		 =       dataset.select(range(_snake_case						)						)

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

     __a		 =       os.path.join(_snake_case				,    f'''train_pseudo.{args.data_file_extension}'''						)
     if args.data_file_extension == "csv":
          dataset.to_csv(_snake_case				,    index=_snake_case						)
     else:
          dataset.to_json(_snake_case						)



def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    **__lowerCamelCase						):
     __a		 =       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		 =       STModelArguments(model_name_or_path=_snake_case						)
     __a		 =       STDataArguments(train_file=_snake_case				,    infer_file=_snake_case						)
     __a		 =       STTrainingArguments(output_dir=_snake_case						)
     __a		 =       argparse.Namespace()

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

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

    # Sanity checks
     __a		 =       {}
     __a		 =       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		 =       args.train_file
     __a		 =       args.infer_file

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

     for key in data_files:
          __a		 =       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		 =       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		 =       f'''{args.output_dir}/self-train_iter-{{}}'''.format
     __a		 =       data_dir_format(0						)

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

     __a		 =       None
     __a		 =       None
     __a		 =       0
     __a		 =       False
     # Show the progress bar
     __a		 =       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		 =       data_dir_format(_snake_case						)
          assert os.path.exists(_snake_case						)

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

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

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

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

          __a		 =       iteration
          __a		 =       data_dir_format(iteration + 1						)

          __a		 =       AutoConfig.from_pretrained(os.path.join(_snake_case				,    'best-checkpoint'						)						)
          __a		 =       config.idalabel
          __a		 =       os.path.join(_snake_case				,    'eval_results_best-checkpoint.json'						)
          __a		 =       os.path.join(_snake_case				,    'test_results_best-checkpoint.json'						)
          assert os.path.exists(_snake_case						)

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

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

          __a		 =       os.path.join(_snake_case				,    f'''train_pseudo.{args.data_file_extension}'''						)

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

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

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





from __future__ import annotations

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					return np.maximum(0				,    __lowerCamelCase						)


if __name__ == "__main__":
	print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]




