





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 						lowerCamelCase__     (     UpperCamelCase__			:       int						)			-> List[Any]:



					'''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 			UpperCamelCase_		:




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = 0.99_99				,						lowerCAmelCase_ = 0.0				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = 1.0				,						lowerCAmelCase_ = 2 / 3				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Tuple:

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

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

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

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

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

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

										_snake_case									=				None

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

										_snake_case									=				model_cls
										_snake_case									=				model_config




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							"EMAModel":
										_snake_case							,     _snake_case									=				model_cls.load_config(__UpperCamelCase				,						return_unused_kwargs=__UpperCamelCase  )
										_snake_case									=				model_cls.from_pretrained(__UpperCamelCase  )

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

										ema_model.load_state_dict(__UpperCamelCase  )
										return ema_model




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							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__.'  )

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

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							float:
										_snake_case									=				max(0				,						optimization_step - self.update_after_step - 1  )

										if step <= 0:
															return 0.0

										if self.use_ema_warmup:
															_snake_case									=				1 - (1 + step / self.inv_gamma) ** -self.power
										else:
															_snake_case									=				(1 + step) / (10 + step)

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




					@torch.no_grad()
					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										if isinstance(__UpperCamelCase				,						torch.nn.Module  ):
															_snake_case									=				(
															    '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				,						)
															_snake_case									=				parameters.parameters()

										_snake_case									=				list(__UpperCamelCase  )

										self.optimization_step += 1

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

										_snake_case									=				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():
																				_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							None:
										_snake_case									=				list(__UpperCamelCase  )
										for s_param, param in zip(self.shadow_params				,						__UpperCamelCase  ):
															param.data.copy_(s_param.to(param.device  ).data  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None  )		->							None:
										_snake_case									=				[
										    p.to(device=__UpperCamelCase				,						dtype=__UpperCamelCase  ) if p.is_floating_point() else p.to(device=__UpperCamelCase  )
										    for p in self.shadow_params
										]




					def 	lowerCAmelCase			(		self  )		->							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				,						lowerCAmelCase_  )		->							None:
										_snake_case									=				[param.detach().cpu().clone() for param in parameters]




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							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.
										_snake_case									=				None






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							None:
										_snake_case									=				copy.deepcopy(__UpperCamelCase  )

										_snake_case									=				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'  )

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

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

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

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

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

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

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










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										debug_launcher(test_script.main  )






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										debug_launcher(test_ops.main  )



