

from argparse import ArgumentParser

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


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


def 	_A  (					A__   ):






 """simple docstring"""



 if not path:
  return "pipe"

 for ext in PipelineDataFormat.SUPPORTED_FORMATS:
  if path.endswith(_UpperCamelCase   ):
   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  (					A__   ):






 """simple docstring"""



 __lowercase													=				pipeline(
     task=args.task  ,       model=args.model if args.model else None  ,       config=args.config  ,       tokenizer=args.tokenizer  ,       device=args.device  ,       )
 __lowercase													=				try_infer_format_from_ext(args.input   ) if args.format == '''infer''' else args.format
 __lowercase													=				PipelineDataFormat.from_str(
     format=_UpperCamelCase  ,       output_path=args.output  ,       input_path=args.input  ,       column=args.column if args.column else nlp.default_input_names  ,       overwrite=args.overwrite  ,       )
 return RunCommand(_UpperCamelCase  ,       _UpperCamelCase   )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  Pipeline      ,lowercase__     :  PipelineDataFormat    ):
   __lowercase													=				nlp
   __lowercase													=				reader






  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase						,			__lowercase													=				self._nlp, []

   for entry in self._reader:
    __lowercase													=				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:
    __lowercase													=				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__    )




'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






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




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

