
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Union[str, 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 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			Iterable[torch.nn.Parameter]  ,	UpperCamelCase__				:			float = 0.9_999  ,	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__				:			int  ,	)						->			List[Any]:






				"""simple docstring"""







				if isinstance(UpperCamelCase__  ,	torch.nn.Module      ):
						snake_case  :       Optional[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'''  ,	UpperCamelCase__  ,	standard_warn=UpperCamelCase__  ,	)
						snake_case  :       Optional[Any]     			=						parameters.parameters()

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

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

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

				snake_case  :       Dict     			=						list(UpperCamelCase__      )
				snake_case  :       Optional[Any]     			=						[p.clone().detach() for p in parameters]

				if kwargs.get('''device'''  ,	UpperCamelCase__      ) is not None:
						snake_case  :       Union[str, Any]     			=						'''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  :       Dict     			=						None

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

				snake_case  :       Tuple     			=						model_cls
				snake_case  :       Tuple     			=						model_config



		@classmethod
		def       lowerCAmelCase				(      cls				:			Optional[Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			"EMAModel":






				"""simple docstring"""







				snake_case			,snake_case  :       Union[str, Any]     			=						model_cls.load_config(UpperCamelCase__  ,	return_unused_kwargs=UpperCamelCase__      )
				snake_case  :       Optional[Any]     			=						model_cls.from_pretrained(UpperCamelCase__      )

				snake_case  :       Dict     			=						cls(model.parameters()  ,	model_cls=UpperCamelCase__  ,	model_config=model.config      )

				ema_model.load_state_dict(UpperCamelCase__      )
				return ema_model



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			str      )						->			List[Any]:






				"""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__.'''      )

				snake_case  :       Union[str, Any]     			=						self.model_cls.from_config(self.model_config      )
				snake_case  :       Optional[int]     			=						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				:			Dict  ,	UpperCamelCase__				:			int      )						->			float:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						max(0  ,	optimization_step - self.update_after_step - 1      )

				if step <= 0:
						return 0.0

				if self.use_ema_warmup:
						snake_case  :       List[str]     			=						1 - (1 + step / self.inv_gamma) ** -self.power
				else:
						snake_case  :       Optional[int]     			=						(1 + step) / (10 + step)

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



		@torch.no_grad()
		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Iterable[torch.nn.Parameter]      )						->			Dict:






				"""simple docstring"""







				if isinstance(UpperCamelCase__  ,	torch.nn.Module      ):
						snake_case  :       Dict     			=						(
						    '''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  :       Tuple     			=						parameters.parameters()

				snake_case  :       Tuple     			=						list(UpperCamelCase__      )

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				snake_case  :       Union[str, Any]     			=						self.get_decay(self.optimization_step      )
				snake_case  :       str     			=						decay
				snake_case  :       Tuple     			=						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  ,	UpperCamelCase__      ):
						if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
								snake_case  :       Optional[Any]     			=						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				:			List[str]  ,	UpperCamelCase__				:			Iterable[torch.nn.Parameter]      )						->			None:






				"""simple docstring"""







				snake_case  :       Tuple     			=						list(UpperCamelCase__      )
				for s_param, param in zip(self.shadow_params  ,	UpperCamelCase__      ):
						param.data.copy_(s_param.to(param.device      ).data      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			List[Any]=None      )						->			None:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						[
				    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				:			Any      )						->			dict:






				"""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       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Iterable[torch.nn.Parameter]      )						->			None:






				"""simple docstring"""







				snake_case  :       int     			=						[param.detach().cpu().clone() for param in parameters]



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Iterable[torch.nn.Parameter]      )						->			None:






				"""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  ,	UpperCamelCase__      ):
						param.data.copy_(c_param.data      )

				# Better memory-wise.
				snake_case  :       Dict     			=						None



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			dict      )						->			None:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						copy.deepcopy(UpperCamelCase__      )

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

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

				snake_case  :       int     			=						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  :       Optional[int]     			=						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  :       Optional[int]     			=						state_dict.get('''inv_gamma'''  ,	self.inv_gamma      )
				if not isinstance(self.inv_gamma  ,	(float, int)      ):
						raise ValueError('''Invalid inv_gamma'''      )

				snake_case  :       Optional[int]     			=						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'''  ,	UpperCamelCase__      )
				if shadow_params is not None:
						snake_case  :       Dict     			=						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'''      )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__						       =      {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["EncoderDecoderModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["TFEncoderDecoderModel"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
						from .configuration_encoder_decoder import EncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encoder_decoder import EncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


