



'''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__    :   List[Any]           =					logging.getLogger(__name__)

A__    :   Union[str, Any]           =					'''pytorch_model.bin'''



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



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



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





def   a_   (		_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      Tuple ,_UpperCAmelCase    :      Any ,_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      Any				)    ->  Any:
      __snake_case :   List[str]													=    datasets.concatenate_datasets([infer_input, infer_output] ,axis=1				)

      if args.do_filter_by_confidence:
            __snake_case :   Union[str, 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
            __snake_case :   List[Any]													=    int(eval_result * len(_UpperCAmelCase				)				)
            print(_UpperCAmelCase				)
            __snake_case :   Optional[Any]													=    dataset.sort('probability' ,reverse=_UpperCAmelCase				)
            __snake_case :   List[Any]													=    dataset.select(range(_UpperCAmelCase				)				)

      __snake_case :   int													=    dataset.remove_columns(['label', 'probability']				)
      __snake_case :   int													=    dataset.rename_column('prediction' ,'label'				)
      __snake_case :   Dict													=    dataset.map(lambda _UpperCAmelCase				: {"label": idalabel[example["label"]]}				)
      __snake_case :   Optional[Any]													=    dataset.shuffle(seed=args.seed				)

      __snake_case :   Optional[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   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      int ,**_UpperCAmelCase    :      Union[str, Any]				)    ->  Optional[int]:
      __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 :   List[Any]													=    STModelArguments(model_name_or_path=_UpperCAmelCase				)
      __snake_case :   Tuple													=    STDataArguments(train_file=_UpperCAmelCase ,infer_file=_UpperCAmelCase				)
      __snake_case :   List[Any]													=    STTrainingArguments(output_dir=_UpperCAmelCase				)
      __snake_case :   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
      __snake_case :   Any													=    {}
      __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 :   List[Any]													=    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 :   Dict													=    args.eval_file

      for key in data_files:
            __snake_case :   Dict													=    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 :   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 :   Dict													=    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()

      __snake_case :   str													=    None
      __snake_case :   Any													=    None
      __snake_case :   int													=    0
      __snake_case :   Union[str, Any]													=    False
      # Show the progress bar
      __snake_case :   Union[str, 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 :   Dict													=    data_dir_format(_UpperCAmelCase				)
            assert os.path.exists(_UpperCAmelCase				)

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            __snake_case :   List[str]													=    os.path.join(_UpperCAmelCase ,'stage-1'				)
            __snake_case :   Union[str, 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(_UpperCAmelCase ,_UpperCAmelCase				):
                        arguments_dict.update({key: value}				)

            __snake_case :   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: 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
                  __snake_case :   Union[str, Any]													=    os.path.join(_UpperCAmelCase ,'best-checkpoint'				)
                  __snake_case :   Optional[int]													=    os.path.join(_UpperCAmelCase ,'stage-2'				)
                  # Update arguments_dict
                  __snake_case :   Tuple													=    model_path
                  __snake_case :   Optional[Any]													=    data_files['train']
                  __snake_case :   str													=    current_output_dir

                  __snake_case :   Dict													=    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				)

            __snake_case :   Dict													=    iteration
            __snake_case :   int													=    data_dir_format(iteration + 1				)

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

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

            __snake_case :   Optional[Any]													=    os.path.join(_UpperCAmelCase ,f'''train_pseudo.{args.data_file_extension}'''				)

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  __snake_case :   Optional[int]													=    eval_result

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

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              __snake_case :   int													=    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'				) ,)








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__    :   Optional[Any]           =					logging.get_logger(__name__)

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



