





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 	_SCREAMING_SNAKE_CASE		(      *SCREAMING_SNAKE_CASE		:int   ,   **SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   int:
       pass







      _UpperCAmelCase             =							False


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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Namespace   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Optional[Any]      =       pipeline(
     task=args.task   ,   model=args.model if args.model else None   ,   config=args.config   ,   tokenizer=args.tokenizer   ,   device=args.device   ,   )
 return ServeCommand(SCREAMING_SNAKE_CASE   ,   args.host   ,   args.port   ,   args.workers   )






class        snake_case_    (		__lowercase						):
  A_      							=   42






class        snake_case_    (		__lowercase						):
  A_      							=   42
  A_      							=   42






class        snake_case_    (		__lowercase						):
  A_      							=   42






class        snake_case_    (		__lowercase						):
  A_      							=   42






class        snake_case_    (		__lowercase						):






  @staticmethod
  def 				UpperCAmelCase__						(							_snake_case			:					ArgumentParser      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       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=8888       ,  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			:					Any       ,  _snake_case			:					Pipeline       ,  _snake_case			:					str       ,  _snake_case			:					int       ,  _snake_case			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       pipeline

   __lowerCAmelCase					:	Any      =       host
   __lowerCAmelCase					:	List[str]      =       port
   __lowerCAmelCase					:	Dict      =       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					:	Optional[int]      =       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=600       ,  )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Optional[Any]:






   '''simple docstring'''

   run(self._app       ,  host=self.host       ,  port=self.port       ,  workers=self.workers      )






  def 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					str = Body(_snake_case       ,  embed=_snake_case      )       ,  _snake_case			:					bool = Body(_snake_case       ,  embed=_snake_case      )      )->int:






   '''simple docstring'''

   try:
    __lowerCAmelCase					:	Optional[int]      =       self._pipeline.tokenizer.tokenize(_snake_case      )

    if return_ids:
     __lowerCAmelCase					:	Dict      =       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=500       ,  detail={"""model""": """""", """error""": str(_snake_case      )}      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					List[int] = Body(_snake_case       ,  embed=_snake_case      )       ,  _snake_case			:					bool = Body(_snake_case       ,  embed=_snake_case      )       ,  _snake_case			:					bool = Body(_snake_case       ,  embed=_snake_case      )       ,  )->Optional[int]:






   '''simple docstring'''

   try:
    __lowerCAmelCase					:	str      =       self._pipeline.tokenizer.decode(_snake_case       ,  _snake_case       ,  _snake_case      )
    return ServeDeTokenizeResult(model=""""""       ,  text=_snake_case      )
   except Exception as e:
    raise HTTPException(status_code=500       ,  detail={"""model""": """""", """error""": str(_snake_case      )}      )






  async def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					Optional[int]=Body(_snake_case       ,  embed=_snake_case      )      )->List[str]:






   '''simple docstring'''

   if len(_snake_case      ) == 0:
    return ServeForwardResult(output=[]       ,  attention=[]      )

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        snake_case_    (		__lowercase						):
  A_      							=   'biogpt'






  def __init__(							self			:					int       ,  _snake_case			:					Any=42384       ,  _snake_case			:					Any=1024       ,  _snake_case			:					List[Any]=24       ,  _snake_case			:					Any=16       ,  _snake_case			:					List[str]=4096       ,  _snake_case			:					Dict="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					Tuple=1024       ,  _snake_case			:					Tuple=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Any=0.0       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					str=1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					str=2       ,  **_snake_case			:					Union[str, Any]       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       max_position_embeddings
   __lowerCAmelCase					:	str      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	Optional[Any]      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	Any      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Any      =       initializer_range
   __lowerCAmelCase					:	int      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[int]      =       scale_embedding
   __lowerCAmelCase					:	List[Any]      =       use_cache
   __lowerCAmelCase					:	str      =       layerdrop
   __lowerCAmelCase					:	Dict      =       activation_dropout
   super().__init__(pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )