


'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase__    	=     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')






@dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	Optional[str]          =     field(
			    default="cifar10"    ,       metadata={"help": "Name of a dataset from the datasets package"}	)
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "The column name of the images in the files."}	)
			lowerCAmelCase :	Optional[str]          =     field(default=UpperCAmelCase__    ,       metadata={"help": "A folder containing the training data."}	)
			lowerCAmelCase :	Optional[str]          =     field(default=UpperCAmelCase__    ,       metadata={"help": "A folder containing the validation data."}	)
			lowerCAmelCase :	Optional[float]          =     field(
			    default=0.15    ,       metadata={"help": "Percent to split off of train for validation."}	)
			lowerCAmelCase :	Optional[int]          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }    ,       )
			lowerCAmelCase :	Optional[int]          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }    ,       )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      {}
					if self.train_dir is not None:
							_UpperCAmelCase		:       str								=      self.train_dir
					if self.validation_dir is not None:
							_UpperCAmelCase		:       List[Any]								=      self.validation_dir
					_UpperCAmelCase		:       List[str]								=      data_files if data_files else None






@dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	str          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
			        )
			    }    ,       )
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}	)
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "Override some existing default config settings when a model is trained from scratch. Example: "
			            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
			        )
			    }    ,       )
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}	)
			lowerCAmelCase :	str          =     field(
			    default="main"    ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,       )
			lowerCAmelCase :	str          =     field(default=UpperCAmelCase__    ,       metadata={"help": "Name or path of preprocessor config."}	)
			lowerCAmelCase :	bool          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }    ,       )
			lowerCAmelCase :	float          =     field(
			    default=0.75    ,       metadata={"help": "The ratio of the number of masked tokens in the input sequence."}	)
			lowerCAmelCase :	bool          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Whether or not to train with normalized pixel values as target."}	)






@dataclass
class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	float          =     field(
			    default=1e-3    ,       metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}	)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      torch.stack([example["pixel_values"] for example in examples]   )
		return {"pixel_values": pixel_values}






def  __lowerCAmelCase ():
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_UpperCAmelCase		:       List[str]								=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
		if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
		else:
				_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_mae"      , __lowerCAmelCase      , __lowerCAmelCase   )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      , datefmt="%m/%d/%Y %H:%M:%S"      , handlers=[logging.StreamHandler(sys.stdout   )]      , )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_UpperCAmelCase		:       List[str]								=      training_args.get_process_log_level()
		logger.setLevel(__lowerCAmelCase   )
		transformers.utils.logging.set_verbosity(__lowerCAmelCase   )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"""   )
		logger.info(F"""Training/evaluation parameters {training_args}"""   )

		# Detecting last checkpoint.
		_UpperCAmelCase		:       Tuple								=      None
		if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
				_UpperCAmelCase		:       Any								=      get_last_checkpoint(training_args.output_dir   )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    "Use --overwrite_output_dir to overcome."   )
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Initialize our dataset.
		_UpperCAmelCase		:       Dict								=      load_dataset(
		    data_args.dataset_name      , data_args.dataset_config_name      , data_files=data_args.data_files      , cache_dir=model_args.cache_dir      , use_auth_token=True if model_args.use_auth_token else None      , )

		# If we don't have a validation split, split off a percentage of train as validation.
		_UpperCAmelCase		:       Any								=      None if "validation" in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split      , __lowerCAmelCase   ) and data_args.train_val_split > 0.0:
				_UpperCAmelCase		:       Optional[int]								=      ds["train"].train_test_split(data_args.train_val_split   )
				_UpperCAmelCase		:       List[Any]								=      split["train"]
				_UpperCAmelCase		:       Any								=      split["test"]

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_UpperCAmelCase		:       Union[str, Any]								=      {
		    "cache_dir": model_args.cache_dir,
		    "revision": model_args.model_revision,
		    "use_auth_token": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_UpperCAmelCase		:       List[Any]								=      ViTMAEConfig.from_pretrained(model_args.config_name      , **__lowerCAmelCase   )
		elif model_args.model_name_or_path:
				_UpperCAmelCase		:       str								=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path      , **__lowerCAmelCase   )
		else:
				_UpperCAmelCase		:       Any								=      ViTMAEConfig()
				logger.warning("You are instantiating a new config instance from scratch."   )
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""   )
						config.update_from_string(model_args.config_overrides   )
						logger.info(F"""New config: {config}"""   )

