from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase       =		'\\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'

_lowerCAmelCase       =		'\\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'

_lowerCAmelCase       =		'\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	(  __snake_case  ,     __snake_case						):
					return float((preds == labels).mean()						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case="binary"						):
					_UpperCamelCase			     =     simple_accuracy(a__  ,     a__						)
					_UpperCamelCase			     =     float(fa_score(y_true=a__  ,     y_pred=a__  ,     average=a__						)						)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     {}
					for id_pred, label in zip(a__  ,     a__						):
										_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(*a__						)
										_UpperCamelCase			     =     fa_score(y_true=a__  ,     y_pred=a__  ,     average='''macro'''						)
										fas.append(a__						)
										_UpperCamelCase			     =     int(sum(pred == label for pred, label in preds_labels						) == len(a__						)						)
										ems.append(a__						)
					_UpperCamelCase			     =     float(sum(a__						) / len(a__						)						)
					_UpperCamelCase			     =     sum(a__						) / len(a__						)
					_UpperCamelCase			     =     float(fa_score(y_true=a__  ,     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  UpperCamelCase_						(   self      :     Tuple		):




												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      :     Any		):




												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      :     List[str] ,			_A      :     Optional[int] ,			_A      :     List[str]		):




												if self.config_name == "axb":
																	return {"matthews_correlation": matthews_corrcoef(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE		)}
												elif self.config_name == "cb":
																	return acc_and_fa(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			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(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE		)[0]
												elif self.config_name == "multirc":
																	return evaluate_multirc(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE		)
												elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																	return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE		)}
												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

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

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







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

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






