



'''simple docstring'''






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class  UpperCAmelCase__						:



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       Optional[int]=1_3,__A      :       Tuple=3_0,__A      :       Dict=2,__A      :       Dict=3,__A      :       Tuple=True,__A      :       str=True,__A      :       Tuple=3_2,__A      :       List[str]=5,__A      :       str=4,__A      :       Optional[int]=3_7,__A      :       str="gelu",__A      :       List[str]=0.1,__A      :       Dict=0.1,__A      :       Dict=1_0,__A      :       Optional[int]=0.02,__A      :       Union[str, Any]=3,__A      :       Any=0.6,__A      :       Dict=None,):
          _lowerCamelCase				:   Tuple								=   parent
          _lowerCamelCase				:   List[str]								=   batch_size
          _lowerCamelCase				:   Optional[Any]								=   image_size
          _lowerCamelCase				:   Optional[Any]								=   patch_size
          _lowerCamelCase				:   List[str]								=   num_channels
          _lowerCamelCase				:   Union[str, Any]								=   is_training
          _lowerCamelCase				:   int								=   use_labels
          _lowerCamelCase				:   Optional[int]								=   hidden_size
          _lowerCamelCase				:   Any								=   num_hidden_layers
          _lowerCamelCase				:   str								=   num_attention_heads
          _lowerCamelCase				:   str								=   intermediate_size
          _lowerCamelCase				:   Union[str, Any]								=   hidden_act
          _lowerCamelCase				:   Optional[int]								=   hidden_dropout_prob
          _lowerCamelCase				:   Tuple								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Any								=   type_sequence_label_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   Optional[int]								=   mask_ratio
          _lowerCamelCase				:   int								=   scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          _lowerCamelCase				:   str								=   (image_size // patch_size) ** 2
          _lowerCamelCase				:   Dict								=   int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_labels:
                _lowerCamelCase				:   Dict								=   ids_tensor([self.batch_size],self.type_sequence_label_size     )

          _lowerCamelCase				:   Any								=   self.get_config()

          return config, pixel_values, labels



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          return ViTMAEConfig(
              image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,is_decoder=UpperCAmelCase_,initializer_range=self.initializer_range,mask_ratio=self.mask_ratio,)



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       int,__A      :       str,__A      :       Optional[int]     ):
          _lowerCamelCase				:   Dict								=   ViTMAEModel(config=UpperCAmelCase_     )
          model.to(UpperCAmelCase_     )
          model.eval()
          _lowerCamelCase				:   Optional[int]								=   model(UpperCAmelCase_     )
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Tuple     ):
          _lowerCamelCase				:   List[Any]								=   ViTMAEForPreTraining(UpperCAmelCase_     )
          model.to(UpperCAmelCase_     )
          model.eval()
          _lowerCamelCase				:   Dict								=   model(UpperCAmelCase_     )
          _lowerCamelCase				:   List[str]								=   (self.image_size // self.patch_size) ** 2
          _lowerCamelCase				:   Optional[int]								=   self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape,(self.batch_size, num_patches, expected_num_channels)     )

          # test greyscale images
          _lowerCamelCase				:   List[Any]								=   1
          _lowerCamelCase				:   Union[str, Any]								=   ViTMAEForPreTraining(UpperCAmelCase_     )
          model.to(UpperCAmelCase_     )
          model.eval()
          _lowerCamelCase				:   List[str]								=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
          _lowerCamelCase				:   Union[str, Any]								=   model(UpperCAmelCase_     )
          _lowerCamelCase				:   Tuple								=   self.patch_size**2
          self.parent.assertEqual(result.logits.shape,(self.batch_size, num_patches, expected_num_channels)     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase				:   List[str]								=   config_and_inputs
          _lowerCamelCase				:   Any								=   {'pixel_values': pixel_values}
          return config, inputs_dict


