



'''simple docstring'''
from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_UpperCamelCase							 =	logging.get_logger(__name__)  # pylint: disable=invalid-name


def      lowercase_		(      lowerCAmelCase__		:     Union[str, Any]       ):

			"""simple docstring"""





			if not path:
						return "pipe"

			for ext in PipelineDataFormat.SUPPORTED_FORMATS:
						if path.endswith(lowercase_       ):
									return ext

			raise Exception(
			    f'Unable to determine file format from file extension {path}. '
			    f'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'       )


def      lowercase_		(      lowerCAmelCase__		:     str       ):

			"""simple docstring"""





			__UpperCAmelCase      :					str   =   pipeline(
			    task=args.task	,							model=args.model if args.model else None	,							config=args.config	,							tokenizer=args.tokenizer	,							device=args.device	,							)
			__UpperCAmelCase      :					Dict   =   try_infer_format_from_ext(args.input       ) if args.format == """infer""" else args.format
			__UpperCAmelCase      :					List[str]   =   PipelineDataFormat.from_str(
			    format=lowercase_	,							output_path=args.output	,							input_path=args.input	,							column=args.column if args.column else nlp.default_input_names	,							overwrite=args.overwrite	,							)
			return RunCommand(lowercase_	,							lowercase_       )






class _A     (			UpperCAmelCase__			):





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   nlp
					__UpperCAmelCase      :					Dict   =   reader





		@staticmethod
		def 							__A				(   __UpperCAmelCase )				->   Any:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   parser.add_parser("""run"""       ,				help="""Run a pipeline through the CLI""" )
					run_parser.add_argument("""--task"""       ,				choices=get_supported_tasks()       ,				help="""Task to run""" )
					run_parser.add_argument("""--input"""       ,				type=UpperCAmelCase__       ,				help="""Path to the file to use for inference""" )
					run_parser.add_argument("""--output"""       ,				type=UpperCAmelCase__       ,				help="""Path to the file that will be used post to write results.""" )
					run_parser.add_argument("""--model"""       ,				type=UpperCAmelCase__       ,				help="""Name or path to the model to instantiate.""" )
					run_parser.add_argument("""--config"""       ,				type=UpperCAmelCase__       ,				help="""Name or path to the model\'s config to instantiate.""" )
					run_parser.add_argument(
					    """--tokenizer"""       ,				type=UpperCAmelCase__       ,				help="""Name of the tokenizer to use. (default: same as the model name)""" )
					run_parser.add_argument(
					    """--column"""       ,				type=UpperCAmelCase__       ,				help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""       ,				)
					run_parser.add_argument(
					    """--format"""       ,				type=UpperCAmelCase__       ,				default="""infer"""       ,				choices=PipelineDataFormat.SUPPORTED_FORMATS       ,				help="""Input format to read from"""       ,				)
					run_parser.add_argument(
					    """--device"""       ,				type=UpperCAmelCase__       ,				default=-1       ,				help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""       ,				)
					run_parser.add_argument("""--overwrite"""       ,				action="""store_true"""       ,				help="""Allow overwriting the output file.""" )
					run_parser.set_defaults(func=UpperCAmelCase__ )





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase ,     __UpperCAmelCase      :					Optional[int]   =   self._nlp, []

					for entry in self._reader:
								__UpperCAmelCase      :					Any   =   nlp(**UpperCAmelCase__ ) if self._reader.is_multi_columns else nlp(UpperCAmelCase__ )
								if isinstance(UpperCAmelCase__       ,				UpperCAmelCase__ ):
											outputs.append(UpperCAmelCase__ )
								else:
											outputs += output

        # Saving data
					if self._nlp.binary_output:
								__UpperCAmelCase      :					Tuple   =   self._reader.save_binary(UpperCAmelCase__ )
								logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
					else:
								self._reader.save(UpperCAmelCase__ )










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """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))


