


"""simple docstring"""


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A     :   Tuple			    =			"\\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     :   Dict			    =			"\\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     :   List[str]			    =			"\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	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase="binary"      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{}
			for id_pred, label in zip(_UpperCamelCase    , _UpperCamelCase      ):
						__lowerCAmelCase				    =			f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
						__lowerCAmelCase				    =			id_pred["prediction"]
						if question_id in question_map:
									question_map[question_id].append((pred, label)      )
						else:
									__lowerCAmelCase				    =			[(pred, label)]
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			[], []
			for question, preds_labels in question_map.items():
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			zip(*_UpperCamelCase      )
						__lowerCAmelCase				    =			fa_score(y_true=_UpperCamelCase    , y_pred=_UpperCamelCase    , average="macro"      )
						fas.append(_UpperCamelCase      )
						__lowerCAmelCase				    =			int(sum(pred == label for pred, label in preds_labels      ) == len(_UpperCamelCase      )      )
						ems.append(_UpperCamelCase      )
			__lowerCAmelCase				    =			float(sum(_UpperCamelCase      ) / len(_UpperCamelCase      )      )
			__lowerCAmelCase				    =			sum(_UpperCamelCase      ) / len(_UpperCamelCase      )
			__lowerCAmelCase				    =			float(fa_score(y_true=_UpperCamelCase    , 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 			snake_case				(     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 			snake_case				(     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 			snake_case				(     self    ,   __a    ,   __a	):
										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":
													__lowerCAmelCase				    =			[
													    {
													        "qas": [
													            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
													            for ref in references
													        ]
													    }
													]
													__lowerCAmelCase				    =			{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'''




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





