





'''simple docstring'''

import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    DeiTForImageClassification,
			    DeiTForImageClassificationWithTeacher,
			    DeiTForMaskedImageModeling,
			    DeiTModel,
			)
			from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class       UpperCAmelCase :




		def __init__(self      :	str							,			A__      :	List[Any]							,			A__      :	str=1_3							,			A__      :	Union[str, Any]=3_0							,			A__      :	Any=2							,			A__      :	Optional[int]=3							,			A__      :	List[str]=True							,			A__      :	str=True							,			A__      :	Union[str, Any]=3_2							,			A__      :	Any=5							,			A__      :	List[str]=4							,			A__      :	str=3_7							,			A__      :	Tuple="gelu"							,			A__      :	Union[str, Any]=0.1							,			A__      :	str=0.1							,			A__      :	List[str]=1_0							,			A__      :	int=0.0_2							,			A__      :	List[Any]=3							,			A__      :	Optional[int]=None							,			A__      :	Union[str, Any]=2							,			)					-> Tuple:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = scope
						lowercase         = encoder_stride

						# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
						lowercase         = (image_size // patch_size) ** 2
						lowercase         = num_patches + 2




		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowercase         = None
						if self.use_labels:
										lowercase         = ids_tensor([self.batch_size]							,			self.type_sequence_label_size				)

						lowercase         = self.get_config()

						return config, pixel_values, labels




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						return DeiTConfig(
						    image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			is_decoder=A__							,			initializer_range=self.initializer_range							,			encoder_stride=self.encoder_stride							,			)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	List[str]							,			A__      :	Optional[int]							,			A__      :	Any				)					-> List[str]:
						lowercase         = DeiTModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__				)
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)				)




		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	str							,			A__      :	Optional[Any]							,			A__      :	Union[str, Any]				)					-> List[Any]:
						lowercase         = DeiTForMaskedImageModeling(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__				)
						self.parent.assertEqual(
						    result.reconstruction.shape							,			(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

						# test greyscale images
						lowercase         = 1
						lowercase         = DeiTForMaskedImageModeling(A__				)
						model.to(A__				)
						model.eval()

						lowercase         = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
						lowercase         = model(A__				)
						self.parent.assertEqual(result.reconstruction.shape							,			(self.batch_size, 1, self.image_size, self.image_size)				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	Union[str, Any]							,			A__      :	Optional[int]							,			A__      :	str				)					-> List[str]:
						lowercase         = self.type_sequence_label_size
						lowercase         = DeiTForImageClassification(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)				)

						# test greyscale images
						lowercase         = 1
						lowercase         = DeiTForImageClassification(A__				)
						model.to(A__				)
						model.eval()

						lowercase         = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
						lowercase         = model(A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)				)





		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Dict:
						lowercase         = self.prepare_config_and_inputs()
						(
						    (
						    lowercase					
						)   ,							(
						    lowercase					
						)   ,							(
						    lowercase					
						)   ,												
						)         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						lowercase_			,       lowercase_			,       unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		(
		    (
		        DeiTModel,
		        DeiTForImageClassification,
		        DeiTForImageClassificationWithTeacher,
		        DeiTForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase					:	Optional[int] 			=		(
		    {
		        '''feature-extraction''': DeiTModel,
		        '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
		    }
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase					:	Optional[int] 			=		False
		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	str 			=		False




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict:
						lowercase         = DeiTModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=A__							,			has_text_modality=A__							,			hidden_size=3_7				)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[int]:
						self.config_tester.run_common_tests()




		@unittest.skip(reason="DeiT does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						pass




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(A__				)
										self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)				)
										lowercase         = model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(A__							,			nn.Linear				)				)




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(A__				)
										lowercase         = inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase         = [*signature.parameters.keys()]

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			A__				)




		def 				UpperCAmelCase__   (self      :	str				)					-> Dict:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> List[str]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__				)




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	str							,			A__      :	List[Any]							,			A__      :	Any=False				)					-> Dict:
						lowercase         = super()._prepare_for_class(A__							,			A__							,			return_labels=A__				)

						if return_labels:
										if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
														del inputs_dict["labels"]

						return inputs_dict




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[Any]:
						if not self.model_tester.is_training:
										return

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = True

						for model_class in self.all_model_classes:
										# DeiTForImageClassificationWithTeacher supports inference-only
										if (
										    model_class in get_values(A__				)
										    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
										):
														continue
										lowercase         = model_class(A__				)
										model.to(A__				)
										model.train()
										lowercase         = self._prepare_for_class(A__							,			A__							,			return_labels=A__				)
										lowercase         = model(**A__				).loss
										loss.backward()




