






'''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		:		Tuple            = True
except (ImportError, AttributeError):
						_UpperCamelCase		:		List[Any]            = object
						def __UpperCAmelCase					( *A :       List[str]       ,    **A :       int						)  ->					str:
									pass







						_UpperCamelCase		:		int            = False


_UpperCamelCase		:		Dict            = logging.get_logger('transformers-cli/serving')
def __UpperCAmelCase					( A :       Namespace						)  ->					Union[str, Any]:
			UpperCAmelCase_	:    List[Any]	  =    pipeline(
			    task=args.task       ,    model=args.model if args.model else None       ,    config=args.config       ,    tokenizer=args.tokenizer       ,    device=args.device       ,    )
			return ServeCommand(__snake_case       ,    args.host       ,    args.port       ,    args.workers						)


class       snake_case__	( a_):
					a_								= 42






class       snake_case__	( a_):
					a_								= 42
					a_								= 42






class       snake_case__	( a_):
					a_								= 42






class       snake_case__	( a_):
					a_								= 42






class       snake_case__	( a_):




					@staticmethod
					def 						A	(   _A   :						Dict	)  ->				int:
								UpperCAmelCase_	:    Optional[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=88_88							,      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   :						List[Any]							,      _A   :						List[str]							,      _A   :						Dict							,      _A   :						int							,      _A   :						Tuple	)  ->				Dict:
								UpperCAmelCase_	:    str	  =    pipeline

								UpperCAmelCase_	:    Optional[int]	  =    host
								UpperCAmelCase_	:    str	  =    port
								UpperCAmelCase_	:    Optional[int]	  =    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}"	)
											UpperCAmelCase_	:    Any	  =    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=6_00							,      )




					def 						A	(   self   :						Tuple	)  ->				Tuple:
								run(self._app							,      host=self.host							,      port=self.port							,      workers=self.workers	)




					def 						A	(   self   :						Dict	)  ->				int:
								return ServeModelInfoResult(infos=vars(self._pipeline.model.config	)	)




					def 						A	(   self   :						int							,      _A   :						List[Any] = Body(_a							,      embed=_a	)							,      _A   :						Union[str, Any] = Body(_a							,      embed=_a	)	)  ->				int:
								try:
											UpperCAmelCase_	:    Tuple	  =    self._pipeline.tokenizer.tokenize(_a	)

											if return_ids:
														UpperCAmelCase_	:    Optional[int]	  =    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=5_00							,      detail={'''model''': '''''', '''error''': str(_a	)}	)




					def 						A	(   self   :						Optional[Any]							,      _A   :						Any = Body(_a							,      embed=_a	)							,      _A   :						List[Any] = Body(_a							,      embed=_a	)							,      _A   :						Optional[int] = Body(_a							,      embed=_a	)							,      )  ->				Optional[Any]:
								try:
											UpperCAmelCase_	:    str	  =    self._pipeline.tokenizer.decode(_a							,      _a							,      _a	)
											return ServeDeTokenizeResult(model=''''''							,      text=_a	)
								except Exception as e:
											raise HTTPException(status_code=5_00							,      detail={'''model''': '''''', '''error''': str(_a	)}	)





					async def 						A	(   self   :						int							,      _A   :						Tuple=Body(_a							,      embed=_a	)	)  ->				int:

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

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




"""simple docstring"""




from __future__ import annotations

from functools import lru_cache
from math import ceil

_A			:       Optional[Any]   		=       1_00

_A			:       Optional[int]   		=       set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A			:       int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
   if prime not in primes:
      continue
   primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))


@lru_cache(maxsize=100  )
def   __magic_name__	(      __snake_case				:       int  ) ->				set[int]:
       if number_to_partition < 0:
              return set()
       elif number_to_partition == 0:
              return {1}

       lowercase    :				set[int]       		=							set()
       lowercase    :				int
       lowercase    :				int

       for prime in primes:
              if prime > number_to_partition:
                     continue
              for sub in partition(number_to_partition - prime  ):
                     ret.add(sub * prime  )

       return ret


def   __magic_name__	(      __snake_case				:       int = 5000  ) ->				int | None:
       for number_to_partition in range(1     , __snake_case  ):
              if len(partition(__snake_case  )  ) > number_unique_partitions:
                     return number_to_partition
       return None


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


