
'''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_       :   Union[str, Any]   =       True
except (ImportError, AttributeError):
 UpperCAmelCase_       :   Optional[Any]   =       object







 def 		A_       (	*_lowerCAmelCase   :    List[str]				,	**_lowerCAmelCase   :    Optional[int]							):



       """simple docstring"""




       pass



 UpperCAmelCase_       :   Optional[int]   =       False


UpperCAmelCase_       :   Tuple   =       logging.get_logger('transformers-cli/serving')







def 		A_       (	_lowerCAmelCase   :    Namespace							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   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  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						(       A ):



    @staticmethod
    def   lowerCamelCase_  (	__A      :       ArgumentParser     ):
          _lowerCamelCase				:   int								=   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=8_8_8_8,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      :       Pipeline,__A      :       str,__A      :       int,__A      :       int     ):
          _lowerCamelCase				:   Tuple								=   pipeline

          _lowerCamelCase				:   str								=   host
          _lowerCamelCase				:   List[str]								=   port
          _lowerCamelCase				:   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}'     )
                _lowerCamelCase				:   Optional[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=6_0_0,)



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



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



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       str = Body(__A,embed=__A     ),__A      :       bool = Body(__A,embed=__A     )     ):
          try:
                _lowerCamelCase				:   Tuple								=   self._pipeline.tokenizer.tokenize(__A     )

                if return_ids:
                      _lowerCamelCase				:   Dict								=   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_0_0,detail={"model": "", "error": str(__A     )}     )



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int] = Body(__A,embed=__A     ),__A      :       bool = Body(__A,embed=__A     ),__A      :       bool = Body(__A,embed=__A     ),):
          try:
                _lowerCamelCase				:   List[str]								=   self._pipeline.tokenizer.decode(__A,__A,__A     )
                return ServeDeTokenizeResult(model="",text=__A     )
          except Exception as e:
                raise HTTPException(status_code=5_0_0,detail={"model": "", "error": str(__A     )}     )



    async def   lowerCamelCase_  (	self      :       Tuple,__A      :       Dict=Body(__A,embed=__A     )     ):

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

          try:
                # Forward through the model
                _lowerCamelCase				:   Tuple								=   self._pipeline(__A     )
                return ServeForwardResult(output=__A     )
          except Exception as e:
                raise HTTPException(5_0_0,{"error": str(__A     )}     )

'''simple docstring'''






import math







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   math.loga(math.sqrt(4 * positive_integer + 1							) / 2 + 1 / 2							)

      return exponent == int(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    float = 1 / 12345							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   int								=   0

      _lowerCamelCase				:   List[str]								=   3
      while True:
            _lowerCamelCase				:   List[Any]								=   (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(_lowerCAmelCase							):
                  _lowerCamelCase				:   Any								=   int(_lowerCAmelCase							)
                  total_partitions += 1
                  if check_partition_perfect(_lowerCAmelCase							):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(_lowerCAmelCase							)
            integer += 1


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