


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

 _SCREAMING_SNAKE_CASE      :			List[str]      				=   True
except (ImportError, AttributeError):
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   object
 def       UpperCAmelCase__						(*UpperCamelCase_							,**UpperCamelCase_    ):



      """simple docstring"""






      pass

 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger("transformers-cli/serving")
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    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   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42
    __magic_name__        =		42






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42






class   A__ (   snake_case__     ):







    """simple docstring"""

    @staticmethod
    def 		a_      (	__snake_case   ):
         snake_case					=    parser.add_parser(
             '''serve'''      , help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''   )
         serve_parser.add_argument(
             '''--task'''      , type=__snake_case      , choices=get_supported_tasks()      , help='''The task to run the pipeline on'''      , )
         serve_parser.add_argument('''--host'''      , type=__snake_case      , default='''localhost'''      , help='''Interface the server will listen on.'''   )
         serve_parser.add_argument('''--port'''      , type=__snake_case      , default=8_8_8_8      , help='''Port the serving will listen to.'''   )
         serve_parser.add_argument('''--workers'''      , type=__snake_case      , default=1      , help='''Number of http workers'''   )
         serve_parser.add_argument('''--model'''      , type=__snake_case      , help='''Model\'s name or path to stored model.'''   )
         serve_parser.add_argument('''--config'''      , type=__snake_case      , help='''Model\'s config name or path to stored model.'''   )
         serve_parser.add_argument('''--tokenizer'''      , type=__snake_case      , help='''Tokenizer name to use.'''   )
         serve_parser.add_argument(
             '''--device'''      , type=__snake_case      , default=-1      , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''      , )
         serve_parser.set_defaults(func=__snake_case   )
    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pipeline

         snake_case					=    host
         snake_case					=    port
         snake_case					=    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}'''   )
              snake_case					=    FastAPI(
                  routes=[
                      APIRoute(
                          '''/'''      , self.model_info      , response_model=__snake_case      , response_class=__snake_case      , methods=['''GET''']      , ),
                      APIRoute(
                          '''/tokenize'''      , self.tokenize      , response_model=__snake_case      , response_class=__snake_case      , methods=['''POST''']      , ),
                      APIRoute(
                          '''/detokenize'''      , self.detokenize      , response_model=__snake_case      , response_class=__snake_case      , methods=['''POST''']      , ),
                      APIRoute(
                          '''/forward'''      , self.forward      , response_model=__snake_case      , response_class=__snake_case      , methods=['''POST''']      , ),
                  ]      , timeout=6_0_0      , )
    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      , __snake_case = Body(__snake_case      , embed=__snake_case   )      , __snake_case = Body(__snake_case      , embed=__snake_case   )   ):
         try:
              snake_case					=    self._pipeline.tokenizer.tokenize(__snake_case   )

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

         except Exception as e:
              raise HTTPException(status_code=5_0_0      , detail={'''model''': '''''', '''error''': str(__snake_case   )}   )
    def 		a_      (	self      , __snake_case = Body(__snake_case      , embed=__snake_case   )      , __snake_case = Body(__snake_case      , embed=__snake_case   )      , __snake_case = Body(__snake_case      , embed=__snake_case   )      , ):
         try:
              snake_case					=    self._pipeline.tokenizer.decode(__snake_case      , __snake_case      , __snake_case   )
              return ServeDeTokenizeResult(model=''''''      , text=__snake_case   )
         except Exception as e:
              raise HTTPException(status_code=5_0_0      , detail={'''model''': '''''', '''error''': str(__snake_case   )}   )




    async def 		a_      (	self      , __snake_case=Body(__snake_case      , embed=__snake_case   )   ):

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

         try:
              # Forward through the model
              snake_case					=    self._pipeline(__snake_case   )
              return ServeForwardResult(output=__snake_case   )
         except Exception as e:
              raise HTTPException(5_0_0      , {'''error''': str(__snake_case   )}   )








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE      :			Dict      				=   {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "VanForImageClassification",
     "VanModel",
     "VanPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			str      				=   _LazyModule(__name__, globals()["__file__"], _import_structure)




