



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


SCREAMING_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"

SCREAMING_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"

SCREAMING_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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
		return float((preds == labels).mean() )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE="binary" )		->							Union[str, Any]:
		UpperCAmelCase_						=				simple_accuracy(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				float(fa_score(y_true=__SCREAMING_SNAKE_CASE			,     y_pred=__SCREAMING_SNAKE_CASE			,     average=__SCREAMING_SNAKE_CASE ) )
		return {
		    "accuracy": acc,
		    "f1": fa,
		}







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
		UpperCAmelCase_						=				{}
		for id_pred, label in zip(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_						=				f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
				UpperCAmelCase_						=				id_pred["prediction"]
				if question_id in question_map:
						question_map[question_id].append((pred, label) )
				else:
						UpperCAmelCase_						=				[(pred, label)]
		UpperCAmelCase_					,						UpperCAmelCase_						=				[], []
		for question, preds_labels in question_map.items():
				UpperCAmelCase_					,						UpperCAmelCase_						=				zip(*__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				fa_score(y_true=__SCREAMING_SNAKE_CASE			,     y_pred=__SCREAMING_SNAKE_CASE			,     average="macro" )
				fas.append(__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				int(sum(pred == label for pred, label in preds_labels ) == len(__SCREAMING_SNAKE_CASE ) )
				ems.append(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				float(sum(__SCREAMING_SNAKE_CASE ) / len(__SCREAMING_SNAKE_CASE ) )
		UpperCAmelCase_						=				sum(__SCREAMING_SNAKE_CASE ) / len(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				float(fa_score(y_true=__SCREAMING_SNAKE_CASE			,     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    A__		(			self     ):
							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    A__		(			self     ):
							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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							if self.config_name == "axb":
									return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase    ,	lowerCAmelCase     )}
							elif self.config_name == "cb":
									return acc_and_fa(lowerCAmelCase    ,	lowerCAmelCase    ,	fa_avg="macro"     )
							elif self.config_name == "record":
									UpperCAmelCase_						=				[
									    {
									        "qas": [
									            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
									            for ref in references
									        ]
									    }
									]
									UpperCAmelCase_						=				{pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
									return evaluate_record(lowerCAmelCase    ,	lowerCAmelCase     )[0]
							elif self.config_name == "multirc":
									return evaluate_multirc(lowerCAmelCase    ,	lowerCAmelCase     )
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
									return {"accuracy": simple_accuracy(lowerCAmelCase    ,	lowerCAmelCase     )}
							else:
									raise KeyError(
									    "You should supply a configuration name selected in "
									    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"     )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  'xlm-roberta'



					def __init__(			self    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase="absolute"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(pad_token_id=lowerCAmelCase    ,	bos_token_id=lowerCAmelCase    ,	eos_token_id=lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				type_vocab_size
							UpperCAmelCase_						=				initializer_range
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				position_embedding_type
							UpperCAmelCase_						=				use_cache
							UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							if self.task == "multiple-choice":
									UpperCAmelCase_						=				{0: "batch", 1: "choice", 2: "sequence"}
							else:
									UpperCAmelCase_						=				{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]     )