@require_torch
class  UpperCAmelCase__						(       __lowerCamelCase						,	__lowerCamelCase						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowerCAmelCase_				    =		{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   List[str]								=   ViTMAEModelTester(self     )
          _lowerCamelCase				:   Any								=   ConfigTester(self,config_class=UpperCAmelCase_,has_text_modality=UpperCAmelCase_,hidden_size=3_7     )



    def   lowerCamelCase_  (	self      :       Any     ):
          self.config_tester.run_common_tests()



    @unittest.skip(reason="ViTMAE does not use inputs_embeds"     )
    def   lowerCamelCase_  (	self      :       Tuple     ):
          pass



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   List[str]								=   model_class(UpperCAmelCase_     )
                self.assertIsInstance(model.get_input_embeddings(),(nn.Module)     )
                _lowerCamelCase				:   Optional[Any]								=   model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(UpperCAmelCase_,nn.Linear     )     )



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Tuple								=   model_class(UpperCAmelCase_     )
                _lowerCamelCase				:   int								=   inspect.signature(model.forward     )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _lowerCamelCase				:   Any								=   [*signature.parameters.keys()]

                _lowerCamelCase				:   Optional[int]								=   ['pixel_values']
                self.assertListEqual(arg_names[:1],UpperCAmelCase_     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_     )



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   List[Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       str,__A      :       Any,__A      :       Union[str, Any]     ):
          # make masks reproducible
          np.random.seed(2     )

          _lowerCamelCase				:   str								=   int((pt_model.config.image_size // pt_model.config.patch_size) ** 2     )
          _lowerCamelCase				:   Tuple								=   np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
          _lowerCamelCase				:   Optional[Any]								=   torch.from_numpy(UpperCAmelCase_     )

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          _lowerCamelCase				:   List[str]								=   pt_noise

          super().check_pt_tf_models(UpperCAmelCase_,UpperCAmelCase_,UpperCAmelCase_     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Tuple								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Optional[Any]								=   model_class(UpperCAmelCase_     )
                model.to(UpperCAmelCase_     )
                model.eval()
                # make random mask reproducible
                torch.manual_seed(2     )
                with torch.no_grad():
                      _lowerCamelCase				:   Tuple								=   model(**self._prepare_for_class(UpperCAmelCase_,UpperCAmelCase_     )     )

                _lowerCamelCase				:   Dict								=   outputs[0].cpu().numpy()
                _lowerCamelCase				:   Optional[int]								=   0

                with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(UpperCAmelCase_     )
                      _lowerCamelCase				:   str								=   model_class.from_pretrained(UpperCAmelCase_     )
                      model.to(UpperCAmelCase_     )
                      # make random mask reproducible
                      torch.manual_seed(2     )
                      with torch.no_grad():
                            _lowerCamelCase				:   List[str]								=   model(**self._prepare_for_class(UpperCAmelCase_,UpperCAmelCase_     )     )

                      # Make sure we don't have nans
                      _lowerCamelCase				:   Tuple								=   after_outputs[0].cpu().numpy()
                      _lowerCamelCase				:   Any								=   0
                      _lowerCamelCase				:   Union[str, Any]								=   np.amax(np.abs(out_a - out_a     )     )
                      self.assertLessEqual(UpperCAmelCase_,1e-5     )



    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."     )
    def   lowerCamelCase_  (	self      :       Tuple     ):
          pass



    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."     )
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          pass



    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."     )
    def   lowerCamelCase_  (	self      :       Tuple     ):
          pass



    @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"     )
    def   lowerCamelCase_  (	self      :       Tuple     ):
          pass



    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          pass



    @slow
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase				:   Tuple								=   ViTMAEModel.from_pretrained(UpperCAmelCase_     )
                self.assertIsNotNone(UpperCAmelCase_     )







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   int								=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
      return image



@require_torch
@require_vision
class  UpperCAmelCase__						(       unittest.TestCase ):



    @cached_property
    def   lowerCamelCase_  (	self      :       int     ):
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"     ) if is_vision_available() else None



    @slow
    def   lowerCamelCase_  (	self      :       str     ):
          # make random mask reproducible across the PT and TF model
          np.random.seed(2     )

