





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 	_a						(   UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   random.seed(UpperCAmelCase					)
   np.random.seed(UpperCAmelCase					)
   torch.manual_seed(UpperCAmelCase					)
   torch.cuda.manual_seed_all(UpperCAmelCase					)
   # ^^ safe to call this function even if cuda is not available




class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Optional[Any]		, A					:     Iterable[torch.nn.Parameter]		, A					:     float = 0.99_99		, A					:     float = 0.0		, A					:     int = 0		, A					:     bool = False		, A					:     Union[float, int] = 1.0		, A					:     Union[float, int] = 2 / 3		, A					:     Optional[Any] = None		, A					:     Dict[str, Any] = None		, **A					:     str		, )       ->int:

          if isinstance(A		, torch.nn.Module      ):
             lowerCamelCase__       :						Any        =							(
                 '''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'''		, A		, standard_warn=A		, )
             lowerCamelCase__       :						Tuple        =							parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             lowerCamelCase__       :						List[Any]        =							True

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

          if kwargs.get('''min_value'''		, A      ) is not None:
             lowerCamelCase__       :						str        =							'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
             deprecate('''min_value'''		, '''1.0.0'''		, A		, standard_warn=A      )
             lowerCamelCase__       :						List[str]        =							kwargs['''min_value''']

          lowerCamelCase__       :						str        =							list(A      )
          lowerCamelCase__       :						List[str]        =							[p.clone().detach() for p in parameters]

          if kwargs.get('''device'''		, A      ) is not None:
             lowerCamelCase__       :						List[Any]        =							'''The `device` argument is deprecated. Please use `to` instead.'''
             deprecate('''device'''		, '''1.0.0'''		, A		, standard_warn=A      )
             self.to(device=kwargs['''device''']      )

          lowerCamelCase__       :						Union[str, Any]        =							None

          lowerCamelCase__       :						Tuple        =							decay
          lowerCamelCase__       :						Tuple        =							min_decay
          lowerCamelCase__       :						Optional[int]        =							update_after_step
          lowerCamelCase__       :						str        =							use_ema_warmup
          lowerCamelCase__       :						Any        =							inv_gamma
          lowerCamelCase__       :						int        =							power
          lowerCamelCase__       :						int        =							0
          lowerCamelCase__       :						Optional[int]        =							None  # set in `step()`

          lowerCamelCase__       :						Any        =							model_cls
          lowerCamelCase__       :						List[Any]        =							model_config







       @classmethod
       def 						__lowerCamelCase   (				cls					:     int		, A					:     Union[str, Any]		, A					:     List[Any]      )       ->"EMAModel":
          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							model_cls.load_config(A		, return_unused_kwargs=A      )
          lowerCamelCase__       :						Any        =							model_cls.from_pretrained(A      )

          lowerCamelCase__       :						Optional[int]        =							cls(model.parameters()		, model_cls=A		, model_config=model.config      )

          ema_model.load_state_dict(A      )
          return ema_model







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     List[str]      )       ->List[str]:
          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__.'''      )

          lowerCamelCase__       :						int        =							self.model_cls.from_config(self.model_config      )
          lowerCamelCase__       :						List[str]        =							self.state_dict()
          state_dict.pop('''shadow_params'''		, A      )

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







       def 						__lowerCamelCase   (				self					:     str		, A					:     int      )       ->float:
          lowerCamelCase__       :						Optional[Any]        =							max(0		, optimization_step - self.update_after_step - 1      )

          if step <= 0:
             return 0.0

          if self.use_ema_warmup:
             lowerCamelCase__       :						str        =							1 - (1 + step / self.inv_gamma) ** -self.power
          else:
             lowerCamelCase__       :						Any        =							(1 + step) / (1_0 + step)

          lowerCamelCase__       :						Optional[int]        =							min(A		, self.decay      )
          # make sure decay is not smaller than min_decay
          lowerCamelCase__       :						Union[str, Any]        =							max(A		, self.min_decay      )
          return cur_decay_value







