


'''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

							__A	:	str     =     True
except (ImportError, AttributeError):
							__A	:	List[str]     =     object





							def      lowerCamelCase      (		*lowerCamelCase_  :Optional[int]				,      **lowerCamelCase_  :List[str] ):







									'''simple docstring'''
									pass






							__A	:	Dict     =     False


__A	:	List[Any]     =     logging.get_logger('transformers-cli/serving')





def      lowerCamelCase      (		lowerCamelCase_  :Namespace ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						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 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		dict





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		List[str]
							lowercase			:		Optional[List[int]]





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		str





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Any





class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									snake_case_		:							Any			       =						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_8_8_8  ,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		:List[Any]  ,_UpperCamelCase		:Pipeline  ,_UpperCamelCase		:str  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int			):
									snake_case_		:							Dict			       =						pipeline

									snake_case_		:							Any			       =						host
									snake_case_		:							Optional[Any]			       =						port
									snake_case_		:							str			       =						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_		:							str			       =						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=6_0_0  ,)





							def 		a__   (  self		:Optional[int]			):
									run(self._app  ,host=self.host  ,port=self.port  ,workers=self.workers			)





							def 		a__   (  self		:Optional[Any]			):
									return ServeModelInfoResult(infos=vars(self._pipeline.model.config			)			)





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:str = Body(_UpperCamelCase  ,embed=_UpperCamelCase			)  ,_UpperCamelCase		:bool = Body(_UpperCamelCase  ,embed=_UpperCamelCase			)			):
									try:
											snake_case_		:							str			       =						self._pipeline.tokenizer.tokenize(_UpperCamelCase			)

											if return_ids:
													snake_case_		:							Dict			       =						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=5_0_0  ,detail={"""model""": """""", """error""": str(_UpperCamelCase			)}			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:List[int] = Body(_UpperCamelCase  ,embed=_UpperCamelCase			)  ,_UpperCamelCase		:bool = Body(_UpperCamelCase  ,embed=_UpperCamelCase			)  ,_UpperCamelCase		:bool = Body(_UpperCamelCase  ,embed=_UpperCamelCase			)  ,):
									try:
											snake_case_		:							Dict			       =						self._pipeline.tokenizer.decode(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)
											return ServeDeTokenizeResult(model=""""""  ,text=_UpperCamelCase			)
									except Exception as e:
											raise HTTPException(status_code=5_0_0  ,detail={"""model""": """""", """error""": str(_UpperCamelCase			)}			)





							async def 		a__   (  self		:Dict  ,_UpperCamelCase		:Dict=Body(_UpperCamelCase  ,embed=_UpperCamelCase			)			):

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

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



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')