





import datasets


_UpperCAmelCase             =							"\\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"

_UpperCAmelCase             =							"\\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"

_UpperCAmelCase             =							"\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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   Optional[Any]:
 return (preds == labels).mean()







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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[str]:






   '''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			:					Optional[int]       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					str      )->Optional[int]:






   '''simple docstring'''

   return {"accuracy": simple_accuracy(_snake_case       ,  _snake_case      )}








from datetime import datetime

import requests






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   bytes:
 __lowerCAmelCase					:	List[Any]      =       """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
 __lowerCAmelCase					:	Dict      =       requests.get(base_url + url   ).json()[0]["""urls"""][0]["""src"""]
 return requests.get(SCREAMING_SNAKE_CASE   ).content


if __name__ == "__main__":
      _UpperCAmelCase             =							input('Enter Video/IGTV url: ').strip()
      _UpperCAmelCase             =							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}.''')