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__				= True
except (ImportError, AttributeError):
     snake_case__				= object
     def       __magic_name__(  *__UpperCAmelCase ,						**__UpperCAmelCase		)						->    str:



           '''simple docstring'''

           pass




     snake_case__				= False


snake_case__				= logging.get_logger('transformers-cli/serving')
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				pipeline(
          task=args.task ,						model=args.model if args.model else None ,						config=args.config ,						tokenizer=args.tokenizer ,						device=args.device ,						)
      return ServeCommand(__UpperCAmelCase ,						args.host ,						args.port ,						args.workers		)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42
     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @staticmethod
     def 		UpperCamelCase_						(					A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				parser.add_parser(
               '''serve'''				,				help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''						)
           serve_parser.add_argument(
               '''--task'''				,				type=A_				,				choices=get_supported_tasks()				,				help='''The task to run the pipeline on'''				,				)
           serve_parser.add_argument('''--host'''				,				type=A_				,				default='''localhost'''				,				help='''Interface the server will listen on.'''						)
           serve_parser.add_argument('''--port'''				,				type=A_				,				default=88_88				,				help='''Port the serving will listen to.'''						)
           serve_parser.add_argument('''--workers'''				,				type=A_				,				default=1				,				help='''Number of http workers'''						)
           serve_parser.add_argument('''--model'''				,				type=A_				,				help='''Model\'s name or path to stored model.'''						)
           serve_parser.add_argument('''--config'''				,				type=A_				,				help='''Model\'s config name or path to stored model.'''						)
           serve_parser.add_argument('''--tokenizer'''				,				type=A_				,				help='''Tokenizer name to use.'''						)
           serve_parser.add_argument(
               '''--device'''				,				type=A_				,				default=-1				,				help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''				,				)
           serve_parser.set_defaults(func=A_						)



     def __init__(					self				,				A_				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				pipeline

           _lowerCamelCase			    =				host
           _lowerCamelCase			    =				port
           _lowerCamelCase			    =				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}'						)
                 _lowerCamelCase			    =				FastAPI(
                     routes=[
                         APIRoute(
                             '''/'''				,				self.model_info				,				response_model=A_				,				response_class=A_				,				methods=['''GET''']				,				),
                         APIRoute(
                             '''/tokenize'''				,				self.tokenize				,				response_model=A_				,				response_class=A_				,				methods=['''POST''']				,				),
                         APIRoute(
                             '''/detokenize'''				,				self.detokenize				,				response_model=A_				,				response_class=A_				,				methods=['''POST''']				,				),
                         APIRoute(
                             '''/forward'''				,				self.forward				,				response_model=A_				,				response_class=A_				,				methods=['''POST''']				,				),
                     ]				,				timeout=6_00				,				)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           run(self._app				,				host=self.host				,				port=self.port				,				workers=self.workers						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)



     def 		UpperCamelCase_						(					self				,				A_ = Body(A_				,				embed=A_						)				,				A_ = Body(A_				,				embed=A_						)						) ->				str:






           """simple docstring"""
           try:
                 _lowerCamelCase			    =				self._pipeline.tokenizer.tokenize(A_						)

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

           except Exception as e:
                 raise HTTPException(status_code=5_00				,				detail={'''model''': '''''', '''error''': str(A_						)}						)



     def 		UpperCamelCase_						(					self				,				A_ = Body(A_				,				embed=A_						)				,				A_ = Body(A_				,				embed=A_						)				,				A_ = Body(A_				,				embed=A_						)				,				) ->				int:






           """simple docstring"""
           try:
                 _lowerCamelCase			    =				self._pipeline.tokenizer.decode(A_				,				A_				,				A_						)
                 return ServeDeTokenizeResult(model=''''''				,				text=A_						)
           except Exception as e:
                 raise HTTPException(status_code=5_00				,				detail={'''model''': '''''', '''error''': str(A_						)}						)





     async def 		UpperCamelCase_						(					self				,				A_=Body(A_				,				embed=A_						)						) ->				Any:






           """simple docstring"""

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

           try:
                 # Forward through the model
                 _lowerCamelCase			    =				self._pipeline(A_						)
                 return ServeForwardResult(output=A_						)
           except Exception as e:
                 raise HTTPException(5_00				,				{'''error''': str(A_						)}						)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)