


"""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


__snake_case         =					logging.getLogger(__name__)

__snake_case         =					"""pytorch_model.bin"""







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







@dataclasses.dataclass
class _lowerCAmelCase       :
   __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				: Tuple													=						dataclasses.field(
       default=snake_case_					,							metadata={'''help''': '''A csv or a json file containing the validation data.'''}			)
   __UpperCAmelCase				: Optional[Any]													=						dataclasses.field(
       default=snake_case_					,							metadata={'''help''': '''The name of the task to train on.'''}					,							)
   __UpperCAmelCase				: Tuple													=						dataclasses.field(
       default=snake_case_					,							metadata={'''help''': '''The list of labels for the task.'''}			)







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







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Optional[Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						List[str]		,							lowercase						:						Optional[int]		,							lowercase						:						List[str]				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Any         =					datasets.concatenate_datasets([infer_input, infer_output]		,							axis=1				)

    if args.do_filter_by_confidence:
        snake_case					:   Optional[int]         =					dataset.filter(lambda lowercase				: example["probability"] > args.confidence_threshold				)

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        snake_case					:   Optional[int]         =					int(eval_result * len(a__				)				)
        print(a__				)
        snake_case					:   int         =					dataset.sort("probability"		,							reverse=a__				)
        snake_case					:   Tuple         =					dataset.select(range(a__				)				)

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

    snake_case					:   List[str]         =					os.path.join(a__		,							F'train_pseudo.{args.data_file_extension}'				)
    if args.data_file_extension == "csv":
        dataset.to_csv(a__		,							index=a__				)
    else:
        dataset.to_json(a__				)







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Tuple		,							lowercase						:						Optional[int]		,							lowercase						:						str		,							**lowercase						:						str				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					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=a__				)
    snake_case					:   Dict         =					STDataArguments(train_file=a__		,							infer_file=a__				)
    snake_case					:   List[str]         =					STTrainingArguments(output_dir=a__				)
    snake_case					:   Union[str, Any]         =					argparse.Namespace()

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

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

        # Sanity checks
    snake_case					:   str         =					{}
    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					:   str         =					args.train_file
    snake_case					:   Any         =					args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        snake_case					:   Any         =					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					:   int         =					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					:   int         =					F'{args.output_dir}/self-train_iter-{{}}'.format
    snake_case					:   Union[str, Any]         =					data_dir_format(0				)

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

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

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        snake_case					:   List[Any]         =					os.path.join(a__		,							"stage-1"				)
        snake_case					:   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(a__		,							a__				):
                arguments_dict.update({key: value}				)

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

        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(a__		,							"best-checkpoint"				)
            snake_case					:   Dict         =					os.path.join(a__		,							"stage-2"				)
            # Update arguments_dict
            snake_case					:   Tuple         =					model_path
            snake_case					:   Any         =					data_files["train"]
            snake_case					:   Union[str, Any]         =					current_output_dir

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

        snake_case					:   List[Any]         =					iteration
        snake_case					:   Union[str, Any]         =					data_dir_format(iteration + 1				)

        snake_case					:   List[Any]         =					AutoConfig.from_pretrained(os.path.join(a__		,							"best-checkpoint"				)				)
        snake_case					:   Union[str, Any]         =					config.idalabel
        snake_case					:   Any         =					os.path.join(a__		,							"eval_results_best-checkpoint.json"				)
        snake_case					:   List[str]         =					os.path.join(a__		,							"test_results_best-checkpoint.json"				)
        assert os.path.exists(a__				)

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

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

        snake_case					:   Any         =					os.path.join(a__		,							F'train_pseudo.{args.data_file_extension}'				)

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            snake_case					:   Union[str, Any]         =					eval_result

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

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    snake_case					:   List[Any]         =					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"		,							a__				)
        logger.info("Best evaluation result: %s = %f"		,							args.eval_metric		,							a__				)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(a__		,							F'eval_results_iter-{iteration}.json'				)		,							os.path.join(a__		,							"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		,							a__				)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(a__		,							F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'				)		,							os.path.join(a__		,							"eval_results_best-iteration.json"				)		,							)









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   Union[str, Any]         =					None

    # Fills the data structure with the probabilities of
    # different transitions and pointers to previous states
    for o in range(1		,							len(lowercase				)				):
        snake_case					:   Optional[Any]         =					observations_space[o]
        snake_case					:   str         =					observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            snake_case					:   str         =					""
            snake_case					:   List[Any]         =					-1
            for k_state in states_space:
                snake_case					:   Tuple         =					(
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    snake_case					:   Optional[Any]         =					probability
                    snake_case					:   int         =					k_state

            # Update probabilities and pointers dicts
            snake_case					:   List[str]         =					(
                probabilities[(arg_max, prior_observation)]
                * transition_probabilities[arg_max][state]
                * emission_probabilities[state][observation]
            )

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

    # argmax for given final observation
    snake_case					:   str         =					""
    snake_case					:   Optional[int]         =					-1
    for k_state in states_space:
        snake_case					:   int         =					probabilities[(k_state, final_observation)]
        if probability > max_probability:
            snake_case					:   Optional[int]         =					probability
            snake_case					:   List[Any]         =					k_state
    snake_case					:   str         =					arg_max

    # Process pointers backwards
    snake_case					:   List[str]         =					last_state
    snake_case					:   Optional[int]         =					[]
    for o in range(len(lowercase				) - 1		,							-1		,							-1				):
        result.append(lowercase				)
        snake_case					:   List[str]         =					pointers[previous, observations_space[o]]
    result.reverse()

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]				):
        raise ValueError("There's an empty parameter"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





