





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		:  Any         =	True
except (ImportError, AttributeError):
				UpperCAmelCase		:  int         =	object







				def 					_SCREAMING_SNAKE_CASE							(  *a   ,		**a    ) -> int:
									pass




				UpperCAmelCase		:  Optional[int]         =	False


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







def 					_SCREAMING_SNAKE_CASE							(  a    ) -> Dict:
					__A      :		List[str]         =							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 		_A(   snake_case_  ):


				"""simple docstring"""


				UpperCamelCase       :    str          =      42





class 		_A(   snake_case_  ):


				"""simple docstring"""


				UpperCamelCase       :    Union[str, Any]          =      42
				UpperCamelCase       :    Union[str, Any]          =      42





class 		_A(   snake_case_  ):


				"""simple docstring"""


				UpperCamelCase       :    str          =      42





class 		_A(   snake_case_  ):


				"""simple docstring"""


				UpperCamelCase       :    Tuple          =      42





class 		_A(   snake_case_  ):


				"""simple docstring"""


				@staticmethod
				def    UpperCAmelCase_						(     _A	):
									__A      :		Union[str, Any]         =							parser.add_parser(
									    'serve'	,   help='CLI tool to run inference requests through REST and GraphQL endpoints.'	)
									serve_parser.add_argument(
									    '--task'	,   type=_A	,   choices=get_supported_tasks()	,   help='The task to run the pipeline on'	,   )
									serve_parser.add_argument('--host'	,   type=_A	,   default='localhost'	,   help='Interface the server will listen on.'	)
									serve_parser.add_argument('--port'	,   type=_A	,   default=8888	,   help='Port the serving will listen to.'	)
									serve_parser.add_argument('--workers'	,   type=_A	,   default=1	,   help='Number of http workers'	)
									serve_parser.add_argument('--model'	,   type=_A	,   help='Model\'s name or path to stored model.'	)
									serve_parser.add_argument('--config'	,   type=_A	,   help='Model\'s config name or path to stored model.'	)
									serve_parser.add_argument('--tokenizer'	,   type=_A	,   help='Tokenizer name to use.'	)
									serve_parser.add_argument(
									    '--device'	,   type=_A	,   default=-1	,   help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'	,   )
									serve_parser.set_defaults(func=_A	)



				def __init__(     self	,   _A	,   _A	,   _A	,   _A	):
									__A      :		Dict         =							pipeline

									__A      :		List[Any]         =							host
									__A      :		Any         =							port
									__A      :		Tuple         =							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      :		int         =							FastAPI(
														    routes=[
														        APIRoute(
														            '/'	,   self.model_info	,   response_model=_A	,   response_class=_A	,   methods=['GET']	,   ),
														        APIRoute(
														            '/tokenize'	,   self.tokenize	,   response_model=_A	,   response_class=_A	,   methods=['POST']	,   ),
														        APIRoute(
														            '/detokenize'	,   self.detokenize	,   response_model=_A	,   response_class=_A	,   methods=['POST']	,   ),
														        APIRoute(
														            '/forward'	,   self.forward	,   response_model=_A	,   response_class=_A	,   methods=['POST']	,   ),
														    ]	,   timeout=600	,   )



				def    UpperCAmelCase_						(     self	):
									run(self._app	,   host=self.host	,   port=self.port	,   workers=self.workers	)



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



				def    UpperCAmelCase_						(     self	,   _A = Body(_A	,   embed=_A	)	,   _A = Body(_A	,   embed=_A	)	):
									try:
														__A      :		List[Any]         =							self._pipeline.tokenizer.tokenize(_A	)

														if return_ids:
																			__A      :		str         =							self._pipeline.tokenizer.convert_tokens_to_ids(_A	)
																			return ServeTokenizeResult(tokens=_A	,   tokens_ids=_A	)
														else:
																			return ServeTokenizeResult(tokens=_A	)

									except Exception as e:
														raise HTTPException(status_code=500	,   detail={'model': '', 'error': str(_A	)}	)



				def    UpperCAmelCase_						(     self	,   _A = Body(_A	,   embed=_A	)	,   _A = Body(_A	,   embed=_A	)	,   _A = Body(_A	,   embed=_A	)	,   ):
									try:
														__A      :		Tuple         =							self._pipeline.tokenizer.decode(_A	,   _A	,   _A	)
														return ServeDeTokenizeResult(model=''	,   text=_A	)
									except Exception as e:
														raise HTTPException(status_code=500	,   detail={'model': '', 'error': str(_A	)}	)





				async def    UpperCAmelCase_						(     self	,   _A=Body(_A	,   embed=_A	)	):

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

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






from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


if __name__ == "__main__":
			print(F"""{solution() = }""")


