


from argparse import ArgumentParser

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


snake_case				:    int       				=     logging.get_logger(__name__)  # pylint: disable=invalid-name
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(__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 				__lowerCamelCase						( UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:List[str] 							=       pipeline(
          task=args.task ,  model=args.model if args.model else None ,  config=args.config ,  tokenizer=args.tokenizer ,  device=args.device ,  )
      a						:Optional[int] 							=       try_infer_format_from_ext(args.input						) if args.format == '''infer''' else args.format
      a						:int 							=       PipelineDataFormat.from_str(
          format=__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(__snake_case ,  __snake_case						)







class 		_snake_case  (      _snake_case							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Any 							=       nlp
       a						:Optional[int] 							=       reader




 @staticmethod
 def        SCREAMING_SNAKE_CASE__				(		_lowerCamelCase    ):
       a						:int 							=       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    ):
       a							,				a						:int 							=       self._nlp, []

       for entry in self._reader:
             a						:List[Any] 							=       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:
             a						:Optional[Any] 							=       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    )







"""simple docstring"""





import datasets


_a														=		"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

_a														=		"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

_a														=		"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,     _KWARGS_DESCRIPTION	)
class     _UpperCAmelCase(    datasets.Metric	):



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''),
							        })				,		codebase_urls=[]				,		reference_urls=[]				,		format='''numpy'''				,		)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							return {"accuracy": simple_accuracy(__a				,		__a)}






