"""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   					=    True
except (ImportError, AttributeError):
						_A   					=    object




						def 						SCREAMING_SNAKE_CASE					(  *__UpperCAmelCase  ,					**__UpperCAmelCase       )							->   Optional[int]:
										pass

						_A   					=    False


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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				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(__UpperCAmelCase  ,					args.host  ,					args.port  ,					args.workers       )

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@staticmethod
					def 			lowerCAmelCase_					(						_snake_case			:				ArgumentParser		)							->       int:
									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=_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			:				Dict   , _snake_case			:				Pipeline   , _snake_case			:				str   , _snake_case			:				int   , _snake_case			:				int		)							->       Optional[int]:
									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=_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 			lowerCAmelCase_					(						self			:				int		)							->       Optional[int]:
									run(self._app   , host=self.host   , port=self.port   , workers=self.workers		)



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



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				str = Body(_snake_case   , embed=_snake_case		)   , _snake_case			:				bool = Body(_snake_case   , embed=_snake_case		)		)							->       List[str]:
									try:
													SCREAMING_SNAKE_CASE__		  =				self._pipeline.tokenizer.tokenize(_snake_case		)

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



					def 			lowerCAmelCase_					(						self			:				str   , _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]:
									try:
													SCREAMING_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=500   , detail={"model": "", "error": str(_snake_case		)}		)



					async def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				Dict=Body(_snake_case   , embed=_snake_case		)		)							->       List[Any]:

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

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





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