    # adapt config
		config.update(
		    {
		        "mask_ratio": model_args.mask_ratio,
		        "norm_pix_loss": model_args.norm_pix_loss,
		    }   )

		# create image processor
		if model_args.image_processor_name:
				_UpperCAmelCase		:       List[str]								=      ViTImageProcessor.from_pretrained(model_args.image_processor_name      , **__lowerCAmelCase   )
		elif model_args.model_name_or_path:
				_UpperCAmelCase		:       str								=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path      , **__lowerCAmelCase   )
		else:
				_UpperCAmelCase		:       int								=      ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_UpperCAmelCase		:       Optional[int]								=      ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path      , from_tf=bool(".ckpt" in model_args.model_name_or_path   )      , config=__lowerCAmelCase      , cache_dir=model_args.cache_dir      , revision=model_args.model_revision      , use_auth_token=True if model_args.use_auth_token else None      , )
		else:
				logger.info("Training new model from scratch"   )
				_UpperCAmelCase		:       List[Any]								=      ViTMAEForPreTraining(__lowerCAmelCase   )

		if training_args.do_train:
				_UpperCAmelCase		:       Any								=      ds["train"].column_names
		else:
				_UpperCAmelCase		:       List[str]								=      ds["validation"].column_names

		if data_args.image_column_name is not None:
				_UpperCAmelCase		:       Any								=      data_args.image_column_name
		elif "image" in column_names:
				_UpperCAmelCase		:       Tuple								=      "image"
		elif "img" in column_names:
				_UpperCAmelCase		:       Union[str, Any]								=      "img"
		else:
				_UpperCAmelCase		:       Any								=      column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_UpperCAmelCase		:       int								=      image_processor.size["shortest_edge"]
		else:
				_UpperCAmelCase		:       str								=      (image_processor.size["height"], image_processor.size["width"])
		_UpperCAmelCase		:       Tuple								=      Compose(
		    [
		        Lambda(lambda __lowerCAmelCase   : img.convert("RGB"   ) if img.mode != "RGB" else img   ),
		        RandomResizedCrop(__lowerCAmelCase      , scale=(0.2, 1.0)      , interpolation=InterpolationMode.BICUBIC   ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean      , std=image_processor.image_std   ),
		    ]   )

		def preprocess_images(__lowerCAmelCase   ):
				_UpperCAmelCase		:       Any								=      [transforms(__lowerCAmelCase   ) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError("--do_train requires a train dataset"   )
				if data_args.max_train_samples is not None:
						_UpperCAmelCase		:       Union[str, Any]								=      ds["train"].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
				# Set the training transforms
				ds["train"].set_transform(__lowerCAmelCase   )

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError("--do_eval requires a validation dataset"   )
				if data_args.max_eval_samples is not None:
						_UpperCAmelCase		:       Any								=      (
						    ds["validation"].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
						)
				# Set the validation transforms
				ds["validation"].set_transform(__lowerCAmelCase   )

		# Compute absolute learning rate
		_UpperCAmelCase		:       Dict								=      (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_UpperCAmelCase		:       Union[str, Any]								=      training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		_UpperCAmelCase		:       Any								=      Trainer(
		    model=__lowerCAmelCase      , args=__lowerCAmelCase      , train_dataset=ds["train"] if training_args.do_train else None      , eval_dataset=ds["validation"] if training_args.do_eval else None      , tokenizer=__lowerCAmelCase      , data_collator=__lowerCAmelCase      , )

		# Training
		if training_args.do_train:
				_UpperCAmelCase		:       Tuple								=      None
				if training_args.resume_from_checkpoint is not None:
						_UpperCAmelCase		:       int								=      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_UpperCAmelCase		:       List[str]								=      last_checkpoint
				_UpperCAmelCase		:       Optional[Any]								=      trainer.train(resume_from_checkpoint=__lowerCAmelCase   )
				trainer.save_model()
				trainer.log_metrics("train"      , train_result.metrics   )
				trainer.save_metrics("train"      , train_result.metrics   )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_UpperCAmelCase		:       Any								=      trainer.evaluate()
				trainer.log_metrics("eval"      , __lowerCAmelCase   )
				trainer.save_metrics("eval"      , __lowerCAmelCase   )

		# Write model card and (optionally) push to hub
		_UpperCAmelCase		:       Optional[int]								=      {
		    "tasks": "masked-auto-encoding",
		    "dataset": data_args.dataset_name,
		    "tags": ["masked-auto-encoding"],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**__lowerCAmelCase   )
		else:
				trainer.create_model_card(**__lowerCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()







