


"""simple docstring"""

import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class  lowerCamelCase__    :
      def __init__(		self						:						Any   ,							A_						:						int				):






       '''simple docstring'''


       if isinstance(A_   ,							A_				):
        # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
        # modified it, it will not be accepted here again, since `auto` values would have been overridden
        __lowercase						=						deepcopy(A_				)
       elif os.path.exists(A_				):
        with io.open(A_   ,							"""r"""   ,							encoding="""utf-8"""				) as f:
         __lowercase						=						json.load(A_				)
       else:
        try:
         __lowercase						=						baseaa.urlsafe_baadecode(A_				).decode("""utf-8"""				)
         __lowercase						=						json.loads(A_				)
        except (UnicodeDecodeError, AttributeError, ValueError):
         raise ValueError(
             F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''				)

       __lowercase						=						config

       self.set_stage_and_offload()
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.get_value("""zero_optimization.stage"""   ,							-1				)

       # offload
       __lowercase						=						False
       if self.is_zeroa() or self.is_zeroa():
        __lowercase						=						set(["""cpu""", """nvme"""]				)
        __lowercase						=						set(
            [
                self.get_value("""zero_optimization.offload_optimizer.device"""				),
                self.get_value("""zero_optimization.offload_param.device"""				),
            ]				)
        if len(offload_devices & offload_devices_valid				) > 0:
         __lowercase						=						True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.config

       # find the config node of interest if it exists
       __lowercase						=						ds_key_long.split("""."""				)
       __lowercase						=						nodes.pop()
       for node in nodes:
        __lowercase						=						config.get(A_				)
        if config is None:
         return None, ds_key

       return config, ds_key
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						int   ,							A_						:						List[str]=None				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.find_config_node(A_				)
       if config is None:
        return default
       return config.get(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str=False				):






       '''simple docstring'''


       __lowercase						=						self.config

       # find the config node of interest if it exists
       __lowercase						=						ds_key_long.split("""."""				)
       for node in nodes:
        __lowercase						=						config
        __lowercase						=						config.get(A_				)
        if config is None:
         if must_exist:
          raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}'''				)
         else:
          return

        # if found remove it
       if parent_config is not None:
        parent_config.pop(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.get_value(A_				)
       return False if value is None else bool(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.get_value(A_				)
       return False if value is None else not bool(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       return self._stage == 2
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self._stage == 3






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       return self._offload






class  lowerCamelCase__    :
      def __init__(		self						:						Union[str, Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						engine






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						str   ,							**A_						:						Any				):






       '''simple docstring'''


       self.engine.backward(A_   ,							**A_				)

       # Deepspeed's `engine.step` performs the following operations:
       # - gradient accumulation check
       # - gradient clipping
       # - optimizer step
       # - zero grad
       # - checking overflow
       # - lr_scheduler step (only if engine.lr_scheduler is not None)
       self.engine.step()
       # and this plugin overrides the above calls with no-ops when Accelerate runs under
       # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
       # training loop that works transparently under many training regimes.






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						str				):






       '''simple docstring'''


       super().__init__(A_   ,							device_placement=A_   ,							scaler=A_				)
       __lowercase						=						hasattr(self.optimizer   ,							"""overflow"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						str=None				):






       '''simple docstring'''


       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if self.__has_overflow__:
        return self.optimizer.overflow
       return False






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Dict   ,							A_						:						List[str]				):






       '''simple docstring'''


       super().__init__(A_   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  lowerCamelCase__    :
      def __init__(		self						:						Dict   ,							A_						:						Any   ,							A_						:						int=0.0_01   ,							A_						:						Union[str, Any]=0   ,							**A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						params
       __lowercase						=						lr
       __lowercase						=						weight_decay
       __lowercase						=						kwargs






class  lowerCamelCase__    :
      def __init__(		self						:						Union[str, Any]   ,							A_						:						Union[str, Any]   ,							A_						:						Any=None   ,							A_						:						str=0   ,							**A_						:						int				):






       '''simple docstring'''


       __lowercase						=						optimizer
       __lowercase						=						total_num_steps
       __lowercase						=						warmup_num_steps
       __lowercase						=						kwargs




"""simple docstring"""

class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						None
       __lowercase						=						None
       __lowercase						=						graph

       self._normalize_graph(A_   ,							A_				)
       __lowercase						=						len(A_				)
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str   ,							A_						:						List[Any]				):






       '''simple docstring'''


       if sources is int:
        __lowercase						=						[sources]
       if sinks is int:
        __lowercase						=						[sinks]

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

       __lowercase						=						sources[0]
       __lowercase						=						sinks[0]

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

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

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






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						algorithm(self				)






class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


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






       '''simple docstring'''


       if not self.executed:
        self._algorithm()
        __lowercase						=						True






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       pass






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       # use this to save your result
       __lowercase						=						-1






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       if not self.executed:
        raise Exception("""You should execute algorithm before using its result!"""				)

       return self.maximum_flow






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Tuple				):






       '''simple docstring'''


       super().__init__(A_				)

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

       __lowercase						=						[0] * self.verticies_count
       __lowercase						=						[0] * self.verticies_count
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						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
       __lowercase						=						[
           i
           for i in range(self.verticies_count				)
           if i != self.source_index and i != self.sink_index
       ]

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

       __lowercase						=						sum(self.preflow[self.source_index]				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       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(A_   ,							A_				)

        self.relabel(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Union[str, Any]   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						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):
         __lowercase						=						self.heights[to_index]

       if min_height is not None:
        __lowercase						=						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}""")
