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


__snake_case							=      logging.getLogger(__name__)

__snake_case							=      'pytorch_model.bin'


@dataclasses.dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






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






@dataclasses.dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	int    		= dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}					)
  lowerCAmelCase__		:	Union[str, Any]    		= dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}					)
  lowerCAmelCase__		:	Tuple    		= dataclasses.field(
      default=lowerCamelCase__		,	metadata={'help': 'A csv or a json file containing the validation data.'}					)
  lowerCAmelCase__		:	int    		= dataclasses.field(
      default=lowerCamelCase__		,	metadata={'help': 'The name of the task to train on.'}		,	)
  lowerCAmelCase__		:	str    		= dataclasses.field(
      default=lowerCamelCase__		,	metadata={'help': 'The list of labels for the task.'}					)






@dataclasses.dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






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






def       __lowerCAmelCase      (						A_   : int						,	A_   : List[Any]						,	A_   : Any						,	A_   : Dict						,	A_   : int						,	A_   : Union[str, Any]					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       datasets.concatenate_datasets([infer_input, infer_output]						,	axis=1					)

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

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

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

  __UpperCAmelCase						    =       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      (						A_   : Dict						,	A_   : Any						,	A_   : Any						,	A_   : int						,	**A_   : Any					)    ->  str:
  __UpperCAmelCase						    =       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()

  __UpperCAmelCase						    =       STModelArguments(model_name_or_path=lowerCamelCase_					)
  __UpperCAmelCase						    =       STDataArguments(train_file=lowerCamelCase_						,	infer_file=lowerCamelCase_					)
  __UpperCAmelCase						    =       STTrainingArguments(output_dir=lowerCamelCase_					)
  __UpperCAmelCase						    =       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
  __UpperCAmelCase						    =       {}
  __UpperCAmelCase						    =       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
  __UpperCAmelCase						    =       args.train_file
  __UpperCAmelCase						    =       args.infer_file

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

  for key in data_files:
    __UpperCAmelCase						    =       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:
      __UpperCAmelCase						    =       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..."					)
  __UpperCAmelCase						    =       F'''{args.output_dir}/self-train_iter-{{}}'''.format
  __UpperCAmelCase						    =       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()

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

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

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

      __UpperCAmelCase						    =       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_					)

    __UpperCAmelCase						    =       iteration
    __UpperCAmelCase						    =       data_dir_format(iteration + 1					)

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

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

    __UpperCAmelCase						    =       os.path.join(lowerCamelCase_						,	F'''train_pseudo.{args.data_file_extension}'''					)

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

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

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




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



