





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


UpperCamelCase__  			=logging.getLogger(__name__)

UpperCamelCase__  			='pytorch_model.bin'





@dataclasses.dataclass
class       lowerCAmelCase__:






       '''simple docstring'''


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





@dataclasses.dataclass
class       lowerCAmelCase__:






       '''simple docstring'''


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





@dataclasses.dataclass
class       lowerCAmelCase__:






       '''simple docstring'''


       __snake_case			      =     dataclasses.field(
           metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     )
       __snake_case			      =     dataclasses.field(
           default='accuracy'	,  metadata={'help': 'The evaluation metric used for the task.'}     )
       __snake_case			      =     dataclasses.field(
           default='no'	,  metadata={
               'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
           }	,  )
       __snake_case			      =     dataclasses.field(
           default=1_0	,  metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=0.0	,  metadata={
               'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
           }	,  )
       __snake_case			      =     dataclasses.field(
           default=lowerCAmelCase_	,  metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=lowerCAmelCase_	,  metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=lowerCAmelCase_	,  metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=0.0	,  metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=1_0_0	,  metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}	,  )
       __snake_case			      =     dataclasses.field(
           default=lowerCAmelCase_	,  metadata={'help': 'Random seed for initialization.'}	,  )






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Tuple         =				datasets.concatenate_datasets([infer_input, infer_output],							axis=1						)

     if args.do_filter_by_confidence:
          _SCREAMING_SNAKE_CASE							:						str         =				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
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				int(eval_result * len(__lowerCamelCase						)						)
          print(__lowerCamelCase						)
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				dataset.sort("probability",							reverse=__lowerCamelCase						)
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				dataset.select(range(__lowerCamelCase						)						)

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

     _SCREAMING_SNAKE_CASE							:						List[Any]         =				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      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							**__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						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()

     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				STModelArguments(model_name_or_path=__lowerCamelCase						)
     _SCREAMING_SNAKE_CASE							:						Any         =				STDataArguments(train_file=__lowerCamelCase,							infer_file=__lowerCamelCase						)
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				STTrainingArguments(output_dir=__lowerCamelCase						)
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				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
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				{}
     _SCREAMING_SNAKE_CASE							:						Union[str, 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
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				args.train_file
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				args.infer_file

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

     for key in data_files:
          _SCREAMING_SNAKE_CASE							:						List[str]         =				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:
               _SCREAMING_SNAKE_CASE							:						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..."						)
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				f"""{args.output_dir}/self-train_iter-{{}}""".format
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				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()

     _SCREAMING_SNAKE_CASE							:						Any         =				None
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				None
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				0
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				False
     # Show the progress bar
     _SCREAMING_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						)						):
          _SCREAMING_SNAKE_CASE							:						Tuple         =				data_dir_format(__lowerCamelCase						)
          assert os.path.exists(__lowerCamelCase						)

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

          _SCREAMING_SNAKE_CASE							:						List[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: 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
               _SCREAMING_SNAKE_CASE							:						Any         =				os.path.join(__lowerCamelCase,							"best-checkpoint"						)
               _SCREAMING_SNAKE_CASE							:						List[Any]         =				os.path.join(__lowerCamelCase,							"stage-2"						)
               # Update arguments_dict
               _SCREAMING_SNAKE_CASE							:						str         =				model_path
               _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				data_files["train"]
               _SCREAMING_SNAKE_CASE							:						Tuple         =				current_output_dir

               _SCREAMING_SNAKE_CASE							:						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						)

          _SCREAMING_SNAKE_CASE							:						int         =				iteration
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				data_dir_format(iteration + 1						)

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

          with open(__lowerCamelCase,							"r"						) as f:
               _SCREAMING_SNAKE_CASE							:						Any         =				float(json.load(__lowerCamelCase						)[args.eval_metric]						)
          _SCREAMING_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.
          _SCREAMING_SNAKE_CASE							:						List[str]         =				load_dataset(args.data_file_extension,							data_files={"data": data_files["infer"]}						)["data"]
          _SCREAMING_SNAKE_CASE							:						List[str]         =				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()

          _SCREAMING_SNAKE_CASE							:						List[str]         =				os.path.join(__lowerCamelCase,							f"""train_pseudo.{args.data_file_extension}"""						)

          if args.evaluation_strategy != IntervalStrategy.NO.value:
               _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				eval_result

               if best_iteration is None:
                    _SCREAMING_SNAKE_CASE							:						Dict         =				new_iteration
                    _SCREAMING_SNAKE_CASE							:						Dict         =				new_eval_result
               else:
                    if new_eval_result - best_eval_result > args.early_stopping_threshold:
                         _SCREAMING_SNAKE_CASE							:						Dict         =				new_iteration
                         _SCREAMING_SNAKE_CASE							:						Any         =				new_eval_result
                         _SCREAMING_SNAKE_CASE							:						Dict         =				0
                    else:
                         if new_eval_result == best_eval_result:
                              _SCREAMING_SNAKE_CASE							:						Dict         =				new_iteration
                              _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				new_eval_result
                         early_stopping_patience_counter += 1

                    if early_stopping_patience_counter >= args.early_stopping_patience:
                         _SCREAMING_SNAKE_CASE							:						Dict         =				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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				'timm_backbone'


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	Optional[int]=3 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	Optional[Any]=None ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   backbone
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   features_only
      _UpperCAmelCase    			=   use_pretrained_backbone
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   out_indices if out_indices is not None else (-1,)
