'''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 _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       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       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = 0.9_9_9_9				,     UpperCamelCase_ = 0.0				,     UpperCamelCase_ = 0				,     UpperCamelCase_ = False				,     UpperCamelCase_ = 1.0				,     UpperCamelCase_ = 2 / 3				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):

              if isinstance(UpperCamelCase_				,     torch.nn.Module      ):
                     __UpperCAmelCase  :				int       				=		(
                         "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"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_				,     )
                     __UpperCAmelCase  :				Optional[int]       				=		parameters.parameters()

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

              if kwargs.get("max_value"				,     UpperCamelCase_      ) is not None:
                     __UpperCAmelCase  :				Dict       				=		"The `max_value` argument is deprecated. Please use `decay` instead."
                     deprecate("max_value"				,     "1.0.0"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_      )
                     __UpperCAmelCase  :				Optional[Any]       				=		kwargs["max_value"]

              if kwargs.get("min_value"				,     UpperCamelCase_      ) is not None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		"The `min_value` argument is deprecated. Please use `min_decay` instead."
                     deprecate("min_value"				,     "1.0.0"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_      )
                     __UpperCAmelCase  :				int       				=		kwargs["min_value"]

              __UpperCAmelCase  :				Union[str, Any]       				=		list(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		[p.clone().detach() for p in parameters]

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

              __UpperCAmelCase  :				int       				=		None

              __UpperCAmelCase  :				int       				=		decay
              __UpperCAmelCase  :				Any       				=		min_decay
              __UpperCAmelCase  :				Any       				=		update_after_step
              __UpperCAmelCase  :				int       				=		use_ema_warmup
              __UpperCAmelCase  :				Dict       				=		inv_gamma
              __UpperCAmelCase  :				Any       				=		power
              __UpperCAmelCase  :				int       				=		0
              __UpperCAmelCase  :				Dict       				=		None  # set in `step()`

              __UpperCAmelCase  :				List[str]       				=		model_cls
              __UpperCAmelCase  :				Optional[int]       				=		model_config






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		model_cls.load_config(UpperCamelCase_				,     return_unused_kwargs=UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		model_cls.from_pretrained(UpperCamelCase_      )

              __UpperCAmelCase  :				int       				=		cls(model.parameters()				,     model_cls=UpperCamelCase_				,     model_config=model.config      )

              ema_model.load_state_dict(UpperCamelCase_      )
              return ema_model






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              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__."      )

              __UpperCAmelCase  :				int       				=		self.model_cls.from_config(self.model_config      )
              __UpperCAmelCase  :				Any       				=		self.state_dict()
              state_dict.pop("shadow_params"				,     UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		max(0				,     optimization_step - self.update_after_step - 1      )

              if step <= 0:
                     return 0.0

              if self.use_ema_warmup:
                     __UpperCAmelCase  :				Optional[int]       				=		1 - (1 + step / self.inv_gamma) ** -self.power
              else:
                     __UpperCAmelCase  :				Any       				=		(1 + step) / (10 + step)

              __UpperCAmelCase  :				Any       				=		min(UpperCamelCase_				,     self.decay      )
              # make sure decay is not smaller than min_decay
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_				,     self.min_decay      )
              return cur_decay_value






       @torch.no_grad()
       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if isinstance(UpperCamelCase_				,     torch.nn.Module      ):
                     __UpperCAmelCase  :				Optional[Any]       				=		(
                         "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"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_				,     )
                     __UpperCAmelCase  :				Tuple       				=		parameters.parameters()

              __UpperCAmelCase  :				Optional[int]       				=		list(UpperCamelCase_      )

              self.optimization_step += 1

              # Compute the decay factor for the exponential moving average.
              __UpperCAmelCase  :				Optional[Any]       				=		self.get_decay(self.optimization_step      )
              __UpperCAmelCase  :				int       				=		decay
              __UpperCAmelCase  :				int       				=		1 - decay

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

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		list(UpperCamelCase_      )
              for s_param, param in zip(self.shadow_params				,     UpperCamelCase_      ):
                     param.data.copy_(s_param.to(param.device      ).data      )






       def 			_snake_case						(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=None      ):
              __UpperCAmelCase  :				str       				=		[
                  p.to(device=UpperCamelCase_				,     dtype=UpperCamelCase_      ) if p.is_floating_point() else p.to(device=UpperCamelCase_      )
                  for p in self.shadow_params
              ]






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		[param.detach().cpu().clone() for param in parameters]






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              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				,     UpperCamelCase_      ):
                     param.data.copy_(c_param.data      )

              # Better memory-wise.
              __UpperCAmelCase  :				str       				=		None







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		copy.deepcopy(UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		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"      )

              __UpperCAmelCase  :				Dict       				=		state_dict.get("min_decay"				,     self.min_decay      )
              if not isinstance(self.min_decay				,     UpperCamelCase_      ):
                     raise ValueError("Invalid min_decay"      )

              __UpperCAmelCase  :				Optional[Any]       				=		state_dict.get("optimization_step"				,     self.optimization_step      )
              if not isinstance(self.optimization_step				,     UpperCamelCase_      ):
                     raise ValueError("Invalid optimization_step"      )

              __UpperCAmelCase  :				List[str]       				=		state_dict.get("update_after_step"				,     self.update_after_step      )
              if not isinstance(self.update_after_step				,     UpperCamelCase_      ):
                     raise ValueError("Invalid update_after_step"      )

              __UpperCAmelCase  :				Dict       				=		state_dict.get("use_ema_warmup"				,     self.use_ema_warmup      )
              if not isinstance(self.use_ema_warmup				,     UpperCamelCase_      ):
                     raise ValueError("Invalid use_ema_warmup"      )

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

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

              __UpperCAmelCase  :				str       				=		state_dict.get("shadow_params"				,     UpperCamelCase_      )
              if shadow_params is not None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		shadow_params
                     if not isinstance(self.shadow_params				,     UpperCamelCase_      ):
                            raise ValueError("shadow_params must be a list"      )
                     if not all(isinstance(UpperCamelCase_				,     torch.Tensor      ) for p in self.shadow_params      ):
                            raise ValueError("shadow_params must all be Tensors"      )







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       if index == len(lowerCamelCase__						):
              return True

       # Recursive Step
       for i in range(lowerCamelCase__						):
              if valid_coloring(graph[index]							,    lowerCamelCase__							,    lowerCamelCase__						):
                     # Color current vertex
                     __UpperCAmelCase  :				List[str]       				=		i
                     # Validate coloring
                     if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    index + 1						):
                            return True
                     # Backtrack
                     __UpperCAmelCase  :				Any       				=		-1
       return False


def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		[-1] * len(lowerCamelCase__						)

       if util_color(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    0						):
              return colored_vertices

       return []






