"""simple docstring"""


class        a				:


    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      graph

      self._normalize_graph(__lowerCAmelCase   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[int]					):
      if sources is int:
        _UpperCAmelCase						 =      [sources]
      if sinks is int:
        _UpperCAmelCase						 =      [sinks]

      if len(__lowerCAmelCase					) == 0 or len(__lowerCAmelCase					) == 0:
        return

      _UpperCAmelCase						 =      sources[0]
      _UpperCAmelCase						 =      sinks[0]

      # make fake vertex if there are more
      # than one source or sink
      if len(__lowerCAmelCase					) > 1 or len(__lowerCAmelCase					) > 1:
        _UpperCAmelCase						 =      0
        for i in sources:
          max_input_flow += sum(self.graph[i]					)

        _UpperCAmelCase						 =      len(self.graph					) + 1
        for room in self.graph:
          room.insert(0   ,				0					)
        self.graph.insert(0   ,				[0] * size					)
        for i in sources:
          _UpperCAmelCase						 =      max_input_flow
        _UpperCAmelCase						 =      0

        _UpperCAmelCase						 =      len(self.graph					) + 1
        for room in self.graph:
          room.append(0					)
        self.graph.append([0] * size					)
        for i in sinks:
          _UpperCAmelCase						 =      max_input_flow
        _UpperCAmelCase						 =      size - 1


    def      lowerCAmelCase_  (					self	:  List[str]					):
      if self.maximum_flow_algorithm is None:
        raise Exception("""You need to set maximum flow algorithm before."""					)
      if self.source_index is None or self.sink_index is None:
        return 0

      self.maximum_flow_algorithm.execute()
      return self.maximum_flow_algorithm.getMaximumFlow()


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      algorithm(self					)



class        a				:


    def __init__(					self	:  Any   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      flow_network
      _UpperCAmelCase						 =      flow_network.verticesCount
      _UpperCAmelCase						 =      flow_network.sourceIndex
      _UpperCAmelCase						 =      flow_network.sinkIndex
      # it's just a reference, so you shouldn't change
      # it in your algorithms, use deep copy before doing that
      _UpperCAmelCase						 =      flow_network.graph
      _UpperCAmelCase						 =      False


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      if not self.executed:
        self._algorithm()
        _UpperCAmelCase						 =      True


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      pass



class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]					):
      super().__init__(__lowerCAmelCase					)
      # use this to save your result
      _UpperCAmelCase						 =      -1


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      if not self.executed:
        raise Exception("""You should execute algorithm before using its result!"""					)

      return self.maximum_flow



class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  Any   ,				__lowerCAmelCase	:  List[str]					):
      super().__init__(__lowerCAmelCase					)

      _UpperCAmelCase						 =      [[0] * self.verticies_count for i in range(self.verticies_count					)]

      _UpperCAmelCase						 =      [0] * self.verticies_count
      _UpperCAmelCase						 =      [0] * self.verticies_count


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.verticies_count

      # push some substance to graph
      for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]					):
        self.preflow[self.source_index][nextvertex_index] += bandwidth
        self.preflow[nextvertex_index][self.source_index] -= bandwidth
        self.excesses[nextvertex_index] += bandwidth

      # Relabel-to-front selection rule
      _UpperCAmelCase						 =      [
          i
          for i in range(self.verticies_count					)
          if i != self.source_index and i != self.sink_index
      ]

      # move through list
      _UpperCAmelCase						 =      0
      while i < len(__lowerCAmelCase					):
        _UpperCAmelCase						 =      vertices_list[i]
        _UpperCAmelCase						 =      self.heights[vertex_index]
        self.process_vertex(__lowerCAmelCase					)
        if self.heights[vertex_index] > previous_height:
          # if it was relabeled, swap elements
          # and start from 0 index
          vertices_list.insert(0   ,				vertices_list.pop(__lowerCAmelCase					)					)
          _UpperCAmelCase						 =      0
        else:
          i += 1

      _UpperCAmelCase						 =      sum(self.preflow[self.source_index]					)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Any					):
      while self.excesses[vertex_index] > 0:
        for neighbour_index in range(self.verticies_count					):
          # if it's neighbour and current vertex is higher
          if (
              self.graph[vertex_index][neighbour_index]
              - self.preflow[vertex_index][neighbour_index]
              > 0
              and self.heights[vertex_index] > self.heights[neighbour_index]
          ):
            self.push(__lowerCAmelCase   ,				__lowerCAmelCase					)

        self.relabel(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Any					):
      _UpperCAmelCase						 =      min(
          self.excesses[from_index]   ,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]   ,				)
      self.preflow[from_index][to_index] += preflow_delta
      self.preflow[to_index][from_index] -= preflow_delta
      self.excesses[from_index] -= preflow_delta
      self.excesses[to_index] += preflow_delta


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      None
      for to_index in range(self.verticies_count					):
        if (
            self.graph[vertex_index][to_index]
            - self.preflow[vertex_index][to_index]
            > 0
        ) and (min_height is None or self.heights[to_index] < min_height):
          _UpperCAmelCase						 =      self.heights[to_index]

      if min_height is not None:
        _UpperCAmelCase						 =      min_height + 1


if __name__ == "__main__":
    UpperCAmelCase__  		=						[0]
    UpperCAmelCase__  		=						[3]
    # graph = [
    #     [0, 0, 4, 6, 0, 0],
    #     [0, 0, 5, 2, 0, 0],
    #     [0, 0, 0, 0, 4, 4],
    #     [0, 0, 0, 0, 6, 6],
    #     [0, 0, 0, 0, 0, 0],
    #     [0, 0, 0, 0, 0, 0],
    # ]
    UpperCAmelCase__  		=						[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

    # prepare our network
    UpperCAmelCase__  		=						FlowNetwork(graph, entrances, exits)
    # set algorithm
    flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
    # and calculate
    UpperCAmelCase__  		=						flow_network.find_maximum_flow()

    print(F'''maximum flow is {maximum_flow}''')

"""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__  		=						True
except (ImportError, AttributeError):
    UpperCAmelCase__  		=						object






    def     __UpperCAmelCase					(			*lowercase   ,**lowercase		):







      """simple docstring"""
      pass

    UpperCAmelCase__  		=						False


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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      pipeline(
      task=args.task   ,model=args.model if args.model else None   ,config=args.config   ,tokenizer=args.tokenizer   ,device=args.device   ,)
  return ServeCommand(lowercase   ,args.host   ,args.port   ,args.workers		)


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  dict



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  List[str]
    _snake_case     :  Optional[List[int]]



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  str



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Any



class        a				(		lowerCAmelCase_   ):


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      _UpperCAmelCase						 =      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=8888   ,				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	:  Tuple   ,				__lowerCAmelCase	:  Pipeline   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      pipeline

      _UpperCAmelCase						 =      host
      _UpperCAmelCase						 =      port
      _UpperCAmelCase						 =      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}'''					)
        _UpperCAmelCase						 =      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=600   ,				)


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


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


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  str = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.tokenize(__lowerCAmelCase					)

        if return_ids:
          _UpperCAmelCase						 =      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=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[int] = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.decode(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)
        return ServeDeTokenizeResult(model=""""""   ,				text=__lowerCAmelCase					)
      except Exception as e:
        raise HTTPException(status_code=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    async def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[Any]=Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):

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

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