





'''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


__magic_name__   :						Tuple    =			logging.getLogger(__name__)

__magic_name__   :						List[str]    =			"""pytorch_model.bin"""


@dataclasses.dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






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







@dataclasses.dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






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







@dataclasses.dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






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







def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  if args.do_filter_by_confidence:
    _snake_case     		=				dataset.filter(lambda SCREAMING_SNAKE_CASE__       : example["probability"] > args.confidence_threshold       )

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    _snake_case     		=				int(eval_result * len(SCREAMING_SNAKE_CASE__       )       )
    print(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				dataset.sort("probability"					,	reverse=SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				dataset.select(range(SCREAMING_SNAKE_CASE__       )       )

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				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     		=				STModelArguments(model_name_or_path=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				STDataArguments(train_file=SCREAMING_SNAKE_CASE__					,	infer_file=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				argparse.Namespace()

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

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

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

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

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

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

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

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

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

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

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

    _snake_case     		=				iteration
    _snake_case     		=				data_dir_format(iteration + 1       )

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

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

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

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

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

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

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









'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


