




'''simple docstring'''






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm


A_			      =		logging.get_logger(__name__)






@dataclass
class        lowercase_      (						lowerCAmelCase_							):
     A_											=			[
         "no_inference",
         "no_cuda",
         "no_tpu",
         "no_speed",
         "no_memory",
         "no_env_print",
         "no_multi_process",
     ]





     def __init__(     self     :					Dict     ,							**__lowerCamelCase     :					Union[str, Any]			):
           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       snake_case__       :   Optional[Any]	      =    deprecated_arg[3:]
                       setattr(self     ,							__lowerCamelCase     ,							not kwargs.pop(__lowerCamelCase			)			)
                       logger.warning(
                           F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
                           F" {positive_arg}={kwargs[positive_arg]}"			)

           snake_case__       :   int	      =    kwargs.pop('torchscript'     ,							self.torchscript			)
           snake_case__       :   List[Any]	      =    kwargs.pop('torch_xla_tpu_print_metrics'     ,							self.torch_xla_tpu_print_metrics			)
           snake_case__       :   List[Any]	      =    kwargs.pop('fp16_opt_level'     ,							self.fpaa_opt_level			)
           super().__init__(**__lowerCamelCase			)

     A_											=			field(default=lowerCAmelCase_						,       metadata={"help": "Trace the models using torchscript"}							)
     A_											=			field(default=lowerCAmelCase_						,       metadata={"help": "Print Xla/PyTorch tpu metrics"}							)
     A_											=			field(
         default="O1"						,       metadata={
             "help": (
                 "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                 "See details at https://nvidia.github.io/apex/amp.html"
             )
         }						,       )





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Dict			):
           requires_backends(self     ,							['torch']			)
           logger.info('PyTorch: setting up devices'			)
           if not self.cuda:
                 snake_case__       :   int	      =    torch.device('cpu'			)
                 snake_case__       :   List[Any]	      =    0
           elif is_torch_tpu_available():
                 snake_case__       :   Optional[int]	      =    xm.xla_device()
                 snake_case__       :   Tuple	      =    0
           else:
                 snake_case__       :   List[str]	      =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
                 snake_case__       :   Optional[int]	      =    torch.cuda.device_count()
           return device, n_gpu





     @property
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return is_torch_tpu_available() and self.tpu





     @property
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           requires_backends(self     ,							['torch']			)
           # TODO(PVP): currently only single GPU is supported
           return torch.cuda.current_device()





     @property
     def 			_lowerCAmelCase      (     self     :					Dict			):
           requires_backends(self     ,							['torch']			)
           return self._setup_devices[0]





     @property
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           requires_backends(self     ,							['torch']			)
           return self._setup_devices[1]





     @property
     def 			_lowerCAmelCase      (     self     :					int			):
           return self.n_gpu > 0






'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
