





'''simple docstring'''






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


A__    :       Dict              =logging.getLogger(__name__)

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




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




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




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




def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ) ->    Union[str, Any]:





      """simple docstring"""


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

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

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

      _lowerCAmelCase												=							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							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     **lowerCAmelCase  ) ->    List[str]:





      """simple docstring"""


      _lowerCAmelCase												=							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()

      _lowerCAmelCase												=							STModelArguments(model_name_or_path=__lowerCAmelCase  )
      _lowerCAmelCase												=							STDataArguments(train_file=__lowerCAmelCase							,     infer_file=__lowerCAmelCase  )
      _lowerCAmelCase												=							STTrainingArguments(output_dir=__lowerCAmelCase  )
      _lowerCAmelCase												=							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
      _lowerCAmelCase												=							{}
      _lowerCAmelCase												=							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
      _lowerCAmelCase												=							args.train_file
      _lowerCAmelCase												=							args.infer_file

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

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

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

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

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

                  _lowerCAmelCase												=							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  )

            _lowerCAmelCase												=							iteration
            _lowerCAmelCase												=							data_dir_format(iteration + 1  )

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

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

            _lowerCAmelCase												=							os.path.join(__lowerCAmelCase							,     f"train_pseudo.{args.data_file_extension}"  )

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

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

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













'''simple docstring'''






from manim import *




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				str:
            _lowerCAmelCase												=							Rectangle(height=0.5      ,      width=0.5  )
            _lowerCAmelCase												=							Rectangle(height=0.46      ,      width=0.46  ).set_stroke(width=0  )

            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""CPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            cpu.move_to([-2.5, -0.5, 0]  )
            self.add(__snake_case  )

            _lowerCAmelCase												=							[mem.copy() for i in range(4  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""GPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            gpu.move_to([-1, -1, 0]  )
            self.add(__snake_case  )

            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )

            _lowerCAmelCase												=							Text("""Model"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0.5      ,      aligned_edge=__snake_case  )
            model.move_to([3, -1.0, 0]  )
            self.add(__snake_case  )

            _lowerCAmelCase												=							[]
            for i, rect in enumerate(__snake_case  ):
                  rect.set_stroke(__snake_case  )
                  # target = fill.copy().set_fill(YELLOW, opacity=0.7)
                  # target.move_to(rect)
                  # self.add(target)

                  _lowerCAmelCase												=							Rectangle(height=0.46 / 4      ,      width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__snake_case      ,      opacity=0.7  )

                  if i == 0:
                        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  )      ,      buff=0.02      ,      direction=__snake_case  )
                        cpu_target.set_x(cpu_target.get_x() + 0.1  )
                  elif i == 3:
                        cpu_target.next_to(cpu_targs[0]      ,      direction=__snake_case      ,      buff=0.0  )
                  else:
                        cpu_target.next_to(cpu_targs[i - 1]      ,      direction=__snake_case      ,      buff=0.0  )
                  self.add(__snake_case  )
                  cpu_targs.append(__snake_case  )

            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )

            _lowerCAmelCase												=							Text("""Loaded Checkpoint"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      aligned_edge=__snake_case      ,      buff=0.4  )
            checkpoint.move_to([3, 0.5, 0]  )

            _lowerCAmelCase												=							Square(side_length=2.2  )
            key.move_to([-5, 2, 0]  )

            _lowerCAmelCase												=							MarkupText(
                f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"      ,      font_size=18      ,      )

            key_text.move_to([-5, 2.4, 0]  )

            self.add(__snake_case      ,      __snake_case  )

            _lowerCAmelCase												=							MarkupText(
                f"<span fgcolor='{BLUE}'>●</span> Checkpoint"      ,      font_size=18      ,      )

            blue_text.next_to(__snake_case      ,      DOWN * 2.4      ,      aligned_edge=key_text.get_left()  )

            _lowerCAmelCase												=							MarkupText(
                f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."      ,      font_size=24      ,      )
            step_a.move_to([2, 2, 0]  )
            self.play(Write(__snake_case  )      ,      Write(__snake_case  )  )

            self.play(Write(__snake_case      ,      run_time=1  )      ,      Create(__snake_case      ,      run_time=1  )  )

            _lowerCAmelCase												=							[]
            _lowerCAmelCase												=							[]
            for i, rect in enumerate(__snake_case  ):
                  _lowerCAmelCase												=							fill.copy().set_fill(__snake_case      ,      opacity=0.7  )
                  target.move_to(__snake_case  )
                  first_animations.append(GrowFromCenter(__snake_case      ,      run_time=1  )  )

                  _lowerCAmelCase												=							target.copy()
                  cpu_target.generate_target()
                  if i < 5:
                        cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
                  else:
                        cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
                  second_animations.append(MoveToTarget(__snake_case      ,      run_time=1.5  )  )

            self.play(*__snake_case  )
            self.play(*__snake_case  )
            self.wait()






