




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   SCREAMING_SNAKE_CASE       (				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 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Dict							,	__A  :						Iterable[torch.nn.Parameter]							,	__A  :						float = 0.9_9_9_9							,	__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  :						List[str]							,	):

						if isinstance(__A							,	torch.nn.Module ):
							snake_case__				:   Tuple     =						(
							    "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							,	)
							snake_case__				:   Union[str, Any]     =						parameters.parameters()

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

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

						if kwargs.get("min_value"							,	__A ) is not None:
							snake_case__				:   Dict     =						"The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"							,	"1.0.0"							,	__A							,	standard_warn=__A )
							snake_case__				:   Any     =						kwargs["min_value"]

						snake_case__				:   Tuple     =						list(__A )
						snake_case__				:   str     =						[p.clone().detach() for p in parameters]

						if kwargs.get("device"							,	__A ) is not None:
							snake_case__				:   Dict     =						"The `device` argument is deprecated. Please use `to` instead."
							deprecate("device"							,	"1.0.0"							,	__A							,	standard_warn=__A )
							self.to(device=kwargs["device"] )

						snake_case__				:   List[str]     =						None

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

						snake_case__				:   List[Any]     =						model_cls
						snake_case__				:   int     =						model_config



					@classmethod
					def  _lowercase  (      cls  :						Dict							,	__A  :						Optional[int]							,	__A  :						int ):
						snake_case__				:   Any     =						model_cls.load_config(__A							,	return_unused_kwargs=__A )
						snake_case__				:   Tuple     =						model_cls.from_pretrained(__A )

						snake_case__				:   List[Any]     =						cls(model.parameters()							,	model_cls=__A							,	model_config=model.config )

						ema_model.load_state_dict(__A )
						return ema_model



					def  _lowercase  (      self  :						int							,	__A  :						int ):
						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__				:   Any     =						self.model_cls.from_config(self.model_config )
						snake_case__				:   Union[str, Any]     =						self.state_dict()
						state_dict.pop("shadow_params"							,	__A )

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



					def  _lowercase  (      self  :						Tuple							,	__A  :						int ):
						snake_case__				:   str     =						max(0							,	optimization_step - self.update_after_step - 1 )

						if step <= 0:
							return 0.0

						if self.use_ema_warmup:
							snake_case__				:   int     =						1 - (1 + step / self.inv_gamma) ** -self.power
						else:
							snake_case__				:   Union[str, Any]     =						(1 + step) / (1_0 + step)

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



					@torch.no_grad()
					def  _lowercase  (      self  :						Any							,	__A  :						Iterable[torch.nn.Parameter] ):
						if isinstance(__A							,	torch.nn.Module ):
							snake_case__				:   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"							,	__A							,	standard_warn=__A							,	)
							snake_case__				:   str     =						parameters.parameters()

						snake_case__				:   List[Any]     =						list(__A )

						self.optimization_step += 1

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

						snake_case__				:   Tuple     =						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():
								snake_case__				:   Any     =						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  _lowercase  (      self  :						str							,	__A  :						Iterable[torch.nn.Parameter] ):
						snake_case__				:   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  _lowercase  (      self  :						Optional[int]							,	__A  :						Tuple=None							,	__A  :						Tuple=None ):
						snake_case__				:   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  _lowercase  (      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  _lowercase  (      self  :						Tuple							,	__A  :						Iterable[torch.nn.Parameter] ):
						snake_case__				:   Any     =						[param.detach().cpu().clone() for param in parameters]



					def  _lowercase  (      self  :						str							,	__A  :						Iterable[torch.nn.Parameter] ):
						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.
						snake_case__				:   Dict     =						None






					def  _lowercase  (      self  :						List[str]							,	__A  :						dict ):
						snake_case__				:   Tuple     =						copy.deepcopy(__A )

						snake_case__				:   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" )

						snake_case__				:   Optional[Any]     =						state_dict.get("min_decay"							,	self.min_decay )
						if not isinstance(self.min_decay							,	__A ):
							raise ValueError("Invalid min_decay" )

						snake_case__				:   int     =						state_dict.get("optimization_step"							,	self.optimization_step )
						if not isinstance(self.optimization_step							,	__A ):
							raise ValueError("Invalid optimization_step" )

						snake_case__				:   List[str]     =						state_dict.get("update_after_step"							,	self.update_after_step )
						if not isinstance(self.update_after_step							,	__A ):
							raise ValueError("Invalid update_after_step" )

						snake_case__				:   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" )

						snake_case__				:   Any     =						state_dict.get("inv_gamma"							,	self.inv_gamma )
						if not isinstance(self.inv_gamma							,	(float, int) ):
							raise ValueError("Invalid inv_gamma" )

						snake_case__				:   Union[str, Any]     =						state_dict.get("power"							,	self.power )
						if not isinstance(self.power							,	(float, int) ):
							raise ValueError("Invalid power" )

						snake_case__				:   List[str]     =						state_dict.get("shadow_params"							,	__A )
						if shadow_params is not None:
							snake_case__				:   Dict     =						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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



