


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

			_snake_case					:		List[str]      =    True
except (ImportError, AttributeError):
			_snake_case					:		List[Any]      =    object







			def     lowerCAmelCase_					( *__lowerCamelCase							,     **__lowerCamelCase     ):
										pass

			_snake_case					:		Optional[Any]      =    False


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







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							dict

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]
							__UpperCAmelCase   :							Optional[List[int]]

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							@staticmethod
							def    __snake_case (							lowerCamelCase	:  ArgumentParser    )     ->   int:
														__snake_case		:      List[str]										=       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=8888   ,     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	:  int   ,     lowerCamelCase	:  Pipeline   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  int    )     ->   str:
														__snake_case		:      List[str]										=       pipeline

														__snake_case		:      List[str]										=       host
														__snake_case		:      int										=       port
														__snake_case		:      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}'    )
																					__snake_case		:      Dict										=       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=600   ,     )

							def    __snake_case (							self	:  int    )     ->   List[str]:
														run(self._app   ,     host=self.host   ,     port=self.port   ,     workers=self.workers    )

							def    __snake_case (							self	:  List[str]    )     ->   int:
														return ServeModelInfoResult(infos=vars(self._pipeline.model.config    )    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  str = Body(lowerCamelCase   ,     embed=lowerCamelCase    )   ,     lowerCamelCase	:  bool = Body(lowerCamelCase   ,     embed=lowerCamelCase    )    )     ->   Dict:
														try:
																					__snake_case		:      Optional[int]										=       self._pipeline.tokenizer.tokenize(lowerCamelCase    )

																					if return_ids:
																												__snake_case		:      Optional[int]										=       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=500   ,     detail={"model": "", "error": str(lowerCamelCase    )}    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  List[int] = Body(lowerCamelCase   ,     embed=lowerCamelCase    )   ,     lowerCamelCase	:  bool = Body(lowerCamelCase   ,     embed=lowerCamelCase    )   ,     lowerCamelCase	:  bool = Body(lowerCamelCase   ,     embed=lowerCamelCase    )   ,     )     ->   Optional[Any]:
														try:
																					__snake_case		:      Optional[Any]										=       self._pipeline.tokenizer.decode(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																					return ServeDeTokenizeResult(model=""   ,     text=lowerCamelCase    )
														except Exception as e:
																					raise HTTPException(status_code=500   ,     detail={"model": "", "error": str(lowerCamelCase    )}    )






							async def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Any=Body(lowerCamelCase   ,     embed=lowerCamelCase    )    )     ->   int:

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

														try:
																					# Forward through the model
																					__snake_case		:      Optional[int]										=       self._pipeline(lowerCamelCase    )
																					return ServeForwardResult(output=lowerCamelCase    )
														except Exception as e:
																					raise HTTPException(500   ,     {"error": str(lowerCamelCase    )}    )





from __future__ import annotations

from functools import lru_cache
from math import ceil

_snake_case					:		Tuple      =    100

_snake_case					:		int      =    set(range(3, NUM_PRIMES, 2))
primes.add(2)
_snake_case					:		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=1_0_0     )
def     lowerCAmelCase_					( __lowerCamelCase     ):
							if number_to_partition < 0:
														return set()
							elif number_to_partition == 0:
														return {1}

							__snake_case		:      set[int]										=       set()
							__snake_case		:      int
							__snake_case		:      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     lowerCAmelCase_					( __lowerCamelCase = 5_0_0_0     ):
							for number_to_partition in range(1							,     __lowerCamelCase     ):
														if len(partition(__lowerCamelCase     )     ) > number_unique_partitions:
																					return number_to_partition
							return None


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

