


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


 def    UpperCAmelCase_  (       *A		,	**A      ):
    '''simple docstring'''





    pass







 UpperCAmelCase_      :		Dict          =		False


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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  pipeline(
       task=args.task		,	model=args.model if args.model else None		,	config=args.config		,	tokenizer=args.tokenizer		,	device=args.device		,	)
   return ServeCommand(lowercase__		,	args.host		,	args.port		,	args.workers      )







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: dict







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[str]
 __lowerCAmelCase						: Optional[List[int]]







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: str







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Any







class 					a			(				snake_case__  ):
 '''simple docstring'''




 @staticmethod
 def   __UpperCamelCase   (    lowerCamelCase_      )      ->    List[Any]:
    _a					:  Optional[Any]						=  parser.add_parser(
        'serve'		,      help='CLI tool to run inference requests through REST and GraphQL endpoints.'      )
    serve_parser.add_argument(
        '--task'		,      type=snake_case__		,      choices=get_supported_tasks()		,      help='The task to run the pipeline on'		,      )
    serve_parser.add_argument('--host'		,      type=snake_case__		,      default='localhost'		,      help='Interface the server will listen on.'      )
    serve_parser.add_argument('--port'		,      type=snake_case__		,      default=8_8_8_8		,      help='Port the serving will listen to.'      )
    serve_parser.add_argument('--workers'		,      type=snake_case__		,      default=1		,      help='Number of http workers'      )
    serve_parser.add_argument('--model'		,      type=snake_case__		,      help='Model\'s name or path to stored model.'      )
    serve_parser.add_argument('--config'		,      type=snake_case__		,      help='Model\'s config name or path to stored model.'      )
    serve_parser.add_argument('--tokenizer'		,      type=snake_case__		,      help='Tokenizer name to use.'      )
    serve_parser.add_argument(
        '--device'		,      type=snake_case__		,      default=-1		,      help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'		,      )
    serve_parser.set_defaults(func=snake_case__      )




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    List[Any]:
    _a					:  List[str]						=  pipeline

    _a					:  str						=  host
    _a					:  Dict						=  port
    _a					:  Dict						=  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					:  List[Any]						=  FastAPI(
           routes=[
               APIRoute(
                   '/'		,      self.model_info		,      response_model=snake_case__		,      response_class=snake_case__		,      methods=['GET']		,      ),
               APIRoute(
                   '/tokenize'		,      self.tokenize		,      response_model=snake_case__		,      response_class=snake_case__		,      methods=['POST']		,      ),
               APIRoute(
                   '/detokenize'		,      self.detokenize		,      response_model=snake_case__		,      response_class=snake_case__		,      methods=['POST']		,      ),
               APIRoute(
                   '/forward'		,      self.forward		,      response_model=snake_case__		,      response_class=snake_case__		,      methods=['POST']		,      ),
           ]		,      timeout=6_0_0		,      )




 def   __UpperCamelCase   (    self      )      ->    str:
    run(self._app		,      host=self.host		,      port=self.port		,      workers=self.workers      )




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_ = Body(snake_case__		,      embed=snake_case__      )		,      lowerCamelCase_ = Body(snake_case__		,      embed=snake_case__      )      )      ->    Optional[int]:




    try:
       _a					:  Optional[Any]						=  self._pipeline.tokenizer.tokenize(snake_case__      )

       if return_ids:
          _a					:  List[str]						=  self._pipeline.tokenizer.convert_tokens_to_ids(snake_case__      )
          return ServeTokenizeResult(tokens=snake_case__		,      tokens_ids=snake_case__      )
       else:
          return ServeTokenizeResult(tokens=snake_case__      )

    except Exception as e:
       raise HTTPException(status_code=5_0_0		,      detail={'model': '', 'error': str(snake_case__      )}      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_ = Body(snake_case__		,      embed=snake_case__      )		,      lowerCamelCase_ = Body(snake_case__		,      embed=snake_case__      )		,      lowerCamelCase_ = Body(snake_case__		,      embed=snake_case__      )		,      )      ->    Any:




    try:
       _a					:  List[Any]						=  self._pipeline.tokenizer.decode(snake_case__		,      snake_case__		,      snake_case__      )
       return ServeDeTokenizeResult(model=''		,      text=snake_case__      )
    except Exception as e:
       raise HTTPException(status_code=5_0_0		,      detail={'model': '', 'error': str(snake_case__      )}      )




 async def   __UpperCamelCase   (    self		,      lowerCamelCase_=Body(snake_case__		,      embed=snake_case__      )      )      ->    List[Any]:




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

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




'''simple docstring'''





def snake_case_     (					lowercase__		):

  if len(lowercase__		) <= 1:
    return [tuple(lowercase__		)]

  UpperCAmelCase__      :					Union[str, Any]				       =							[]

  def generate(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					str				       =							[0] * n
    res.append(tuple(lowercase__		)		)

    UpperCAmelCase__      :					Any				       =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							arr[i], arr[0]
        else:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							arr[i], arr[c[i]]
        res.append(tuple(lowercase__		)		)
        c[i] += 1
        UpperCAmelCase__      :					List[str]				       =							0
      else:
        UpperCAmelCase__      :					Dict				       =							0
        i += 1

  generate(len(lowercase__		)   ,       lowercase__		)
  return res


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       input("""Enter numbers separated by a comma:\n""").strip()
      SCREAMING_SNAKE_CASE     =       [int(item) for item in user_input.split(""",""")]
      print(heaps(arr))