       @torch.no_grad()
       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Iterable[torch.nn.Parameter]      )       ->List[Any]:
          if isinstance(A		, torch.nn.Module      ):
             lowerCamelCase__       :						List[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'''		, A		, standard_warn=A		, )
             lowerCamelCase__       :						str        =							parameters.parameters()

          lowerCamelCase__       :						int        =							list(A      )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          lowerCamelCase__       :						List[str]        =							self.get_decay(self.optimization_step      )
          lowerCamelCase__       :						Union[str, Any]        =							decay
          lowerCamelCase__       :						List[str]        =							1 - decay

          lowerCamelCase__       :						List[Any]        =							contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

          for s_param, param in zip(self.shadow_params		, A      ):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                lowerCamelCase__       :						Optional[int]        =							deepspeed.zero.GatheredParameters(A		, modifier_rank=A      )

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







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Iterable[torch.nn.Parameter]      )       ->None:
          lowerCamelCase__       :						Union[str, Any]        =							list(A      )
          for s_param, param in zip(self.shadow_params		, A      ):
             param.data.copy_(s_param.to(param.device      ).data      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     List[Any]=None		, A					:     List[Any]=None      )       ->None:
          lowerCamelCase__       :						Optional[int]        =							[
              p.to(device=A		, dtype=A      ) if p.is_floating_point() else p.to(device=A      )
              for p in self.shadow_params
          ]







       def 						__lowerCamelCase   (				self					:     Dict      )       ->dict:
          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 						__lowerCamelCase   (				self					:     Any		, A					:     Iterable[torch.nn.Parameter]      )       ->None:
          lowerCamelCase__       :						Optional[int]        =							[param.detach().cpu().clone() for param in parameters]







       def 						__lowerCamelCase   (				self					:     int		, A					:     Iterable[torch.nn.Parameter]      )       ->None:
          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		, A      ):
             param.data.copy_(c_param.data      )

          # Better memory-wise.
          lowerCamelCase__       :						Optional[int]        =							None







       def 						__lowerCamelCase   (				self					:     Any		, A					:     dict      )       ->None:
          lowerCamelCase__       :						List[str]        =							copy.deepcopy(A      )

          lowerCamelCase__       :						List[Any]        =							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'''      )

          lowerCamelCase__       :						Any        =							state_dict.get('''min_decay'''		, self.min_decay      )
          if not isinstance(self.min_decay		, A      ):
             raise ValueError('''Invalid min_decay'''      )

          lowerCamelCase__       :						str        =							state_dict.get('''optimization_step'''		, self.optimization_step      )
          if not isinstance(self.optimization_step		, A      ):
             raise ValueError('''Invalid optimization_step'''      )

          lowerCamelCase__       :						Any        =							state_dict.get('''update_after_step'''		, self.update_after_step      )
          if not isinstance(self.update_after_step		, A      ):
             raise ValueError('''Invalid update_after_step'''      )

          lowerCamelCase__       :						Dict        =							state_dict.get('''use_ema_warmup'''		, self.use_ema_warmup      )
          if not isinstance(self.use_ema_warmup		, A      ):
             raise ValueError('''Invalid use_ema_warmup'''      )

          lowerCamelCase__       :						List[Any]        =							state_dict.get('''inv_gamma'''		, self.inv_gamma      )
          if not isinstance(self.inv_gamma		, (float, int)      ):
             raise ValueError('''Invalid inv_gamma'''      )

          lowerCamelCase__       :						Tuple        =							state_dict.get('''power'''		, self.power      )
          if not isinstance(self.power		, (float, int)      ):
             raise ValueError('''Invalid power'''      )

          lowerCamelCase__       :						Optional[int]        =							state_dict.get('''shadow_params'''		, A      )
          if shadow_params is not None:
             lowerCamelCase__       :						str        =							shadow_params
             if not isinstance(self.shadow_params		, A      ):
                raise ValueError('''shadow_params must be a list'''      )
             if not all(isinstance(A		, torch.Tensor      ) for p in self.shadow_params      ):
                raise ValueError('''shadow_params must all be Tensors'''      )










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



