






"""simple docstring"""

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm






def _lowerCAmelCase						(	__lowerCamelCase:List[Any]			,    __lowerCamelCase:Dict ):

						'''simple docstring'''






						if isinstance(__A			,    (list, tuple) ):
												return honor_type(__A			,    (move_to_device(__A			,    __A ) for t in state) )
						elif isinstance(__A			,    __A ):
												return type(__A )({k: move_to_device(__A			,    __A ) for k, v in state.items()} )
						elif isinstance(__A			,    torch.Tensor ):
												return state.to(__A )
						return state


class     A_				(   torch.optim.Optimizer	):







				def __init__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    str=None						)					->       int:
										__magic_name__      =  optimizer
										__magic_name__      =  scaler
										__magic_name__      =  AcceleratorState()
										__magic_name__      =  GradientState()
										__magic_name__      =  device_placement
										__magic_name__      =  False
										__magic_name__      =  None

										# Handle device placement
										if device_placement:
																__magic_name__      =  self.optimizer.state_dict()
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																						xm.send_cpu_data_to_device(lowerCAmelCase__    ,     self.accelerator_state.device						)
																else:
																						__magic_name__      =  move_to_device(lowerCAmelCase__    ,     self.accelerator_state.device						)
																self.optimizer.load_state_dict(lowerCAmelCase__						)







				@property
				def      _snake_case       (				self				:    List[str]						)					->       Tuple:
										return self.optimizer.state







				@state.setter
				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Tuple						)					->       Tuple:
										__magic_name__      =  state







				@property
				def      _snake_case       (				self				:    Dict						)					->       Optional[Any]:
										return self.optimizer.param_groups







				@param_groups.setter
				def      _snake_case       (				self				:    Tuple    ,     __lowerCamelCase				:    List[Any]						)					->       Dict:
										__magic_name__      =  param_groups







				@property
				def      _snake_case       (				self				:    List[Any]						)					->       Optional[int]:
										return self.optimizer.defaults







				@defaults.setter
				def      _snake_case       (				self				:    Optional[Any]    ,     __lowerCamelCase				:    Union[str, Any]						)					->       List[Any]:
										__magic_name__      =  defaults







				def      _snake_case       (				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[int]						)					->       Tuple:
										self.optimizer.add_param_group(lowerCAmelCase__						)







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Dict						)					->       List[Any]:
										if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
																xm.send_cpu_data_to_device(lowerCAmelCase__    ,     self.accelerator_state.device						)
										self.optimizer.load_state_dict(lowerCAmelCase__						)







				def      _snake_case       (				self				:    Tuple						)					->       Any:
										return self.optimizer.state_dict()







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Union[str, Any]=None						)					->       List[str]:
										if self.gradient_state.sync_gradients:
																__magic_name__      =  "set_to_none" in inspect.signature(self.optimizer.zero_grad						).parameters
																if accept_arg:
																						if set_to_none is None:
																												__magic_name__      =  False
																						self.optimizer.zero_grad(set_to_none=lowerCAmelCase__						)
																else:
																						if set_to_none is not None:
																												raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."						)
																						self.optimizer.zero_grad()







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    str=None						)					->       Dict:
										if self.gradient_state.sync_gradients:
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																						__magic_name__      =  {"closure": closure} if closure is not None else {}
																						xm.optimizer_step(self.optimizer    ,     optimizer_args=lowerCAmelCase__						)
																elif self.scaler is not None:
																						__magic_name__      =  False
																						if self._last_scale is None:
																												# `get_scale` is an async operation requiring full synchronization
																												# on CPU and GPUs before finishing. As a result, we store away
																												# the prior one to reduce the call overhead
																												__magic_name__      =  self.scaler.get_scale()
																												__magic_name__      =  True
																						self.scaler.step(self.optimizer    ,     lowerCAmelCase__						)
																						self.scaler.update()
																						__magic_name__      =  self.scaler.get_scale()
																						if not new_scale:
																												# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																												__magic_name__      =  scale_after < self._last_scale
																						__magic_name__      =  scale_after

																else:
																						self.optimizer.step(lowerCAmelCase__						)







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    List[Any]						)					->       Dict:
										for param_group in self.optimizer.param_groups:
																__magic_name__      =  [parameters_map.get(lowerCAmelCase__    ,     lowerCAmelCase__						) for p in param_group["params"]]







				@property
				def      _snake_case       (				self				:    Tuple						)					->       List[Any]:
										warnings.warn(
										    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
										    "`optimizer.step_was_skipped` instead."    ,     lowerCAmelCase__    ,     )
										return self._is_overflow







				@property
				def      _snake_case       (				self				:    List[Any]						)					->       Optional[Any]:
										return self._is_overflow







				def __getstate__(				self				:    Tuple						)					->       Dict:
										return self.__dict__.copy()







				def __setstate__(				self				:    int    ,     __lowerCamelCase				:    Dict						)					->       Union[str, Any]:
										self.__dict__.update(lowerCAmelCase__						)






lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



