

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase  :      Optional[Any]			 ="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_lowerCAmelCase  :      List[str]			 ="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_lowerCAmelCase  :      Any			 ="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	return float((preds == labels).mean()      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE="binary"      ):
	UpperCAmelCase__:	Union[str, Any]									= simple_accuracy(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= float(fa_score(y_true=SCREAMING_SNAKE_CASE	,y_pred=SCREAMING_SNAKE_CASE	,average=SCREAMING_SNAKE_CASE      )      )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}



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






					'''simple docstring'''



					def    _UpperCAmelCase    (				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    _UpperCAmelCase    (				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    _UpperCAmelCase    (				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__:	int									= [
							    {
							        "qas": [
							            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
							            for ref in references
							        ]
							    }
							]
							UpperCAmelCase__:	List[str]									= {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\",]"						)


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes