




'''simple docstring'''






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


A_			      =		{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=None		)    ->   Optional[int]:
      # Initialise PyTorch model
      snake_case__       :   Optional[Any]	      =    XLNetConfig.from_json_file(__SCREAMING_SNAKE_CASE		)

      snake_case__       :   Optional[Any]	      =    finetuning_task.lower() if finetuning_task is not None else ''
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(f"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"		)
            snake_case__       :   Any	      =    finetuning_task
            snake_case__       :   List[str]	      =    GLUE_TASKS_NUM_LABELS[finetuning_task]
            snake_case__       :   str	      =    XLNetForSequenceClassification(__SCREAMING_SNAKE_CASE		)
      elif "squad" in finetuning_task:
            snake_case__       :   Optional[int]	      =    finetuning_task
            snake_case__       :   Union[str, Any]	      =    XLNetForQuestionAnswering(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__       :   Tuple	      =    XLNetLMHeadModel(__SCREAMING_SNAKE_CASE		)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      # Save pytorch-model
      snake_case__       :   List[Any]	      =    os.path.join(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      snake_case__       :   Tuple	      =    os.path.join(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      print(f"Save PyTorch model to {os.path.abspath(__SCREAMING_SNAKE_CASE		)}"		)
      torch.save(model.state_dict()				,      __SCREAMING_SNAKE_CASE		)
      print(f"Save configuration file to {os.path.abspath(__SCREAMING_SNAKE_CASE		)}"		)
      with open(__SCREAMING_SNAKE_CASE				,      'w'				,      encoding='utf-8'		) as f:
            f.write(config.to_json_string()		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--xlnet_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained XLNet model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the folder to store the PyTorch model or dataset/vocab.",
      )
      parser.add_argument(
          "--finetuning_task",
          default=None,
          type=str,
          help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
      )
      A_			      =		parser.parse_args()
      print(args)

      convert_xlnet_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
      )






'''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))
