



'''simple docstring'''
import datasets


snake_case_			:     Optional[Any]  	=  '\\n@InProceedings{conneau2018xnli,\n  author = "Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin",\n  title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n  booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing",\n  year = "2018",\n  publisher = "Association for Computational Linguistics",\n  location = "Brussels, Belgium",\n}\n'

snake_case_			:     Dict  	=  '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

snake_case_			:     Tuple  	=  '\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric("xnli")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION       )
class 							lowercase__     (							datasets.Metric       ):





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION			,citation=_CITATION			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
							        {
							            'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'			),
							            'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'			),
							        }			)			,codebase_urls=[]			,reference_urls=[]			,format='numpy'			,)





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							return {"accuracy": simple_accuracy(lowerCamelCase__			,lowerCamelCase__			)}







'''simple docstring'''
from datetime import datetime

import requests






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
						_UpperCamelCase :		Optional[int]			    =       requests.get(base_url + url							).json()[0]['urls'][0]['src']
						return requests.get(UpperCAmelCase_							).content


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  input('Enter Video/IGTV url: ').strip()
					snake_case_			:     str  	=  F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
					with open(file_name, 'wb') as fp:
										fp.write(download_video(url))
					print(F"""Done. Video saved to disk as {file_name}.""")


