


"""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__    :							Tuple            =					True
except (ImportError, AttributeError):
		a__    :							int            =					object




		def        UpperCAmelCase__	(*lowerCAmelCase_  ,						**lowerCAmelCase_       ):




							'''simple docstring'''


							pass



		a__    :							str            =					False


a__    :							str            =					logging.get_logger('''transformers-cli/serving''')




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_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(lowerCAmelCase_  ,						args.host  ,						args.port  ,						args.workers       )




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  dict




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  List[str]
	snake_case__    :  Optional[List[int]]




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  str




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





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




	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    Pipeline      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    int       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    pipeline

						__SCREAMING_SNAKE_CASE						       =    host
						__SCREAMING_SNAKE_CASE						       =    port
						__SCREAMING_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}"""       )
											__SCREAMING_SNAKE_CASE						       =    FastAPI(
											    routes=[
											        APIRoute(
											            "/"      ,      self.model_info      ,      response_model=UpperCAmelCase__      ,      response_class=UpperCAmelCase__      ,      methods=["GET"]      ,      ),
											        APIRoute(
											            "/tokenize"      ,      self.tokenize      ,      response_model=UpperCAmelCase__      ,      response_class=UpperCAmelCase__      ,      methods=["POST"]      ,      ),
											        APIRoute(
											            "/detokenize"      ,      self.detokenize      ,      response_model=UpperCAmelCase__      ,      response_class=UpperCAmelCase__      ,      methods=["POST"]      ,      ),
											        APIRoute(
											            "/forward"      ,      self.forward      ,      response_model=UpperCAmelCase__      ,      response_class=UpperCAmelCase__      ,      methods=["POST"]      ,      ),
											    ]      ,      timeout=6_0_0      ,      )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     int:
						run(self._app      ,      host=self.host      ,      port=self.port      ,      workers=self.workers       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Any:
						return ServeModelInfoResult(infos=vars(self._pipeline.model.config       )       )




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    str = Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )      ,      UpperCAmelCase__      :    bool = Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )       )	->     List[Any]:
						try:
											__SCREAMING_SNAKE_CASE						       =    self._pipeline.tokenizer.tokenize(UpperCAmelCase__       )

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

						except Exception as e:
											raise HTTPException(status_code=5_0_0      ,      detail={"model": "", "error": str(UpperCAmelCase__       )}       )




	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    List[int] = Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )      ,      UpperCAmelCase__      :    bool = Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )      ,      UpperCAmelCase__      :    bool = Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )      ,      )	->     Optional[int]:
						try:
											__SCREAMING_SNAKE_CASE						       =    self._pipeline.tokenizer.decode(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )
											return ServeDeTokenizeResult(model=""      ,      text=UpperCAmelCase__       )
						except Exception as e:
											raise HTTPException(status_code=5_0_0      ,      detail={"model": "", "error": str(UpperCAmelCase__       )}       )




	async def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    str=Body(UpperCAmelCase__      ,      embed=UpperCAmelCase__       )       )	->     Union[str, Any]:

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

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




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if not nums:
										return 0
					__SCREAMING_SNAKE_CASE						       =    nums[0]
					__SCREAMING_SNAKE_CASE						       =    0
					for num in nums[1:]:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (
										    max_excluding + num,
										    max(lowerCAmelCase_  ,						lowerCAmelCase_       ),
										)
					return max(lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
