
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     :	Optional[int]    = True
except (ImportError, AttributeError):
					A     :	Any    = object


					def     __lowerCAmelCase			(			*a__						,    **a__  )       ->  Any:
									pass






					A     :	Optional[Any]    = False


A     :	Optional[int]    = logging.get_logger('transformers-cli/serving')


def     __lowerCAmelCase			(			a__  )       ->  Any:
				__a										=    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			):
				snake_case_						   =    42







class 			__A( a			):
				snake_case_						   =    42
				snake_case_						   =    42







class 			__A( a			):
				snake_case_						   =    42







class 			__A( a			):
				snake_case_						   =    42







class 			__A( a			):




				@staticmethod
				def 				SCREAMING_SNAKE_CASE_		(     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    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=8_888		,     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		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    pipeline

								__a										=    host
								__a										=    port
								__a										=    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}"""				)
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case = Body(_snake_case		,     embed=_snake_case				)		,     _snake_case = Body(_snake_case		,     embed=_snake_case				)				)    ->      str:


								'''simple docstring'''




								try:
												__a										=    self._pipeline.tokenizer.tokenize(_snake_case				)

												if return_ids:
																__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case = Body(_snake_case		,     embed=_snake_case				)		,     _snake_case = Body(_snake_case		,     embed=_snake_case				)		,     _snake_case = Body(_snake_case		,     embed=_snake_case				)		,     )    ->      str:


								'''simple docstring'''




								try:
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=Body(_snake_case		,     embed=_snake_case				)				)    ->      List[str]:


								'''simple docstring'''




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

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

from math import ceil


def     __lowerCAmelCase			(			a__ = 1001  )       ->  int:
				__a										=    1

				for i in range(1						,    int(ceil(n / 2.0  )  )  ):
								__a										=    2 * i + 1
								__a										=    2 * i
								__a										=    total + 4 * odd**2 - 6 * even

				return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															A     :	List[Any]    = int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')