




'''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_						:      Any       =						True
except (ImportError, AttributeError):
   UpperCamelCase_						:      Any       =						object





   def      __a     (       *_UpperCamelCase:   Union[str, Any] ,						**_UpperCamelCase:   List[str]   )						->			int:

        """simple docstring"""




        pass





   UpperCamelCase_						:      int       =						False


UpperCamelCase_						:      Any       =						logging.get_logger('''transformers-cli/serving''')





def      __a     (       _UpperCamelCase:   Namespace   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				pipeline(
         task=args.task ,						model=args.model if args.model else None ,						config=args.config ,						tokenizer=args.tokenizer ,						device=args.device ,						)
     return ServeCommand(_UpperCamelCase ,						args.host ,						args.port ,						args.workers   )

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   dict







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]
     SCREAMING_SNAKE_CASE_							:   Optional[List[int]]







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any







class        _a    (						__lowerCAmelCase							):






     @staticmethod
     def     _lowercase     (			_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				parser.add_parser(
              "serve" ,help="CLI tool to run inference requests through REST and GraphQL endpoints."   )
          serve_parser.add_argument(
              "--task" ,type=_SCREAMING_SNAKE_CASE ,choices=get_supported_tasks() ,help="The task to run the pipeline on" ,)
          serve_parser.add_argument("--host" ,type=_SCREAMING_SNAKE_CASE ,default="localhost" ,help="Interface the server will listen on."   )
          serve_parser.add_argument("--port" ,type=_SCREAMING_SNAKE_CASE ,default=8_888 ,help="Port the serving will listen to."   )
          serve_parser.add_argument("--workers" ,type=_SCREAMING_SNAKE_CASE ,default=1 ,help="Number of http workers"   )
          serve_parser.add_argument("--model" ,type=_SCREAMING_SNAKE_CASE ,help="Model's name or path to stored model."   )
          serve_parser.add_argument("--config" ,type=_SCREAMING_SNAKE_CASE ,help="Model's config name or path to stored model."   )
          serve_parser.add_argument("--tokenizer" ,type=_SCREAMING_SNAKE_CASE ,help="Tokenizer name to use."   )
          serve_parser.add_argument(
              "--device" ,type=_SCREAMING_SNAKE_CASE ,default=-1 ,help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" ,)
          serve_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          _snake_case							=				pipeline

          _snake_case							=				host
          _snake_case							=				port
          _snake_case							=				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							=				FastAPI(
                   routes=[
                       APIRoute(
                           "/" ,self.model_info ,response_model=_SCREAMING_SNAKE_CASE ,response_class=_SCREAMING_SNAKE_CASE ,methods=["GET"] ,),
                       APIRoute(
                           "/tokenize" ,self.tokenize ,response_model=_SCREAMING_SNAKE_CASE ,response_class=_SCREAMING_SNAKE_CASE ,methods=["POST"] ,),
                       APIRoute(
                           "/detokenize" ,self.detokenize ,response_model=_SCREAMING_SNAKE_CASE ,response_class=_SCREAMING_SNAKE_CASE ,methods=["POST"] ,),
                       APIRoute(
                           "/forward" ,self.forward ,response_model=_SCREAMING_SNAKE_CASE ,response_class=_SCREAMING_SNAKE_CASE ,methods=["POST"] ,),
                   ] ,timeout=600 ,)






     def     _lowercase     (			self   )							->      Optional[Any]:
          run(self._app ,host=self.host ,port=self.port ,workers=self.workers   )






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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE = Body(_SCREAMING_SNAKE_CASE ,embed=_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE = Body(_SCREAMING_SNAKE_CASE ,embed=_SCREAMING_SNAKE_CASE   )   )							->      Union[str, Any]:
          try:
               _snake_case							=				self._pipeline.tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )

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

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






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






     async def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=Body(_SCREAMING_SNAKE_CASE ,embed=_SCREAMING_SNAKE_CASE   )   )							->      Optional[Any]:

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

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








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

     # Validation
     def count_nodes(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

     def count_coins(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_coins(node.left   ) + count_coins(node.right   ) + node.data

     if count_nodes(_UpperCamelCase   ) != count_coins(_UpperCamelCase   ):
          raise ValueError("The nodes number should be same as the number of coins"   )

     # Main calculation
     def get_distrib(_UpperCamelCase:   TreeNode | None   ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0 ,						1   )

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


