

'''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

     lowerCamelCase			:				Dict 			=					True
except (ImportError, AttributeError):
     lowerCamelCase			:				Any 			=					object


     def 	_lowerCAmelCase (						*_UpperCamelCase						:					Union[str, Any]							,			**_UpperCamelCase						:					str    )				->      Union[str, Any]:
          """simple docstring"""




          pass







     lowerCamelCase			:				Optional[int] 			=					False


lowerCamelCase			:				int 			=					logging.get_logger("transformers-cli/serving")


def 	_lowerCAmelCase (						_UpperCamelCase						:					Namespace    )				->      str:
     """simple docstring"""




     _SCREAMING_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(lowerCamelCase__							,			args.host							,			args.port							,			args.workers    )







class 							A__   (	a_  ):
  A__					=    42







class 							A__   (	a_  ):
  A__					=    42
  A__					=    42







class 							A__   (	a_  ):
  A__					=    42







class 							A__   (	a_  ):
  A__					=    42







class 							A__   (	a_  ):





  @staticmethod
  def 						A  (		_a				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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=8888							, 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				:     Dict							, _a				:     List[Any]							, _a				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Optional[Any]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =pipeline

       _SCREAMING_SNAKE_CASE							   =host
       _SCREAMING_SNAKE_CASE							   =port
       _SCREAMING_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}"			)
            _SCREAMING_SNAKE_CASE							   =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=600							, )





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





       '''simple docstring'''






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





  def 						A  (		self				:     List[Any]			)     ->   Optional[Any]:





       '''simple docstring'''






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





  def 						A  (		self				:     str							, _a				:     Union[str, Any] = Body(_a							, embed=_a			)							, _a				:     Any = Body(_a							, embed=_a			)			)     ->   Dict:





       '''simple docstring'''






       try:
            _SCREAMING_SNAKE_CASE							   =self._pipeline.tokenizer.tokenize(_a			)

            if return_ids:
                 _SCREAMING_SNAKE_CASE							   =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=500							, detail={'model': '', 'error': str(_a			)}			)





  def 						A  (		self				:     Any							, _a				:     Optional[Any] = Body(_a							, embed=_a			)							, _a				:     List[str] = Body(_a							, embed=_a			)							, _a				:     Dict = Body(_a							, embed=_a			)							, )     ->   Optional[int]:





       '''simple docstring'''






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





  async def 						A  (		self				:     Optional[int]							, _a				:     Union[str, Any]=Body(_a							, embed=_a			)			)     ->   str:





       '''simple docstring'''






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

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



'''simple docstring'''
from math import factorial


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if successes > trials:
          raise ValueError('successes must be lower or equal to trials'    )
     if trials < 0 or successes < 0:
          raise ValueError('the function is defined for non-negative integers'    )
     if not isinstance(_UpperCamelCase							,			_UpperCamelCase    ) or not isinstance(_UpperCamelCase							,			_UpperCamelCase    ):
          raise ValueError('the function is defined for non-negative integers'    )
     if not 0 < prob < 1:
          raise ValueError('prob has to be in range of 1 - 0'    )
     _SCREAMING_SNAKE_CASE							   =(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _SCREAMING_SNAKE_CASE							   =float(factorial(_UpperCamelCase    )    )
     coefficient /= factorial(_UpperCamelCase    ) * factorial(trials - successes    )
     return probability * coefficient


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     print("Probability of 2 successes out of 4 trails")
     print("with probability of 0.75 is:", end=" ")
     print(binomial_distribution(2, 4, 0.7_5))
