





'''simple docstring'''






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   __A    (				a_    :	Union[str, Any]   ,a_    :	List[str]						):
		return float((preds == labels).mean()						)






def   __A    (				a_    :	Tuple   ,a_    :	Any   ,a_    :	str="binary"						):
		lowerCAmelCase  :     Optional[Any]       						= simple_accuracy(a_   ,a_						)
		lowerCAmelCase  :     Union[str, Any]       						= float(fa_score(y_true=a_   ,y_pred=a_   ,average=a_						)						)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}






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











"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
			lowerCAmelCase__    						=			input('Enter image url: ').strip()
			print(F'Downloading image from {url} ...')
			lowerCAmelCase__    						=			BeautifulSoup(requests.get(url).content, 'html.parser')
			# The image URL is in the content field of the first meta tag with property og:image
			lowerCAmelCase__    						=			soup.find('meta', {'property': 'og:image'})['content']
			lowerCAmelCase__    						=			requests.get(image_url).content
			lowerCAmelCase__    						=			F'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
			with open(file_name, 'wb') as fp:
						fp.write(image_data)
			print(F'Done. Image saved to disk as {file_name}.')

