
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

     __magic_name__            =  True
except (ImportError, AttributeError):
     __magic_name__            =  object





     def 	SCREAMING_SNAKE_CASE__    (      *__lowerCAmelCase    ,		**__lowerCAmelCase							):
         pass





     __magic_name__            =  False


__magic_name__            =  logging.get_logger('''transformers-cli/serving''')





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		dict





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]
      _A						:		Optional[List[int]]





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		str





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				parser.add_parser(
              "serve"		,	help="CLI tool to run inference requests through REST and GraphQL endpoints." )
          serve_parser.add_argument(
              "--task"		,	type=lowerCamelCase		,	choices=get_supported_tasks()		,	help="The task to run the pipeline on"		,	)
          serve_parser.add_argument("--host"		,	type=lowerCamelCase		,	default="localhost"		,	help="Interface the server will listen on." )
          serve_parser.add_argument("--port"		,	type=lowerCamelCase		,	default=88_88		,	help="Port the serving will listen to." )
          serve_parser.add_argument("--workers"		,	type=lowerCamelCase		,	default=1		,	help="Number of http workers" )
          serve_parser.add_argument("--model"		,	type=lowerCamelCase		,	help="Model's name or path to stored model." )
          serve_parser.add_argument("--config"		,	type=lowerCamelCase		,	help="Model's config name or path to stored model." )
          serve_parser.add_argument("--tokenizer"		,	type=lowerCamelCase		,	help="Tokenizer name to use." )
          serve_parser.add_argument(
              "--device"		,	type=lowerCamelCase		,	default=-1		,	help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"		,	)
          serve_parser.set_defaults(func=lowerCamelCase )
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          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=lowerCamelCase		,	response_class=lowerCamelCase		,	methods=["GET"]		,	),
                      APIRoute(
                          "/tokenize"		,	self.tokenize		,	response_model=lowerCamelCase		,	response_class=lowerCamelCase		,	methods=["POST"]		,	),
                      APIRoute(
                          "/detokenize"		,	self.detokenize		,	response_model=lowerCamelCase		,	response_class=lowerCamelCase		,	methods=["POST"]		,	),
                      APIRoute(
                          "/forward"		,	self.forward		,	response_model=lowerCamelCase		,	response_class=lowerCamelCase		,	methods=["POST"]		,	),
                  ]		,	timeout=6_00		,	)
      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(lowerCamelCase		,	embed=lowerCamelCase )		,	lowerCamelCase = Body(lowerCamelCase		,	embed=lowerCamelCase ) ):
          try:
              snake_case__						=				self._pipeline.tokenizer.tokenize(lowerCamelCase )

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

          except Exception as e:
              raise HTTPException(status_code=5_00		,	detail={"model": "", "error": str(lowerCamelCase )} )
      def A_			(			self		,	lowerCamelCase = Body(lowerCamelCase		,	embed=lowerCamelCase )		,	lowerCamelCase = Body(lowerCamelCase		,	embed=lowerCamelCase )		,	lowerCamelCase = Body(lowerCamelCase		,	embed=lowerCamelCase )		,	):
          try:
              snake_case__						=				self._pipeline.tokenizer.decode(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              return ServeDeTokenizeResult(model=""		,	text=lowerCamelCase )
          except Exception as e:
              raise HTTPException(status_code=5_00		,	detail={"model": "", "error": str(lowerCamelCase )} )



      async def A_			(			self		,	lowerCamelCase=Body(lowerCamelCase		,	embed=lowerCamelCase ) ):

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

          try:
              # Forward through the model
              snake_case__						=				self._pipeline(lowerCamelCase )
              return ServeForwardResult(output=lowerCamelCase )
          except Exception as e:
              raise HTTPException(5_00		,	{"error": str(lowerCamelCase )} )





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[False] * len(__lowerCAmelCase							)
    snake_case__						=				[-1] * len(__lowerCAmelCase							)

    def dfs(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				True
        snake_case__						=				c
        for u in graph[v]:
            if not visited[u]:
                dfs(__lowerCAmelCase    ,		1 - c							)

    for i in range(len(__lowerCAmelCase							)							):
        if not visited[i]:
            dfs(__lowerCAmelCase    ,		0							)

    for i in range(len(__lowerCAmelCase							)							):
        for j in graph[i]:
            if color[i] == color[j]:
                return False

    return True


# Adjacency list of graph
__magic_name__            =  {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))