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






class 	lowerCAmelCase__			:




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter]	,  lowerCamelCase__  :     float = 0.9_9_9_9	,  lowerCamelCase__  :     float = 0.0	,  lowerCamelCase__  :     int = 0	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     Union[float, int] = 1.0	,  lowerCamelCase__  :     Union[float, int] = 2 / 3	,  lowerCamelCase__  :     Optional[Any] = None	,  lowerCamelCase__  :     Dict[str, Any] = None	,  **lowerCamelCase__  :     Optional[int]	,  )   ->Optional[Any]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       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"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       List[str]								=      parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							_UpperCAmelCase		:       Optional[int]								=      True

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

					if kwargs.get("min_value"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Optional[int]								=      "The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      kwargs["min_value"]

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

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

					_UpperCAmelCase		:       Union[str, Any]								=      None

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

					_UpperCAmelCase		:       Optional[int]								=      model_cls
					_UpperCAmelCase		:       Union[str, Any]								=      model_config




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->"EMAModel":



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model_cls.load_config(lowerCamelCase__	,  return_unused_kwargs=lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      model_cls.from_pretrained(lowerCamelCase__ )

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

					ema_model.load_state_dict(lowerCamelCase__ )
					return ema_model




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int )   ->Dict:



					'''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__." )

					_UpperCAmelCase		:       int								=      self.model_cls.from_config(self.model_config )
					_UpperCAmelCase		:       Union[str, Any]								=      self.state_dict()
					state_dict.pop("shadow_params"	,  lowerCamelCase__ )

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




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     int )   ->float:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      max(0	,  optimization_step - self.update_after_step - 1 )

					if step <= 0:
							return 0.0

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

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




			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->Dict:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       Union[str, Any]								=      (
							    "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"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       Any								=      parameters.parameters()

					_UpperCAmelCase		:       Dict								=      list(lowerCamelCase__ )

					self.optimization_step += 1

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

					_UpperCAmelCase		:       Union[str, 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	,  lowerCamelCase__ ):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									_UpperCAmelCase		:       str								=      deepspeed.zero.GatheredParameters(lowerCamelCase__	,  modifier_rank=lowerCamelCase__ )

							with context_manager():
									if param.requires_grad:
											s_param.sub_(one_minus_decay * (s_param - param) )
									else:
											s_param.copy_(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      list(lowerCamelCase__ )
					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							param.data.copy_(s_param.to(param.device ).data )




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Dict=None	,  lowerCamelCase__  :     Optional[int]=None )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [
					    p.to(device=lowerCamelCase__	,  dtype=lowerCamelCase__ ) if p.is_floating_point() else p.to(device=lowerCamelCase__ )
					    for p in self.shadow_params
					]




			def 							lowerCAmelCase__    (		self  :     List[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]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      [param.detach().cpu().clone() for param in parameters]




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     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	,  lowerCamelCase__ ):
							param.data.copy_(c_param.data )

					# Better memory-wise.
					_UpperCAmelCase		:       int								=      None






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     dict )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      copy.deepcopy(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      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		:       Union[str, Any]								=      state_dict.get("min_decay"	,  self.min_decay )
					if not isinstance(self.min_decay	,  lowerCamelCase__ ):
							raise ValueError("Invalid min_decay" )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("optimization_step"	,  self.optimization_step )
					if not isinstance(self.optimization_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid optimization_step" )

					_UpperCAmelCase		:       List[Any]								=      state_dict.get("update_after_step"	,  self.update_after_step )
					if not isinstance(self.update_after_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid update_after_step" )

					_UpperCAmelCase		:       str								=      state_dict.get("use_ema_warmup"	,  self.use_ema_warmup )
					if not isinstance(self.use_ema_warmup	,  lowerCamelCase__ ):
							raise ValueError("Invalid use_ema_warmup" )

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

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

					_UpperCAmelCase		:       List[str]								=      state_dict.get("shadow_params"	,  lowerCamelCase__ )
					if shadow_params is not None:
							_UpperCAmelCase		:       Optional[Any]								=      shadow_params
							if not isinstance(self.shadow_params	,  lowerCamelCase__ ):
									raise ValueError("shadow_params must be a list" )
							if not all(isinstance(lowerCamelCase__	,  torch.Tensor ) for p in self.shadow_params ):
									raise ValueError("shadow_params must all be Tensors" )



