



"""simple docstring"""


from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[str] = None				):






				'''simple docstring'''



				if version.parse(hfh.__version__				).release < version.parse('''0.11.0'''				).release:
								# old versions of hfh don't url-encode the file path
								_UpperCAmelCase					=    quote(_SCREAMING_SNAKE_CASE				)
				return hfh.hf_hub_url(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''					,     revision=_SCREAMING_SNAKE_CASE				)








"""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	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]				,   __UpperCamelCase     :    float = 0.9_9_9_9				,   __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     :    Optional[Any]				,   )->Tuple:

							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''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					=    parameters.parameters()

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

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

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

							_UpperCAmelCase					=    list(__UpperCamelCase   )
							_UpperCAmelCase					=    [p.clone().detach() for p in parameters]

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

							_UpperCAmelCase					=    None

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

							_UpperCAmelCase					=    model_cls
							_UpperCAmelCase					=    model_config



			@classmethod
			def  lowercase__						(		cls     :    List[str]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any   )->"EMAModel":
							_UpperCAmelCase		,						_UpperCAmelCase					=    model_cls.load_config(__UpperCamelCase				,   return_unused_kwargs=__UpperCamelCase   )
							_UpperCAmelCase					=    model_cls.from_pretrained(__UpperCamelCase   )

							_UpperCAmelCase					=    cls(model.parameters()				,   model_cls=__UpperCamelCase				,   model_config=model.config   )

							ema_model.load_state_dict(__UpperCamelCase   )
							return ema_model



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Any   )->Optional[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__.'''   )

							_UpperCAmelCase					=    self.model_cls.from_config(self.model_config   )
							_UpperCAmelCase					=    self.state_dict()
							state_dict.pop('''shadow_params'''				,   __UpperCamelCase   )

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



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    int   )->float:
							_UpperCAmelCase					=    max(0				,   optimization_step - self.update_after_step - 1   )

							if step <= 0:
											return 0.0

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

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



			@torch.no_grad()
			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->Optional[Any]:
							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''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					=    parameters.parameters()

							_UpperCAmelCase					=    list(__UpperCamelCase   )

							self.optimization_step += 1

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

							_UpperCAmelCase					=    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					=    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  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    list(__UpperCamelCase   )
							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											param.data.copy_(s_param.to(param.device   ).data   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Union[str, Any]=None   )->None:
							_UpperCAmelCase					=    [
							    p.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   ) if p.is_floating_point() else p.to(device=__UpperCamelCase   )
							    for p in self.shadow_params
							]



			def  lowercase__						(		self     :    Optional[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  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    [param.detach().cpu().clone() for param in parameters]



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    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				,   __UpperCamelCase   ):
											param.data.copy_(c_param.data   )

							# Better memory-wise.
							_UpperCAmelCase					=    None



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    dict   )->None:
							_UpperCAmelCase					=    copy.deepcopy(__UpperCamelCase   )

							_UpperCAmelCase					=    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					=    state_dict.get('''min_decay'''				,   self.min_decay   )
							if not isinstance(self.min_decay				,   __UpperCamelCase   ):
											raise ValueError('''Invalid min_decay'''   )

							_UpperCAmelCase					=    state_dict.get('''optimization_step'''				,   self.optimization_step   )
							if not isinstance(self.optimization_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid optimization_step'''   )

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

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

							_UpperCAmelCase					=    state_dict.get('''shadow_params'''				,   __UpperCamelCase   )
							if shadow_params is not None:
											_UpperCAmelCase					=    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'''   )