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[Any]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
										return

						lowercase         = False
						lowercase         = True

						for model_class in self.all_model_classes:
										if model_class in get_values(A__				) or not model_class.supports_gradient_checkpointing:
														continue
										# DeiTForImageClassificationWithTeacher supports inference-only
										if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
														continue
										lowercase         = model_class(A__				)
										model.gradient_checkpointing_enable()
										model.to(A__				)
										model.train()
										lowercase         = self._prepare_for_class(A__							,			A__							,			return_labels=A__				)
										lowercase         = model(**A__				).loss
										loss.backward()




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						lowercase         = [
						    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
						    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
						    {"title": "regression", "num_labels": 1, "dtype": torch.float},
						]

						for model_class in self.all_model_classes:
										if (
										    model_class
										    not in [
										        *get_values(A__				),
										        *get_values(A__				),
										    ]
										    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
										):
														continue

										for problem_type in problem_types:
														with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'				):
																		lowercase         = problem_type["title"]
																		lowercase         = problem_type["num_labels"]

																		lowercase         = model_class(A__				)
																		model.to(A__				)
																		model.train()

																		lowercase         = self._prepare_for_class(A__							,			A__							,			return_labels=A__				)

																		if problem_type["num_labels"] > 1:
																						lowercase         = inputs["labels"].unsqueeze(1				).repeat(1							,			problem_type["num_labels"]				)

																		lowercase         = inputs["labels"].to(problem_type["dtype"]				)

																		# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																		# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																		# they have the same size." which is a symptom something in wrong for the regression problem.
																		# See https://github.com/huggingface/transformers/issues/11780
																		with warnings.catch_warnings(record=A__				) as warning_list:
																						lowercase         = model(**A__				).loss
																		for w in warning_list:
																						if "Using a target size that is different to the input size" in str(w.message				):
																										raise ValueError(
																										    f'Something is going wrong in the regression problem: intercepted {w.message}'				)

																		loss.backward()





		@slow
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[str]:
						for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = DeiTModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
				return image


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




		@cached_property
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Optional[Any]:
						return (
						    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
						    if is_vision_available()
						    else None
						)




		@slow
		def 				UpperCAmelCase__   (self      :	int				)					-> List[str]:
						lowercase         = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				).to(
						    A__				)

						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(images=A__							,			return_tensors="pt"				).to(A__				)

						# forward pass
						with torch.no_grad():
										lowercase         = model(**A__				)

						# verify the logits
						lowercase         = torch.Size((1, 1_0_0_0)				)
						self.assertEqual(outputs.logits.shape							,			A__				)

						lowercase         = torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]				).to(A__				)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]							,			A__							,			atol=1e-4				)				)





		@slow
		@require_accelerate
		@require_torch_gpu
		def 				UpperCAmelCase__   (self      :	str				)					-> Optional[int]:
						lowercase         = DeiTModel.from_pretrained(
						    "facebook/deit-base-distilled-patch16-224"							,			torch_dtype=torch.floataa							,			device_map="auto"				)
						lowercase         = self.default_image_processor

						lowercase         = prepare_img()
						lowercase         = image_processor(images=A__							,			return_tensors="pt"				)
						lowercase         = inputs.pixel_values.to(A__				)

						# forward pass to make sure inference works in fp16
						with torch.no_grad():
										lowercase         = model(A__				)











'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCamelCase : str											=			"\\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 : Optional[int]											=			"\\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 : int											=			"\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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_="binary"							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				for id_pred, label in zip(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
								lowercase         = id_pred["prediction"]
								if question_id in question_map:
												question_map[question_id].append((pred, label)							)
								else:
												lowercase         = [(pred, label)]
				lowercase   ,							lowercase         = [], []
				for question, preds_labels in question_map.items():
								lowercase   ,							lowercase         = zip(*lowerCAmelCase_							)
								lowercase         = fa_score(y_true=lowerCAmelCase_		,							y_pred=lowerCAmelCase_		,							average="macro"							)
								fas.append(lowerCAmelCase_							)
								lowercase         = int(sum(pred == label for pred, label in preds_labels							) == len(lowerCAmelCase_							)							)
								ems.append(lowerCAmelCase_							)
				lowercase         = float(sum(lowerCAmelCase_							) / len(lowerCAmelCase_							)							)
				lowercase         = sum(lowerCAmelCase_							) / len(lowerCAmelCase_							)
				lowercase         = 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      :	Any				)					-> 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 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						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      :	Any							,			A__      :	int							,			A__      :	List[str]				)					-> Dict:
						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         = [
										    {
										        "qas": [
										            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
										            for ref in references
										        ]
										    }
										]
										lowercase         = {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\",]"				)




