


'''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

		__UpperCAmelCase       							=				True
except (ImportError, AttributeError):
		__UpperCAmelCase       							=				object


		def    _snake_case  (	*A					,       **A			)		->       str:
									pass






		__UpperCAmelCase       							=				False


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


def    _snake_case  (	A			)		->       Optional[int]:
							lowerCAmelCase__			  =      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 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  dict




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  List[str]
							lowercase__     :  Optional[List[int]]




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  str




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Any




class 						a__							(				a__    ):





							'''simple docstring'''






							@staticmethod
							def     __SCREAMING_SNAKE_CASE				(						lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__			  =      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   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      pipeline

														lowerCAmelCase__			  =      host
														lowerCAmelCase__			  =      port
														lowerCAmelCase__			  =      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__			  =      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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														run(self._app   ,       host=self.host   ,       port=self.port   ,       workers=self.workers  )







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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_ = Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )   ,       lowerCamelCase_ = Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )  )			->				List[Any]:
														try:
																					lowerCAmelCase__			  =      self._pipeline.tokenizer.tokenize(lowerCamelCase_  )

																					if return_ids:
																												lowerCAmelCase__			  =      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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_ = Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )   ,       lowerCamelCase_ = Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )   ,       lowerCamelCase_ = Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )   ,       )			->				Optional[Any]:
														try:
																					lowerCAmelCase__			  =      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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=Body(lowerCamelCase_   ,       embed=lowerCamelCase_  )  )			->				List[str]:

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

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






from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

 UpperCamelCase      :   Any			      =  int(input("enter the value to interpolate: "	)	)
 UpperCamelCase      :   List[Any]			      =  (value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  y[0][0]
 for i in range(1				,  _lowerCAmelCase	):
  summ += (ucal(_lowerCAmelCase				,  _lowerCAmelCase	) * y[0][i]) / math.factorial(_lowerCAmelCase	)

 print(F"""the value at {value} is {summ}"""	)


if __name__ == "__main__":
    main()
