




"""simple docstring"""

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a							      =     [
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   Any=None							,    _snake_case				:   Any=None							,    _snake_case				:   List[str]=None				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  True
      while ask_again:
            _A     =  input(_snake_case				)
            try:
                  if default is not None and len(_snake_case				) == 0:
                        return default
                  return convert_value(_snake_case				) if convert_value is not None else result
            except Exception:
                  if error_message is not None:
                        print(_snake_case				)






def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Optional[Any]=[]							,    _snake_case				:   str=None							,    _snake_case				:   Tuple=0				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  BulletMenu(_snake_case							,    _snake_case				)
      _A     =  menu.run(default_choice=_snake_case				)
      return convert_value(_snake_case				) if convert_value is not None else result






def 		_snake_case	(   _snake_case				:   str				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  int(_snake_case				)
      return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]				)






def 		_snake_case	(   _snake_case				:   Any				)       ->   Tuple:






      '''simple docstring'''


      _A     =  int(_snake_case				)
      return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]				)






def 		_snake_case	(   _snake_case				:   List[Any]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  int(_snake_case				)
      return DynamoBackend(DYNAMO_BACKENDS[value]				).value






def 		_snake_case	(   _snake_case				:   Tuple				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  int(_snake_case				)
      return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]				)






def 		_snake_case	(   _snake_case				:   Optional[int]				)       ->   List[str]:






      '''simple docstring'''


      _A     =  int(_snake_case				)
      return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]				)






def 		_snake_case	(   _snake_case				:   int				)       ->   List[Any]:






      '''simple docstring'''


      return {"yes": True, "no": False}[value.lower()]







class 							lowercase_			(	argparse.RawDescriptionHelpFormatter				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  super()._format_usage(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  usage.replace('<command> [<args>] '       ,				''						)
       return usage







"""simple docstring"""

class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       _A     =  None
       _A     =  graph

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Dict						):
       if sources is int:
             _A     =  [sources]
       if sinks is int:
             _A     =  [sinks]

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

       _A     =  sources[0]
       _A     =  sinks[0]

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

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

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       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]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  algorithm(self						)





class 							lowercase_			:



 '''simple docstring'''

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

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



 def      lowerCAmelCase_				(					self			:			int						):
       pass





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       super().__init__(_UpperCAmelCase						)
       # use this to save your result
       _A     =  -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 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]						):
       super().__init__(_UpperCAmelCase						)

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

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

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  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
       _A     =  [
           i
           for i in range(self.verticies_count						)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       _A     =  0
       while i < len(_UpperCAmelCase						):
             _A     =  vertices_list[i]
             _A     =  self.heights[vertex_index]
             self.process_vertex(_UpperCAmelCase						)
             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(_UpperCAmelCase						)						)
                   _A     =  0
             else:
                   i += 1

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

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			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(_UpperCAmelCase       ,				_UpperCAmelCase						)

             self.relabel(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  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			:			Union[str, Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  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):
                   _A     =  self.heights[to_index]

       if min_height is not None:
             _A     =  min_height + 1


if __name__ == "__main__":
       a							      =     [0]
       a							      =     [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],
       # ]
       a							      =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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

