


'''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   :    List[Any]											= True
except (ImportError, AttributeError):
  __A   :    List[str]											= object







  def 							UpperCamelCase_     (       *A__   :  Tuple				,       **A__   :  Optional[Any]   ):
    '''simple docstring'''





    pass
  __A   :    Union[str, Any]											= False


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







def 							UpperCamelCase_     (       A__   :  Namespace   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  pipeline(
      task=args.task				,       model=args.model if args.model else None				,       config=args.config				,       tokenizer=args.tokenizer				,       device=args.device				,       )
  return ServeCommand(A__				,       args.host				,       args.port				,       args.workers   )






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42
    lowercase  					=   42







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



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

    def __init__(  self       :    int       ,		lowerCamelCase       :    Pipeline       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    Optional[Any]:
      lowerCAmelCase_     : Tuple     =  pipeline

      lowerCAmelCase_     : Tuple     =  host
      lowerCAmelCase_     : Union[str, Any]     =  port
      lowerCAmelCase_     : List[str]     =  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_     : Any     =  FastAPI(
            routes=[
                APIRoute(
                    """/"""       ,		self.model_info       ,		response_model=lowerCamelCase       ,		response_class=lowerCamelCase       ,		methods=["""GET"""]       ,		),
                APIRoute(
                    """/tokenize"""       ,		self.tokenize       ,		response_model=lowerCamelCase       ,		response_class=lowerCamelCase       ,		methods=["""POST"""]       ,		),
                APIRoute(
                    """/detokenize"""       ,		self.detokenize       ,		response_model=lowerCamelCase       ,		response_class=lowerCamelCase       ,		methods=["""POST"""]       ,		),
                APIRoute(
                    """/forward"""       ,		self.forward       ,		response_model=lowerCamelCase       ,		response_class=lowerCamelCase       ,		methods=["""POST"""]       ,		),
            ]       ,		timeout=6_00       ,		)

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

    def __lowercase  (  self       :    Any						)  ->    Union[str, Any]:
      return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    str = Body(lowerCamelCase       ,		embed=lowerCamelCase						)       ,		lowerCamelCase       :    bool = Body(lowerCamelCase       ,		embed=lowerCamelCase						)						)  ->    Dict:
      try:
        lowerCAmelCase_     : List[Any]     =  self._pipeline.tokenizer.tokenize(lowerCamelCase						)

        if return_ids:
          lowerCAmelCase_     : Optional[int]     =  self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase						)
          return ServeTokenizeResult(tokens=lowerCamelCase       ,		tokens_ids=lowerCamelCase						)
        else:
          return ServeTokenizeResult(tokens=lowerCamelCase						)

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

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    List[int] = Body(lowerCamelCase       ,		embed=lowerCamelCase						)       ,		lowerCamelCase       :    bool = Body(lowerCamelCase       ,		embed=lowerCamelCase						)       ,		lowerCamelCase       :    bool = Body(lowerCamelCase       ,		embed=lowerCamelCase						)       ,		)  ->    Optional[Any]:
      try:
        lowerCAmelCase_     : Dict     =  self._pipeline.tokenizer.decode(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						)
        return ServeDeTokenizeResult(model=""""""       ,		text=lowerCamelCase						)
      except Exception as e:
        raise HTTPException(status_code=5_00       ,		detail={"""model""": """""", """error""": str(lowerCamelCase						)}						)





    async def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    Optional[Any]=Body(lowerCamelCase       ,		embed=lowerCamelCase						)						)  ->    Any:

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

      try:
        # Forward through the model
        lowerCAmelCase_     : Dict     =  self._pipeline(lowerCamelCase						)
        return ServeForwardResult(output=lowerCamelCase						)
      except Exception as e:
        raise HTTPException(5_00       ,		{"""error""": str(lowerCamelCase						)}						)







'''simple docstring'''






from typing import Union

import fire
import torch
from tqdm import tqdm







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  str = "cpu"				,       A__   :  Union[str, None] = None   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  torch.load(A__				,       map_location=A__   )
  for k, v in tqdm(state_dict.items()   ):
    if not isinstance(A__				,       torch.Tensor   ):
      raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""   )
    lowerCAmelCase_     : List[str]     =  v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_     : List[str]     =  src_path
  torch.save(A__				,       A__   )


if __name__ == "__main__":
  fire.Fire(convert)



