






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






class 					_UpperCAmelCase       :




					def __init__(				self     :						Dict    ,						A     :						Iterable[torch.nn.Parameter]    ,						A     :						float = 0.9999    ,						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    ,						)  ->  Union[str, Any]:

							if isinstance(A    ,						torch.nn.Module   ):
									lowercase_ : List[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    ,						)
									lowercase_ : int							       =   parameters.parameters()

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

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

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

							lowercase_ : Union[str, Any]							       =   list(A   )
							lowercase_ : int							       =   [p.clone().detach() for p in parameters]

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

							lowercase_ : Optional[Any]							       =   None

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

							lowercase_ : List[str]							       =   model_cls
							lowercase_ : int							       =   model_config




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

							lowercase_ : List[str]							       =   cls(model.parameters()    ,						model_cls=A    ,						model_config=model.config   )

							ema_model.load_state_dict(A   )
							return ema_model




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

							lowercase_ : Union[str, Any]							       =   self.model_cls.from_config(self.model_config   )
							lowercase_ : Dict							       =   self.state_dict()
							state_dict.pop('''shadow_params'''    ,						A   )

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




					def   A  (				self     :						str    ,						A     :						int   )  ->  float:
							lowercase_ : Any							       =   max(0    ,						optimization_step - self.update_after_step - 1   )

							if step <= 0:
									return 0.0

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

							lowercase_ : str							       =   min(A    ,						self.decay   )
							# make sure decay is not smaller than min_decay
							lowercase_ : Any							       =   max(A    ,						self.min_decay   )
							return cur_decay_value




					@torch.no_grad()
					def   A  (				self     :						Optional[int]    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  Dict:
							if isinstance(A    ,						torch.nn.Module   ):
									lowercase_ : str							       =   (
									    '''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    ,						)
									lowercase_ : Optional[int]							       =   parameters.parameters()

							lowercase_ : List[Any]							       =   list(A   )

							self.optimization_step += 1

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

							lowercase_ : Optional[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():
											lowercase_ : Tuple							       =   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   A  (				self     :						Tuple    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  None:
							lowercase_ : int							       =   list(A   )
							for s_param, param in zip(self.shadow_params    ,						A   ):
									param.data.copy_(s_param.to(param.device   ).data   )




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




					def   A  (				self     :						Union[str, Any]   )  ->  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   A  (				self     :						Tuple    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  None:
							lowercase_ : Tuple							       =   [param.detach().cpu().clone() for param in parameters]




					def   A  (				self     :						Union[str, Any]    ,						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.
							lowercase_ : int							       =   None






					def   A  (				self     :						Tuple    ,						A     :						dict   )  ->  None:
							lowercase_ : Dict							       =   copy.deepcopy(A   )

							lowercase_ : 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'''   )

							lowercase_ : Union[str, Any]							       =   state_dict.get('''min_decay'''    ,						self.min_decay   )
							if not isinstance(self.min_decay    ,						A   ):
									raise ValueError('''Invalid min_decay'''   )

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

							lowercase_ : Union[str, 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'''   )

							lowercase_ : List[str]							       =   state_dict.get('''use_ema_warmup'''    ,						self.use_ema_warmup   )
							if not isinstance(self.use_ema_warmup    ,						A   ):
									raise ValueError('''Invalid use_ema_warmup'''   )

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

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

							lowercase_ : Optional[int]							       =   state_dict.get('''shadow_params'''    ,						A   )
							if shadow_params is not None:
									lowercase_ : Any							       =   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'''   )









"""simple docstring"""


from ..utils import DummyObject, requires_backends






class 					lowercase(     metaclass=__a ):







 '''simple docstring'''





 lowercase__ 					=					["note_seq"]


 def __init__(				self:		Dict,      *a_:		Union[str, Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(self,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Optional[int],      *a_:		Any,      **a_:		Optional[Any]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple,      *a_:		Optional[Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


