




'''simple docstring'''


from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

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


try:
  from fastapi import Body, FastAPI, HTTPException
  from fastapi.routing import APIRoute
  from pydantic import BaseModel
  from starlette.responses import JSONResponse
  from uvicorn import run

  UpperCAmelCase_					=   True
except (ImportError, AttributeError):
  UpperCAmelCase_					=   object


  def     SCREAMING_SNAKE_CASE							(   *a_						:				str					,       **a_						:				Dict      ):
    pass

  UpperCAmelCase_					=   False


UpperCAmelCase_					=   logging.get_logger("transformers-cli/serving")


def     SCREAMING_SNAKE_CASE							(   a_						:				int      ):
  __a								=       pipeline(
      task=args.task					,       model=args.model if args.model else None					,       config=args.config					,       tokenizer=args.tokenizer					,       device=args.device					,       )
  return ServeCommand(__lowerCAmelCase					,       args.host					,       args.port					,       args.workers      )





class  __lowercase    (	_a			):
   _a    						=   42





class  __lowercase    (	_a			):
   _a    						=   42
   _a    						=   42





class  __lowercase    (	_a			):
   _a    						=   42





class  __lowercase    (	_a			):
   _a    						=   42





class  __lowercase    (	_a			):







   @staticmethod
   def 		UpperCamelCase__      (						UpperCamelCase   )					->     List[str]:
     __a								=       parser.add_parser(
         'serve'   ,				help='CLI tool to run inference requests through REST and GraphQL endpoints.'   )
     serve_parser.add_argument(
         '--task'   ,				type=__lowerCamelCase   ,				choices=get_supported_tasks()   ,				help='The task to run the pipeline on'   ,				)
     serve_parser.add_argument('--host'   ,				type=__lowerCamelCase   ,				default='localhost'   ,				help='Interface the server will listen on.'   )
     serve_parser.add_argument('--port'   ,				type=__lowerCamelCase   ,				default=8888   ,				help='Port the serving will listen to.'   )
     serve_parser.add_argument('--workers'   ,				type=__lowerCamelCase   ,				default=1   ,				help='Number of http workers'   )
     serve_parser.add_argument('--model'   ,				type=__lowerCamelCase   ,				help='Model\'s name or path to stored model.'   )
     serve_parser.add_argument('--config'   ,				type=__lowerCamelCase   ,				help='Model\'s config name or path to stored model.'   )
     serve_parser.add_argument('--tokenizer'   ,				type=__lowerCamelCase   ,				help='Tokenizer name to use.'   )
     serve_parser.add_argument(
         '--device'   ,				type=__lowerCamelCase   ,				default=-1   ,				help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'   ,				)
     serve_parser.set_defaults(func=__lowerCamelCase   )







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Any:
     __a								=       pipeline

     __a								=       host
     __a								=       port
     __a								=       workers

     if not _serve_dependencies_installed:
       raise RuntimeError(
           'Using serve command requires FastAPI and uvicorn. '
           'Please install transformers with [serving]: pip install "transformers[serving]".'
           'Or install FastAPI and uvicorn separately.'   )
     else:
       logger.info(f"Serving model over {host}:{port}"   )
       __a								=       FastAPI(
           routes=[
               APIRoute(
                   '/'   ,				self.model_info   ,				response_model=__lowerCamelCase   ,				response_class=__lowerCamelCase   ,				methods=['GET']   ,				),
               APIRoute(
                   '/tokenize'   ,				self.tokenize   ,				response_model=__lowerCamelCase   ,				response_class=__lowerCamelCase   ,				methods=['POST']   ,				),
               APIRoute(
                   '/detokenize'   ,				self.detokenize   ,				response_model=__lowerCamelCase   ,				response_class=__lowerCamelCase   ,				methods=['POST']   ,				),
               APIRoute(
                   '/forward'   ,				self.forward   ,				response_model=__lowerCamelCase   ,				response_class=__lowerCamelCase   ,				methods=['POST']   ,				),
           ]   ,				timeout=600   ,				)







   def 		UpperCamelCase__      (						self   )					->     str:
     run(self._app   ,				host=self.host   ,				port=self.port   ,				workers=self.workers   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     return ServeModelInfoResult(infos=vars(self._pipeline.model.config   )   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase = Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   ,				UpperCamelCase = Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   )					->     Any:
     try:
       __a								=       self._pipeline.tokenizer.tokenize(__lowerCamelCase   )

       if return_ids:
         __a								=       self._pipeline.tokenizer.convert_tokens_to_ids(__lowerCamelCase   )
         return ServeTokenizeResult(tokens=__lowerCamelCase   ,				tokens_ids=__lowerCamelCase   )
       else:
         return ServeTokenizeResult(tokens=__lowerCamelCase   )

     except Exception as e:
       raise HTTPException(status_code=500   ,				detail={'model': '', 'error': str(__lowerCamelCase   )}   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase = Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   ,				UpperCamelCase = Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   ,				UpperCamelCase = Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   ,				)					->     Any:
     try:
       __a								=       self._pipeline.tokenizer.decode(__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   )
       return ServeDeTokenizeResult(model=''   ,				text=__lowerCamelCase   )
     except Exception as e:
       raise HTTPException(status_code=500   ,				detail={'model': '', 'error': str(__lowerCamelCase   )}   )







   async def 		UpperCamelCase__      (						self   ,				UpperCamelCase=Body(__lowerCamelCase   ,				embed=__lowerCamelCase   )   )					->     Optional[int]:

     # Check we don't have empty string
     if len(__lowerCamelCase   ) == 0:
       return ServeForwardResult(output=[]   ,				attention=[]   )

     try:
       # Forward through the model
       __a								=       self._pipeline(__lowerCamelCase   )
       return ServeForwardResult(output=__lowerCamelCase   )
     except Exception as e:
       raise HTTPException(500   ,				{'error': str(__lowerCamelCase   )}   )









from scipy.stats import pearsonr

import datasets


__magic_name__  :  Union[str, Any]					=       """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__magic_name__  :  Tuple					=       """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__magic_name__  :  int					=       """
@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, Ilhan 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, Antonio 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     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       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.pearsonr.html''']       ,						)



     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict=False						):



       """simple docstring"""
       if return_pvalue:
         lowerCAmelCase__										=      pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)[0]						)}

