



"""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_    ( A_   :						int		):


       '''simple docstring'''





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







class 			__snake_case			:




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






         """simple docstring"""






         if isinstance(__lowerCAmelCase		,   torch.nn.Module						):
                _lowerCamelCase		:							Dict            =					(
                    '''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		,   )
                _lowerCamelCase		:							int            =					parameters.parameters()

                # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                _lowerCamelCase		:							Optional[int]            =					True

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

         if kwargs.get('''min_value'''		,   __lowerCAmelCase						) is not None:
                _lowerCamelCase		:							Optional[int]            =					'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
                deprecate('''min_value'''		,   '''1.0.0'''		,   __lowerCAmelCase		,   standard_warn=__lowerCAmelCase						)
                _lowerCamelCase		:							Optional[int]            =					kwargs['''min_value''']

         _lowerCamelCase		:							int            =					list(__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					[p.clone().detach() for p in parameters]

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

         _lowerCamelCase		:							Union[str, Any]            =					None

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

         _lowerCamelCase		:							int            =					model_cls
         _lowerCamelCase		:							Dict            =					model_config




  @classmethod
  def 		SCREAMING_SNAKE_CASE							(					cls	:		int		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase     ,       _lowerCamelCase		:							Optional[int]            =					model_cls.load_config(__lowerCAmelCase		,   return_unused_kwargs=__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					model_cls.from_pretrained(__lowerCAmelCase						)

         _lowerCamelCase		:							Union[str, Any]            =					cls(model.parameters()		,   model_cls=__lowerCAmelCase		,   model_config=model.config						)

         ema_model.load_state_dict(__lowerCAmelCase						)
         return ema_model




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		Optional[int]						):






         """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__.'''						)

         _lowerCamelCase		:							Dict            =					self.model_cls.from_config(self.model_config						)
         _lowerCamelCase		:							int            =					self.state_dict()
         state_dict.pop('''shadow_params'''		,   __lowerCAmelCase						)

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		str		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					max(0		,   optimization_step - self.update_after_step - 1						)

         if step <= 0:
                return 0.0

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

         _lowerCamelCase		:							List[Any]            =					min(__lowerCAmelCase		,   self.decay						)
         # make sure decay is not smaller than min_decay
         _lowerCamelCase		:							str            =					max(__lowerCAmelCase		,   self.min_decay						)
         return cur_decay_value




  @torch.no_grad()
  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		Iterable[torch.nn.Parameter]						):






         """simple docstring"""






         if isinstance(__lowerCAmelCase		,   torch.nn.Module						):
                _lowerCamelCase		:							int            =					(
                    '''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		,   )
                _lowerCamelCase		:							Dict            =					parameters.parameters()

         _lowerCamelCase		:							List[Any]            =					list(__lowerCAmelCase						)

         self.optimization_step += 1

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

         _lowerCamelCase		:							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		,   __lowerCAmelCase						):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                       _lowerCamelCase		:							Optional[int]            =					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 		SCREAMING_SNAKE_CASE							(					self	:		str		,   __lowerCAmelCase	:		Iterable[torch.nn.Parameter]						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					list(__lowerCAmelCase						)
         for s_param, param in zip(self.shadow_params		,   __lowerCAmelCase						):
                param.data.copy_(s_param.to(param.device						).data						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		List[Any]=None		,   __lowerCAmelCase	:		List[str]=None						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					[
             p.to(device=__lowerCAmelCase		,   dtype=__lowerCAmelCase						) if p.is_floating_point() else p.to(device=__lowerCAmelCase						)
             for p in self.shadow_params
         ]




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """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 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		Iterable[torch.nn.Parameter]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					[param.detach().cpu().clone() for param in parameters]




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		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.
         _lowerCamelCase		:							str            =					None




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		dict						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					copy.deepcopy(__lowerCAmelCase						)

         _lowerCamelCase		:							str            =					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		,   __lowerCAmelCase						):
                raise ValueError('''Invalid min_decay'''						)

         _lowerCamelCase		:							Union[str, Any]            =					state_dict.get('''optimization_step'''		,   self.optimization_step						)
         if not isinstance(self.optimization_step		,   __lowerCAmelCase						):
                raise ValueError('''Invalid optimization_step'''						)

         _lowerCamelCase		:							Optional[Any]            =					state_dict.get('''update_after_step'''		,   self.update_after_step						)
         if not isinstance(self.update_after_step		,   __lowerCAmelCase						):
                raise ValueError('''Invalid update_after_step'''						)

         _lowerCamelCase		:							Tuple            =					state_dict.get('''use_ema_warmup'''		,   self.use_ema_warmup						)
         if not isinstance(self.use_ema_warmup		,   __lowerCAmelCase						):
                raise ValueError('''Invalid use_ema_warmup'''						)

         _lowerCamelCase		:							int            =					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		:							List[Any]            =					state_dict.get('''shadow_params'''		,   __lowerCAmelCase						)
         if shadow_params is not None:
                _lowerCamelCase		:							Optional[Any]            =					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"""






from ..utils import DummyObject, requires_backends



class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						Union[str, Any]			,     *_SCREAMING_SNAKE_CASE:						Any			,     **_SCREAMING_SNAKE_CASE:						int)							->			List[Any]:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Union[str, Any]			,     *_SCREAMING_SNAKE_CASE:						List[Any]			,     **_SCREAMING_SNAKE_CASE:						Union[str, Any])							->			Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						int			,     *_SCREAMING_SNAKE_CASE:						Any			,     **_SCREAMING_SNAKE_CASE:						str)							->			Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])






class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						Tuple			,     *_SCREAMING_SNAKE_CASE:						Optional[Any]			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			Optional[int]:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Dict			,     *_SCREAMING_SNAKE_CASE:						Optional[Any]			,     **_SCREAMING_SNAKE_CASE:						int)							->			List[Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Optional[Any]			,     *_SCREAMING_SNAKE_CASE:						Dict			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])






class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						int			,     *_SCREAMING_SNAKE_CASE:						List[Any]			,     **_SCREAMING_SNAKE_CASE:						int)							->			List[str]:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Dict			,     *_SCREAMING_SNAKE_CASE:						Any			,     **_SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						List[str]			,     **_SCREAMING_SNAKE_CASE:						int)							->			int:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])






class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						Tuple			,     *_SCREAMING_SNAKE_CASE:						List[str]			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						List[str]			,     *_SCREAMING_SNAKE_CASE:						Tuple			,     **_SCREAMING_SNAKE_CASE:						int)							->			int:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Optional[int]			,     *_SCREAMING_SNAKE_CASE:						str			,     **_SCREAMING_SNAKE_CASE:						Any)							->			int:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])






class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						Optional[Any]			,     *_SCREAMING_SNAKE_CASE:						Union[str, Any]			,     **_SCREAMING_SNAKE_CASE:						Optional[int])							->			str:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Optional[int]			,     *_SCREAMING_SNAKE_CASE:						List[Any]			,     **_SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						int			,     *_SCREAMING_SNAKE_CASE:						str			,     **_SCREAMING_SNAKE_CASE:						Any)							->			Optional[Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])






class      A__  (    metaclass=__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['torch', 'transformers', 'onnx']

	def __init__(	self:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						List[str]			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			int:

				"""simple docstring"""

				requires_backends(self			,     ["torch", "transformers", "onnx"])

	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Union[str, Any]			,     *_SCREAMING_SNAKE_CASE:						Optional[int]			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			List[Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						str			,     *_SCREAMING_SNAKE_CASE:						Optional[int]			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				requires_backends(cls			,     ["torch", "transformers", "onnx"])