




from argparse import ArgumentParser

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


UpperCamelCase__     :   Union[str, Any]					    =    logging.get_logger(__name__)  # pylint: disable=invalid-name







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str  ):
					if not path:
										return "pipe"

					for ext in PipelineDataFormat.SUPPORTED_FORMATS:
										if path.endswith(SCREAMING_SNAKE_CASE_  ):
															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 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]  ):
					__lowerCAmelCase            = pipeline(
					    task=args.task   ,   model=args.model if args.model else None   ,   config=args.config   ,   tokenizer=args.tokenizer   ,   device=args.device   ,   )
					__lowerCAmelCase            = try_infer_format_from_ext(args.input  ) if args.format == "infer" else args.format
					__lowerCAmelCase            = PipelineDataFormat.from_str(
					    format=SCREAMING_SNAKE_CASE_   ,   output_path=args.output   ,   input_path=args.input   ,   column=args.column if args.column else nlp.default_input_names   ,   overwrite=args.overwrite   ,   )
					return RunCommand(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )


class     a__					(      snake_case__   ):






						def __init__(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = nlp
											__lowerCAmelCase            = reader






						@staticmethod
						def        __SCREAMING_SNAKE_CASE(     _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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=_A	,    help="Path to the file to use for inference"    )
											run_parser.add_argument("--output"	,    type=_A	,    help="Path to the file that will be used post to write results."    )
											run_parser.add_argument("--model"	,    type=_A	,    help="Name or path to the model to instantiate."    )
											run_parser.add_argument("--config"	,    type=_A	,    help="Name or path to the model's config to instantiate."    )
											run_parser.add_argument(
											    "--tokenizer"	,    type=_A	,    help="Name of the tokenizer to use. (default: same as the model name)"    )
											run_parser.add_argument(
											    "--column"	,    type=_A	,    help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"	,    )
											run_parser.add_argument(
											    "--format"	,    type=_A	,    default="infer"	,    choices=PipelineDataFormat.SUPPORTED_FORMATS	,    help="Input format to read from"	,    )
											run_parser.add_argument(
											    "--device"	,    type=_A	,    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=_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = self._nlp, []

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

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










import requests
from bsa import BeautifulSoup







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       dict  ):
					__lowerCAmelCase            = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_   ,   params=SCREAMING_SNAKE_CASE_  ).content   ,   "html.parser"  )
					__lowerCAmelCase            = soup.find("div"   ,   attrs={"class": "gs_ri"}  )
					__lowerCAmelCase            = div.find("div"   ,   attrs={"class": "gs_fl"}  ).find_all("a"  )
					return anchors[2].get_text()


if __name__ == "__main__":
					UpperCamelCase__					    =    {
					    """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))




