




"""simple docstring"""



import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers





def  _snake_case     (    UpperCAmelCase_  :					int			):
     random.seed(_lowerCamelCase			)
     np.random.seed(_lowerCamelCase			)
     torch.manual_seed(_lowerCamelCase			)
     torch.cuda.manual_seed_all(_lowerCamelCase			)
     # ^^ safe to call this function even if cuda is not available



class 					a						:



    """simple docstring"""



    def __init__(     self: Dict    ,						UpperCamelCase: Iterable[torch.nn.Parameter]    ,						UpperCamelCase: float = 0.9_999    ,						UpperCamelCase: float = 0.0    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Union[float, int] = 1.0    ,						UpperCamelCase: Union[float, int] = 2 / 3    ,						UpperCamelCase: Optional[Any] = None    ,						UpperCamelCase: Dict[str, Any] = None    ,						**UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


         if isinstance(__lowerCamelCase    ,						torch.nn.Module       ):
              A__										=  (
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""    ,						"""1.0.0"""    ,						__lowerCamelCase    ,						standard_warn=__lowerCamelCase    ,						)
              A__										=  parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              A__										=  True

         if kwargs.get("""max_value"""    ,						__lowerCamelCase       ) is not None:
              A__										=  "The `max_value` argument is deprecated. Please use `decay` instead."
              deprecate("""max_value"""    ,						"""1.0.0"""    ,						__lowerCamelCase    ,						standard_warn=__lowerCamelCase       )
              A__										=  kwargs["max_value"]

         if kwargs.get("""min_value"""    ,						__lowerCamelCase       ) is not None:
              A__										=  "The `min_value` argument is deprecated. Please use `min_decay` instead."
              deprecate("""min_value"""    ,						"""1.0.0"""    ,						__lowerCamelCase    ,						standard_warn=__lowerCamelCase       )
              A__										=  kwargs["min_value"]

         A__										=  list(__lowerCamelCase       )
         A__										=  [p.clone().detach() for p in parameters]

         if kwargs.get("""device"""    ,						__lowerCamelCase       ) is not None:
              A__										=  "The `device` argument is deprecated. Please use `to` instead."
              deprecate("""device"""    ,						"""1.0.0"""    ,						__lowerCamelCase    ,						standard_warn=__lowerCamelCase       )
              self.to(device=kwargs["""device"""]       )

         A__										=  None

         A__										=  decay
         A__										=  min_decay
         A__										=  update_after_step
         A__										=  use_ema_warmup
         A__										=  inv_gamma
         A__										=  power
         A__										=  0
         A__										=  None  # set in `step()`

         A__										=  model_cls
         A__										=  model_config

    @classmethod
    def 				UpperCamelCase	(     cls: Any    ,						UpperCamelCase: int    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  model_cls.load_config(__lowerCamelCase    ,						return_unused_kwargs=__lowerCamelCase       )
         A__										=  model_cls.from_pretrained(__lowerCamelCase       )

         A__										=  cls(model.parameters()    ,						model_cls=__lowerCamelCase    ,						model_config=model.config       )

         ema_model.load_state_dict(__lowerCamelCase       )
         return ema_model

    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         if self.model_cls is None:
              raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""       )

         if self.model_config is None:
              raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""       )

         A__										=  self.model_cls.from_config(self.model_config       )
         A__										=  self.state_dict()
         state_dict.pop("""shadow_params"""    ,						__lowerCamelCase       )

         model.register_to_config(**__lowerCamelCase       )
         self.copy_to(model.parameters()       )
         model.save_pretrained(__lowerCamelCase       )

    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  max(0    ,						optimization_step - self.update_after_step - 1       )

         if step <= 0:
              return 0.0

         if self.use_ema_warmup:
              A__										=  1 - (1 + step / self.inv_gamma) ** -self.power
         else:
              A__										=  (1 + step) / (10 + step)

         A__										=  min(__lowerCamelCase    ,						self.decay       )
         # make sure decay is not smaller than min_decay
         A__										=  max(__lowerCamelCase    ,						self.min_decay       )
         return cur_decay_value

    @torch.no_grad()
    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Iterable[torch.nn.Parameter]       ):






         """simple docstring"""


         if isinstance(__lowerCamelCase    ,						torch.nn.Module       ):
              A__										=  (
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""    ,						"""1.0.0"""    ,						__lowerCamelCase    ,						standard_warn=__lowerCamelCase    ,						)
              A__										=  parameters.parameters()

