






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

 snake_case__   :						Any													=   True
except (ImportError, AttributeError):
 snake_case__   :						Dict													=   object






 def      _snake_case     (*__lowercase     ,   **__lowercase):
    pass

 snake_case__   :						Union[str, Any]													=   False


snake_case__   :						Optional[int]													=   logging.get_logger("""transformers-cli/serving""")






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  pipeline(
       task=args.task     ,   model=args.model if args.model else None     ,   config=args.config     ,   tokenizer=args.tokenizer     ,   device=args.device     ,   )
   return ServeCommand(__lowercase     ,   args.host     ,   args.port     ,   args.workers)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42
  A_						       = 42




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





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




  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  pipeline

     UpperCamelCase_						=  host
     UpperCamelCase_						=  port
     UpperCamelCase_						=  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}"""			)
        UpperCamelCase_						=  FastAPI(
            routes=[
                APIRoute(
                    '/'		,		self.model_info		,		response_model=_UpperCAmelCase		,		response_class=_UpperCAmelCase		,		methods=['GET']		,		),
                APIRoute(
                    '/tokenize'		,		self.tokenize		,		response_model=_UpperCAmelCase		,		response_class=_UpperCAmelCase		,		methods=['POST']		,		),
                APIRoute(
                    '/detokenize'		,		self.detokenize		,		response_model=_UpperCAmelCase		,		response_class=_UpperCAmelCase		,		methods=['POST']		,		),
                APIRoute(
                    '/forward'		,		self.forward		,		response_model=_UpperCAmelCase		,		response_class=_UpperCAmelCase		,		methods=['POST']		,		),
            ]		,		timeout=600		,		)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     run(self._app		,		host=self.host		,		port=self.port		,		workers=self.workers			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return ServeModelInfoResult(infos=vars(self._pipeline.model.config			)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase = Body(_UpperCAmelCase		,		embed=_UpperCAmelCase			)		,		_UpperCAmelCase = Body(_UpperCAmelCase		,		embed=_UpperCAmelCase			)			)      ->			Union[str, Any]:
     try:
        UpperCamelCase_						=  self._pipeline.tokenizer.tokenize(_UpperCAmelCase			)

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

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




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




  async def  _UpperCAmelCase  (  self		,		_UpperCAmelCase=Body(_UpperCAmelCase		,		embed=_UpperCAmelCase			)			)      ->			Tuple:

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

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









"""simple docstring"""
import numpy as np

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return 1 / (1 + np.exp(-vector   ))

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return vector * sigmoid(__snake_case   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

