
from scipy.stats import spearmanr

import datasets


__lowerCAmelCase       :      Optional[Any]         =		"\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

__lowerCAmelCase       :      List[Any]         =		"\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

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


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



      """simple docstring"""







      def 		snake_case_      (   self		:							str					):
          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 		snake_case_      (   self		:							Dict					,							_snake_case		:							Dict					,							_snake_case		:							Union[str, Any]					,							_snake_case		:							Tuple=False					):
          __lowercase		:      Optional[Any]						     =							spearmanr(_snake_case					,							_snake_case					)
          if return_pvalue:
              return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
          else:
              return {"spearmanr": results[0]}






from argparse import ArgumentParser

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


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


def     lowercase	(    a ):



		'''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 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				:str				     =						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 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				:Any				     =						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 _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						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:
						SCREAMING_SNAKE_CASE_				:Dict				     =						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)






