import math

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						float:




       '''simple docstring'''






       if (
           not isinstance(lowerCAmelCase_       ,					(int, float)   )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError('power_factor must be a valid float value between -1 and 1.'   )
       return apparent_power * power_factor

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						float:




       '''simple docstring'''






       if (
           not isinstance(lowerCAmelCase_       ,					(int, float)   )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError('power_factor must be a valid float value between -1 and 1.'   )
       return apparent_power * math.sqrt(1 - power_factor**2   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
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 							UpperCamelCase				(    *lowerCAmelCase_       ,					**lowerCAmelCase_   )		->						List[Any]:




             '''simple docstring'''






             pass





      UpperCAmelCase_								=							False


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

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _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      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     dict





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]
     _SCREAMING_SNAKE_CASE     :     Optional[List[int]]





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     str





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any





class      lowerCAmelCase						(					_a			):



     @staticmethod
     def 			a__  (					lowerCAmelCase__       ):
            _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				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _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 			a__  (					self       ):
            run(self._app				,      host=self.host				,      port=self.port				,      workers=self.workers       )



     def 			a__  (					self       ):
            return ServeModelInfoResult(infos=vars(self._pipeline.model.config       )       )



     def 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):
            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 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      ):
            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 			a__  (					self				,      lowerCAmelCase__=Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):

            # 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__       )}       )