






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    __magic_name__    (	*SCREAMING_SNAKE_CASE     ,     **SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
														pass






							UpperCamelCase								=		False


UpperCamelCase								=		logging.get_logger("transformers-cli/serving")






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   List[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 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       int							       = 42




class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       Dict							       = 42
				_UpperCamelCase					:       List[str]							       = 42




class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       List[Any]							       = 42




class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       Union[str, Any]							       = 42




class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):


				@staticmethod
				def 						__a					(					_lowerCAmelCase      ):
											_lowercase							:   Union[str, Any] 							=   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 ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   int 							=   pipeline

											_lowercase							:   List[Any] 							=   host
											_lowercase							:   int 							=   port
											_lowercase							:   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}"""      )
																		_lowercase							:   Optional[Any] 							=   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 						__a					(					self      ):
											run(self._app ,						host=self.host ,						port=self.port ,						workers=self.workers      )


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


				def 						__a					(					self ,						_lowerCAmelCase = Body(_a ,						embed=_a      ) ,						_lowerCAmelCase = Body(_a ,						embed=_a      )      ):
											try:
																		_lowercase							:   Dict 							=   self._pipeline.tokenizer.tokenize(_a      )

																		if return_ids:
																									_lowercase							:   int 							=   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 						__a					(					self ,						_lowerCAmelCase = Body(_a ,						embed=_a      ) ,						_lowerCAmelCase = Body(_a ,						embed=_a      ) ,						_lowerCAmelCase = Body(_a ,						embed=_a      ) ,						):
											try:
																		_lowercase							:   Dict 							=   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 						__a					(					self ,						_lowerCAmelCase=Body(_a ,						embed=_a      )      ):

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

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









'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






