
"""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

   _A     :			Dict  						=			True
except (ImportError, AttributeError):
   _A     :			Optional[int]  						=			object


   def     __magic_name__      ( *__snake_case							:			Optional[int]	,					**__snake_case							:			List[Any]    )					->							Any:
          pass
   _A     :			str  						=			False


_A     :			Dict  						=			logging.get_logger("""transformers-cli/serving""")


def     __magic_name__      ( __snake_case							:			Namespace    )					->							Any:
       lowercase       :							Union[str, Any]									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       return ServeCommand(__snake_case	,					args.host	,					args.port	,					args.workers    )

class 						a__		( a_		):
   __lowerCAmelCase				=							42


class 						a__		( a_		):
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42


class 						a__		( a_		):
   __lowerCAmelCase				=							42


class 						a__		( a_		):
   __lowerCAmelCase				=							42


class 						a__		( a_		):
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							str									=				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=8_888      ,       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							):
          lowercase       :							Dict									=				pipeline

          lowercase       :							Optional[int]									=				host
          lowercase       :							Any									=				port
          lowercase       :							Union[str, Any]									=				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}"""							)
                 lowercase       :							Optional[Any]									=				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=600      ,       )
   def      __magic_name__     (     self							):
          run(self._app      ,       host=self.host      ,       port=self.port      ,       workers=self.workers							)
   def      __magic_name__     (     self							):
          return ServeModelInfoResult(infos=vars(self._pipeline.model.config							)							)
   def      __magic_name__     (     self      ,       _a = Body(_a      ,       embed=_a							)      ,       _a = Body(_a      ,       embed=_a							)							):
          try:
                 lowercase       :							int									=				self._pipeline.tokenizer.tokenize(_a							)

                 if return_ids:
                        lowercase       :							int									=				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=500      ,       detail={"model": "", "error": str(_a							)}							)
   def      __magic_name__     (     self      ,       _a = Body(_a      ,       embed=_a							)      ,       _a = Body(_a      ,       embed=_a							)      ,       _a = Body(_a      ,       embed=_a							)      ,       ):
          try:
                 lowercase       :							Tuple									=				self._pipeline.tokenizer.decode(_a      ,       _a      ,       _a							)
                 return ServeDeTokenizeResult(model=""      ,       text=_a							)
          except Exception as e:
                 raise HTTPException(status_code=500      ,       detail={"model": "", "error": str(_a							)}							)

   async def      __magic_name__     (     self      ,       _a=Body(_a      ,       embed=_a							)							):

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

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




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


