

"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       (DEISMultistepScheduler,)
					lowercase__				 =       (("num_inference_steps", 25),)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,**lowercase_			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''solver_order''': 2,
						}

						config.update(**lowercase_		)
						return config







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Optional[int]=0   ,**lowercase_			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dummy_sample
						lowerCAmelCase__	:						str  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(**lowercase_		)
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)
							scheduler.set_timesteps(lowercase_		)
							# copy over dummy past residuals
							lowerCAmelCase__	:						str  				=	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowercase_		)
								lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class.from_pretrained(lowercase_		)
								new_scheduler.set_timesteps(lowercase_		)
								# copy over dummy past residuals
								lowerCAmelCase__	:						Optional[int]  				=	dummy_past_residuals[: new_scheduler.config.solver_order]

							lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	sample, sample
							for t in range(lowercase_   ,time_step + scheduler.config.solver_order + 1		):
								lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
								lowerCAmelCase__	:						int  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

								assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str=0   ,**lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	self.dummy_sample
						lowerCAmelCase__	:						Any  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						str  				=	scheduler_class(**lowercase_		)
							scheduler.set_timesteps(lowercase_		)

							# copy over dummy past residuals (must be after setting timesteps)
							lowerCAmelCase__	:						Tuple  				=	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowercase_		)
								lowerCAmelCase__	:						Any  				=	scheduler_class.from_pretrained(lowercase_		)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(lowercase_		)

								# copy over dummy past residual (must be after setting timesteps)
								lowerCAmelCase__	:						Optional[Any]  				=	dummy_past_residuals[: new_scheduler.config.solver_order]

							lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						Any  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str=None   ,**lowercase_			:				str		):
						if scheduler is None:
							lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
							lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(**lowercase_		)
							lowerCAmelCase__	:						Tuple  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Optional[int]  				=	self.get_scheduler_config(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	1_0
						lowerCAmelCase__	:						Dict  				=	self.dummy_model()
						lowerCAmelCase__	:						int  				=	self.dummy_sample_deter
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						int  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						return sample







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	dict(self.forward_default_kwargs		)

						lowerCAmelCase__	:						Dict  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						Optional[int]  				=	scheduler_class(**lowercase_		)

							lowerCAmelCase__	:						int  				=	self.dummy_sample
							lowerCAmelCase__	:						int  				=	0.1 * sample

							if num_inference_steps is not None and hasattr(lowercase_   ,'''set_timesteps'''		):
								scheduler.set_timesteps(lowercase_		)
							elif num_inference_steps is not None and not hasattr(lowercase_   ,'''set_timesteps'''		):
								lowerCAmelCase__	:						Tuple  				=	num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							lowerCAmelCase__	:						Dict  				=	[residual + 0.2, residual + 0.15, residual + 0.10]
							lowerCAmelCase__	:						int  				=	dummy_past_residuals[: scheduler.config.solver_order]

							lowerCAmelCase__	:						str  				=	scheduler.timesteps[5]
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.timesteps[6]

							lowerCAmelCase__	:						Dict  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						List[str]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

							self.assertEqual(output_a.shape   ,sample.shape		)
							self.assertEqual(output_a.shape   ,output_a.shape		)







					def        __lowerCAmelCase (       self			:				int		):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						lowerCAmelCase__	:						str  				=	DEISMultistepScheduler(**self.get_scheduler_config()		)
						lowerCAmelCase__	:						str  				=	self.full_loop(scheduler=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3

						lowerCAmelCase__	:						Dict  				=	DPMSolverSinglestepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						List[Any]  				=	DPMSolverMultistepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						str  				=	UniPCMultistepScheduler.from_config(scheduler.config		)
						lowerCAmelCase__	:						Dict  				=	DEISMultistepScheduler.from_config(scheduler.config		)

						lowerCAmelCase__	:						List[Any]  				=	self.full_loop(scheduler=lowercase_		)
						lowerCAmelCase__	:						int  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						self.check_over_configs(thresholding=lowercase_		)
						for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=lowercase_   ,prediction_type=lowercase_   ,sample_max_value=lowercase_   ,algorithm_type='''deis'''   ,solver_order=lowercase_   ,solver_type=lowercase_   ,)







					def        __lowerCAmelCase (       self			:				List[str]		):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										lowerCAmelCase__	:						str  				=	self.full_loop(
										    solver_order=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"







					def        __lowerCAmelCase (       self			:				List[Any]		):
						self.check_over_configs(lower_order_final=lowercase_		)
						self.check_over_configs(lower_order_final=lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=lowercase_   ,time_step=0		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.full_loop()
						lowerCAmelCase__	:						Any  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.2_3916		) < 1E-3







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	self.full_loop(prediction_type='''v_prediction'''		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_mean.item() - 0.091		) < 1E-3







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_scheduler_config(thresholding=lowercase_   ,dynamic_thresholding_ratio=0		)
						lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	1_0
						lowerCAmelCase__	:						Optional[Any]  				=	self.dummy_model()
						lowerCAmelCase__	:						str  				=	self.dummy_sample_deter.half()
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						List[Any]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						assert sample.dtype == torch.floataa












from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase_ :						int      =      '''\
@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}
}
'''

UpperCAmelCase_ :						Optional[Any]      =      '''\
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.
'''

UpperCAmelCase_ :						int      =      '''
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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Optional[Any]					)		->			Union[str, Any]:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  int	,			__magic_name__					:  Any="binary"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	simple_accuracy(__magic_name__	,			__magic_name__					)
	UpperCamelCase     :Dict          =	float(fa_score(y_true=__magic_name__	,			y_pred=__magic_name__	,			average=__magic_name__					)					)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  Optional[Any]					)		->			Optional[Any]:






	"""simple docstring"""



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





	def 						_A (		self				:					str	):
		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				:					Optional[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 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					str	):
		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     :Optional[Any]          =	[
			    {
			        """qas""": [
			            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
			            for ref in references
			        ]
			    }
			]
			UpperCamelCase     :Tuple          =	{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\",]"""	)
