






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__				   =		True
except (ImportError, AttributeError):
     UpperCAmelCase__				   =		object
     def 						_a    (      *a :str	,							**a :Optional[Any] )							->			int:
      pass

     UpperCAmelCase__				   =		False


UpperCAmelCase__				   =		logging.get_logger("transformers-cli/serving")
def 						_a    (      a :Namespace )							->			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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  42






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  42
      __snake_case       =  42






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  42






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  42






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @staticmethod
      def       __lowerCAmelCase					(      __UpperCAmelCase      :						ArgumentParser  )    ->Dict:




       """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=__UpperCAmelCase ,					choices=get_supported_tasks() ,					help='''The task to run the pipeline on''' ,					)
       serve_parser.add_argument('''--host''' ,					type=__UpperCAmelCase ,					default='''localhost''' ,					help='''Interface the server will listen on.'''  )
       serve_parser.add_argument('''--port''' ,					type=__UpperCAmelCase ,					default=8_888 ,					help='''Port the serving will listen to.'''  )
       serve_parser.add_argument('''--workers''' ,					type=__UpperCAmelCase ,					default=1 ,					help='''Number of http workers'''  )
       serve_parser.add_argument('''--model''' ,					type=__UpperCAmelCase ,					help='''Model\'s name or path to stored model.'''  )
       serve_parser.add_argument('''--config''' ,					type=__UpperCAmelCase ,					help='''Model\'s config name or path to stored model.'''  )
       serve_parser.add_argument('''--tokenizer''' ,					type=__UpperCAmelCase ,					help='''Tokenizer name to use.'''  )
       serve_parser.add_argument(
           '''--device''' ,					type=__UpperCAmelCase ,					default=-1 ,					help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' ,					)
       serve_parser.set_defaults(func=__UpperCAmelCase  )







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						Pipeline ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						int  )    ->List[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=__UpperCAmelCase ,					response_class=__UpperCAmelCase ,					methods=['''GET'''] ,					),
                APIRoute(
                    '''/tokenize''' ,					self.tokenize ,					response_model=__UpperCAmelCase ,					response_class=__UpperCAmelCase ,					methods=['''POST'''] ,					),
                APIRoute(
                    '''/detokenize''' ,					self.detokenize ,					response_model=__UpperCAmelCase ,					response_class=__UpperCAmelCase ,					methods=['''POST'''] ,					),
                APIRoute(
                    '''/forward''' ,					self.forward ,					response_model=__UpperCAmelCase ,					response_class=__UpperCAmelCase ,					methods=['''POST'''] ,					),
            ] ,					timeout=600 ,					)







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Union[str, Any]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						int  )    ->Union[str, Any]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						str = Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  ) ,					__UpperCAmelCase      :						bool = Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  )  )    ->Tuple:




       """simple docstring"""


       try:
        a		  =      self._pipeline.tokenizer.tokenize(__UpperCAmelCase  )

        if return_ids:
         a		  =      self._pipeline.tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )
         return ServeTokenizeResult(tokens=__UpperCAmelCase ,					tokens_ids=__UpperCAmelCase  )
        else:
         return ServeTokenizeResult(tokens=__UpperCAmelCase  )

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







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						List[int] = Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  ) ,					__UpperCAmelCase      :						bool = Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  ) ,					__UpperCAmelCase      :						bool = Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  ) ,					)    ->Any:




       """simple docstring"""


       try:
        a		  =      self._pipeline.tokenizer.decode(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
        return ServeDeTokenizeResult(model='''''' ,					text=__UpperCAmelCase  )
       except Exception as e:
        raise HTTPException(status_code=500 ,					detail={'''model''': '''''', '''error''': str(__UpperCAmelCase  )}  )







      async def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						Optional[int]=Body(__UpperCAmelCase ,					embed=__UpperCAmelCase  )  )    ->Any:




       """simple docstring"""


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

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










import math
def 						_a    (      a :int )							->			list:
 a		  =      [True] * n
 a		  =      False
 a		  =      False
 a		  =      True

 for i in range(3	,							int(n**0.5 + 1 )	,							2 ):
  a		  =      i * 2
  while index < n:
   a		  =      False
   a		  =      index + i

 a		  =      [2]

 for i in range(3	,							a	,							2 ):
  if is_prime[i]:
   primes.append(a )

 return primes
def 						_a    (      a :int = 999_966_663_333 )							->			int:
 a		  =      math.floor(math.sqrt(a ) ) + 100
 a		  =      prime_sieve(a )

 a		  =      0
 a		  =      0
 a		  =      primes[prime_index]

 while (last_prime**2) <= limit:
  a		  =      primes[prime_index + 1]

  a		  =      last_prime**2
  a		  =      next_prime**2

  # Get numbers divisible by lps(current)
  a		  =      lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  a		  =      upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  a		  =      0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  a		  =      next_prime
  prime_index += 1

 return matches_sum


if __name__ == "__main__":
     print(solution())


