"""simple docstring"""




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCAmelCase     :				int    				=    "\\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     :				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     :				Optional[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"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = {}
		for id_pred, label in zip(lowerCamelCase__      ,	lowerCamelCase__  ):
				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(*lowerCamelCase__  )
				lowerCAmelCase__   = fa_score(y_true=lowerCamelCase__      ,	y_pred=lowerCamelCase__      ,	average="""macro"""  )
				fas.append(lowerCamelCase__  )
				lowerCAmelCase__   = int(sum(pred == label for pred, label in preds_labels  ) == len(lowerCamelCase__  )  )
				ems.append(lowerCamelCase__  )
		lowerCAmelCase__   = float(sum(lowerCamelCase__  ) / len(lowerCamelCase__  )  )
		lowerCAmelCase__   = sum(lowerCamelCase__  ) / len(lowerCamelCase__  )
		lowerCAmelCase__   = 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    a_   (  datasets.Metric      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							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     _SCREAMING_SNAKE_CASE    (						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     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  List[Any]	,    snake_case__    :  Dict  ):
							if self.config_name == "axb":
									return {"matthews_correlation": matthews_corrcoef(snake_case__	,    snake_case__  )}
							elif self.config_name == "cb":
									return acc_and_fa(snake_case__	,    snake_case__	,    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(snake_case__	,    snake_case__  )[0]
							elif self.config_name == "multirc":
									return evaluate_multirc(snake_case__	,    snake_case__  )
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
									return {"accuracy": simple_accuracy(snake_case__	,    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 requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    numpify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    np.ndarray  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    torchify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    torch.Tensor  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