         A__										=  list(__lowerCamelCase       )

         self.optimization_step += 1

         # Compute the decay factor for the exponential moving average.
         A__										=  self.get_decay(self.optimization_step       )
         A__										=  decay
         A__										=  1 - decay

         A__										=  contextlib.nullcontext
         if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              import deepspeed

         for s_param, param in zip(self.shadow_params    ,						__lowerCamelCase       ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   A__										=  deepspeed.zero.GatheredParameters(__lowerCamelCase    ,						modifier_rank=__lowerCamelCase       )

              with context_manager():
                   if param.requires_grad:
                        s_param.sub_(one_minus_decay * (s_param - param)       )
                   else:
                        s_param.copy_(__lowerCamelCase       )

    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: Iterable[torch.nn.Parameter]       ):






         """simple docstring"""


         A__										=  list(__lowerCamelCase       )
         for s_param, param in zip(self.shadow_params    ,						__lowerCamelCase       ):
              param.data.copy_(s_param.to(param.device       ).data       )

    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: str=None    ,						UpperCamelCase: int=None       ):






         """simple docstring"""


         A__										=  [
             p.to(device=__lowerCamelCase    ,						dtype=__lowerCamelCase       ) if p.is_floating_point() else p.to(device=__lowerCamelCase       )
             for p in self.shadow_params
         ]

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         return {
             "decay": self.decay,
             "min_decay": self.min_decay,
             "optimization_step": self.optimization_step,
             "update_after_step": self.update_after_step,
             "use_ema_warmup": self.use_ema_warmup,
             "inv_gamma": self.inv_gamma,
             "power": self.power,
             "shadow_params": self.shadow_params,
         }

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Iterable[torch.nn.Parameter]       ):






         """simple docstring"""


         A__										=  [param.detach().cpu().clone() for param in parameters]

    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: Iterable[torch.nn.Parameter]       ):






         """simple docstring"""


         if self.temp_stored_params is None:
              raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""       )
         for c_param, param in zip(self.temp_stored_params    ,						__lowerCamelCase       ):
              param.data.copy_(c_param.data       )

         # Better memory-wise.
         A__										=  None





    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: dict       ):






         """simple docstring"""


         A__										=  copy.deepcopy(__lowerCamelCase       )

         A__										=  state_dict.get("""decay"""    ,						self.decay       )
         if self.decay < 0.0 or self.decay > 1.0:
              raise ValueError("""Decay must be between 0 and 1"""       )

         A__										=  state_dict.get("""min_decay"""    ,						self.min_decay       )
         if not isinstance(self.min_decay    ,						__lowerCamelCase       ):
              raise ValueError("""Invalid min_decay"""       )

         A__										=  state_dict.get("""optimization_step"""    ,						self.optimization_step       )
         if not isinstance(self.optimization_step    ,						__lowerCamelCase       ):
              raise ValueError("""Invalid optimization_step"""       )

         A__										=  state_dict.get("""update_after_step"""    ,						self.update_after_step       )
         if not isinstance(self.update_after_step    ,						__lowerCamelCase       ):
              raise ValueError("""Invalid update_after_step"""       )

         A__										=  state_dict.get("""use_ema_warmup"""    ,						self.use_ema_warmup       )
         if not isinstance(self.use_ema_warmup    ,						__lowerCamelCase       ):
              raise ValueError("""Invalid use_ema_warmup"""       )

         A__										=  state_dict.get("""inv_gamma"""    ,						self.inv_gamma       )
         if not isinstance(self.inv_gamma    ,						(float, int)       ):
              raise ValueError("""Invalid inv_gamma"""       )

         A__										=  state_dict.get("""power"""    ,						self.power       )
         if not isinstance(self.power    ,						(float, int)       ):
              raise ValueError("""Invalid power"""       )

         A__										=  state_dict.get("""shadow_params"""    ,						__lowerCamelCase       )
         if shadow_params is not None:
              A__										=  shadow_params
              if not isinstance(self.shadow_params    ,						__lowerCamelCase       ):
                   raise ValueError("""shadow_params must be a list"""       )
              if not all(isinstance(__lowerCamelCase    ,						torch.Tensor       ) for p in self.shadow_params       ):
                   raise ValueError("""shadow_params must all be Tensors"""       )









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(UpperCAmelCase_			)			):
          if indegree[i] == 0:
               queue.append(UpperCAmelCase_			)

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(UpperCAmelCase_			)

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






