"""simple docstring"""





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_UpperCamelCase     :		Any											=					"\\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"

_UpperCamelCase     :		Any											=					"\\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"

_UpperCamelCase     :		Any											=					"\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        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Union[str, Any]="binary"  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			simple_accuracy(_lowerCAmelCase     ,				_lowerCAmelCase  )
       lowercase__    :	str     					=			float(fa_score(y_true=_lowerCAmelCase     ,				y_pred=_lowerCAmelCase     ,				average=_lowerCAmelCase  )  )
       return {
           "accuracy": acc,
           "f1": fa,
       }





def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             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							) ->      Dict:
             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	,    a	,    a							) ->      Union[str, 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":
                    lowercase__    :	str     					=			[
                        {
                            'qas': [
                                {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                                for ref in references
                            ]
                        }
                    ]
                    lowercase__    :	str     					=			{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",]'							)


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

