
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A   =			"\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

_A   =			"\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

_A   =			"\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    return float((preds == labels).mean()		)



def 						lowercase_      (       A__			,		A__			,		A__="binary"		)							->							int:
    """simple docstring"""

    snake_case								= simple_accuracy(A__			,		A__		)
    snake_case								= float(fa_score(y_true=A__			,		y_pred=A__			,		average=A__		)		)
    return {
        "accuracy": acc,
        "f1": fa,
    }



def 						lowercase_      (       A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case								= {}
    for id_pred, label in zip(A__			,		A__		):
        snake_case								= F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
        snake_case								= id_pred["prediction"]
        if question_id in question_map:
            question_map[question_id].append((pred, label)		)
        else:
            snake_case								= [(pred, label)]
    snake_case					,							snake_case								= [], []
    for question, preds_labels in question_map.items():
        snake_case					,							snake_case								= zip(*A__		)
        snake_case								= fa_score(y_true=A__			,		y_pred=A__			,		average="macro"		)
        fas.append(A__		)
        snake_case								= int(sum(pred == label for pred, label in preds_labels		) == len(A__		)		)
        ems.append(A__		)
    snake_case								= float(sum(A__		) / len(A__		)		)
    snake_case								= sum(A__		) / len(A__		)
    snake_case								= float(fa_score(y_true=A__			,		y_pred=[id_pred["prediction"] for id_pred in ids_preds]		)		)
    return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION  )
class 					lowerCamelCase		(  datasets.Metric  ):






     """simple docstring"""




     def 		UpperCAmelCase(self				:   Tuple       )						->						List[str]:
         if self.config_name not in [
             "boolq",
             "cb",
             "copa",
             "multirc",
             "record",
             "rte",
             "wic",
             "wsc",
             "wsc.fixed",
             "axb",
             "axg",
         ]:
             raise KeyError(
                 "You should supply a configuration name selected in "
                 "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
         return datasets.MetricInfo(
             description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(self._get_feature_types()       )    ,   codebase_urls=[]    ,   reference_urls=[]    ,   format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None    ,   )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         if self.config_name == "record":
             return {
                 "predictions": {
                     "idx": {
                         "passage": datasets.Value("int64"       ),
                         "query": datasets.Value("int64"       ),
                     },
                     "prediction_text": datasets.Value("string"       ),
                 },
                 "references": {
                     "idx": {
                         "passage": datasets.Value("int64"       ),
                         "query": datasets.Value("int64"       ),
                     },
                     "answers": datasets.Sequence(datasets.Value("string"       )       ),
                 },
             }
         elif self.config_name == "multirc":
             return {
                 "predictions": {
                     "idx": {
                         "answer": datasets.Value("int64"       ),
                         "paragraph": datasets.Value("int64"       ),
                         "question": datasets.Value("int64"       ),
                     },
                     "prediction": datasets.Value("int64"       ),
                 },
                 "references": datasets.Value("int64"       ),
             }
         else:
             return {
                 "predictions": datasets.Value("int64"       ),
                 "references": datasets.Value("int64"       ),
             }





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   int    ,   _A				:   Tuple       )						->						Optional[Any]:
         if self.config_name == "axb":
             return {"matthews_correlation": matthews_corrcoef(_A    ,   _A       )}
         elif self.config_name == "cb":
             return acc_and_fa(_A    ,   _A    ,   fa_avg="macro"       )
         elif self.config_name == "record":
             snake_case								= [
                 {
                     "qas": [
                         {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                         for ref in references
                     ]
                 }
             ]
             snake_case								= {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
             return evaluate_record(_A    ,   _A       )[0]
         elif self.config_name == "multirc":
             return evaluate_multirc(_A    ,   _A       )
         elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
             return {"accuracy": simple_accuracy(_A    ,   _A       )}
         else:
             raise KeyError(
                 "You should supply a configuration name selected in "
                 "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )



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   =			logging.getLogger(__name__)

_A   =			"pytorch_model.bin"







@dataclasses.dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		dataclasses.field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			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__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			metadata={"help": "A csv or a json file containing the validation data."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			metadata={"help": "The name of the task to train on."}							,			)
     UpperCAmelCase__    :							Optional[List[str]]									=		dataclasses.field(
         default=A_							,			metadata={"help": "The list of labels for the task."}  )







@dataclasses.dataclass
class 					lowerCamelCase		:
     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=A_							,			metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}							,			)
     UpperCAmelCase__    :							Optional[bool]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}							,			)
     UpperCAmelCase__    :							Optional[bool]									=		dataclasses.field(
         default=A_							,			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=1_00							,			metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,			)
     UpperCAmelCase__    :							Optional[int]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Random seed for initialization."}							,			)



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

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

    if args.do_filter_by_confidence:
        snake_case								= 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
        snake_case								= int(eval_result * len(A__		)		)
        print(A__		)
        snake_case								= dataset.sort("probability"			,		reverse=A__		)
        snake_case								= dataset.select(range(A__		)		)

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

    snake_case								= 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 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		**A__		)							->							List[Any]:
    """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=A__		)
    snake_case								= STDataArguments(train_file=A__			,		infer_file=A__		)
    snake_case								= STTrainingArguments(output_dir=A__		)
    snake_case								= 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								= {}
    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=A__		)
            os.makedirs(A__			,		exist_ok=A__		)
    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(A__		)
        assert os.path.exists(A__		)

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

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

            snake_case								= 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								= iteration
        snake_case								= data_dir_format(iteration + 1		)

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

        with open(A__			,		"r"		) as f:
            snake_case								= float(json.load(A__		)[args.eval_metric]		)
        snake_case								= 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								= 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(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								= os.path.join(A__			,		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"			,		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"		)			,		)

