

from argparse import ArgumentParser

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


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







def       lowercase					(	__A :					str		)				->			Dict:





    '''simple docstring'''




    if not path:
        return "pipe"

    for ext in PipelineDataFormat.SUPPORTED_FORMATS:
        if path.endswith(__A		):
            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					(	__A :					Optional[Any]		)				->			Tuple:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		pipeline(
        task=args.task ,    model=args.model if args.model else None ,    config=args.config ,    tokenizer=args.tokenizer ,    device=args.device ,    )
    snake_case							:		Optional[int]        =		try_infer_format_from_ext(args.input		) if args.format == """infer""" else args.format
    snake_case							:		Any        =		PipelineDataFormat.from_str(
        format=__A ,    output_path=args.output ,    input_path=args.input ,    column=args.column if args.column else nlp.default_input_names ,    overwrite=args.overwrite ,    )
    return RunCommand(__A ,    __A		)



class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		nlp
      snake_case							:		int        =		reader





  @staticmethod
  def    snake_case_       (  SCREAMING_SNAKE_CASE_    ):

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Tuple        =		self._nlp, []

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

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





"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

