


"""simple docstring"""



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case             =   "\\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"

__snake_case             =   "\\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"

__snake_case             =   "\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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Optional[int]			)     ->						List[Any]:


				"""simple docstring"""





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







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


				"""simple docstring"""





				snake_case   :				List[Any]				 =   simple_accuracy(lowerCamelCase__						,							lowerCamelCase__			)
				snake_case   :				List[str]				 =   float(fa_score(y_true=lowerCamelCase__						,							y_pred=lowerCamelCase__						,							average=lowerCamelCase__			)			)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







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


				"""simple docstring"""





				snake_case   :				Union[str, Any]				 =   {}
				for id_pred, label in zip(lowerCamelCase__						,							lowerCamelCase__			):
								snake_case   :				Any				 =   F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
								snake_case   :				Dict				 =   id_pred["prediction"]
								if question_id in question_map:
												question_map[question_id].append((pred, label)			)
								else:
												snake_case   :				List[str]				 =   [(pred, label)]
				snake_case ,snake_case   :				Tuple				 =   [], []
				for question, preds_labels in question_map.items():
								snake_case ,snake_case   :				str				 =   zip(*lowerCamelCase__			)
								snake_case   :				Union[str, Any]				 =   fa_score(y_true=lowerCamelCase__						,							y_pred=lowerCamelCase__						,							average="macro"			)
								fas.append(lowerCamelCase__			)
								snake_case   :				int				 =   int(sum(pred == label for pred, label in preds_labels			) == len(lowerCamelCase__			)			)
								ems.append(lowerCamelCase__			)
				snake_case   :				Optional[Any]				 =   float(sum(lowerCamelCase__			) / len(lowerCamelCase__			)			)
				snake_case   :				List[str]				 =   sum(lowerCamelCase__			) / len(lowerCamelCase__			)
				snake_case   :				Optional[int]				 =   float(fa_score(y_true=lowerCamelCase__						,							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     ):







			def 			lowerCamelCase		(     self						)     ->		Tuple:

							'''simple docstring'''




							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 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

							'''simple docstring'''




							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 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Dict:

							'''simple docstring'''




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









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