          _lowerCamelCase				:   Union[str, Any]								=   ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"     ).to(UpperCAmelCase_     )

          _lowerCamelCase				:   Tuple								=   self.default_image_processor
          _lowerCamelCase				:   Dict								=   prepare_img()
          _lowerCamelCase				:   Optional[int]								=   image_processor(images=UpperCAmelCase_,return_tensors="pt"     ).to(UpperCAmelCase_     )

          # prepare a noise vector that will be also used for testing the TF model
          # (this way we can ensure that the PT and TF models operate on the same inputs)
          _lowerCamelCase				:   Union[str, Any]								=   ViTMAEConfig()
          _lowerCamelCase				:   int								=   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
          _lowerCamelCase				:   Any								=   np.random.uniform(size=(1, num_patches)     )

          # forward pass
          with torch.no_grad():
                _lowerCamelCase				:   Dict								=   model(**UpperCAmelCase_,noise=torch.from_numpy(UpperCAmelCase_     ).to(device=UpperCAmelCase_     )     )

          # verify the logits
          _lowerCamelCase				:   Tuple								=   torch.Size((1, 1_9_6, 7_6_8)     )
          self.assertEqual(outputs.logits.shape,UpperCAmelCase_     )

          _lowerCamelCase				:   Any								=   torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]     )

          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],expected_slice.to(UpperCAmelCase_     ),atol=1e-4     )     )

'''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 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      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  UpperCAmelCase__						:



    def __init__(	self      :       List[str],__A      :       Iterable[torch.nn.Parameter],__A      :       float = 0.9999,__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      :       Optional[Any],):

          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   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",__A,standard_warn=__A,)
                _lowerCamelCase				:   Dict								=   parameters.parameters()

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

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

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

          _lowerCamelCase				:   Optional[int]								=   list(__A     )
          _lowerCamelCase				:   List[Any]								=   [p.clone().detach() for p in parameters]

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

          _lowerCamelCase				:   Tuple								=   None

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

          _lowerCamelCase				:   List[str]								=   model_cls
          _lowerCamelCase				:   Dict								=   model_config



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   model_cls.load_config(__A,return_unused_kwargs=__A     )
          _lowerCamelCase				:   Optional[Any]								=   model_cls.from_pretrained(__A     )

          _lowerCamelCase				:   Union[str, Any]								=   cls(model.parameters(),model_cls=__A,model_config=model.config     )

          ema_model.load_state_dict(__A     )
          return ema_model



    def   lowerCamelCase_  (	self      :       str,__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__."     )

          _lowerCamelCase				:   Tuple								=   self.model_cls.from_config(self.model_config     )
          _lowerCamelCase				:   List[str]								=   self.state_dict()
          state_dict.pop("shadow_params",__A     )

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



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          _lowerCamelCase				:   str								=   max(0,optimization_step - self.update_after_step - 1     )

          if step <= 0:
                return 0.0

          if self.use_ema_warmup:
                _lowerCamelCase				:   Tuple								=   1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                _lowerCamelCase				:   List[str]								=   (1 + step) / (1_0 + step)

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



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

          _lowerCamelCase				:   str								=   list(__A     )

          self.optimization_step += 1

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

          _lowerCamelCase				:   List[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,__A     ):
                if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      _lowerCamelCase				:   List[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   lowerCamelCase_  (	self      :       Dict,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   list(__A     )
          for s_param, param in zip(self.shadow_params,__A     ):
                param.data.copy_(s_param.to(param.device     ).data     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Dict=None,__A      :       Any=None     ):
          _lowerCamelCase				:   int								=   [
              p.to(device=__A,dtype=__A     ) if p.is_floating_point() else p.to(device=__A     )
              for p in self.shadow_params
          ]



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          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      :       Tuple,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   [param.detach().cpu().clone() for param in parameters]



    def   lowerCamelCase_  (	self      :       int,__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.
          _lowerCamelCase				:   List[str]								=   None



    def   lowerCamelCase_  (	self      :       Dict,__A      :       dict     ):
          _lowerCamelCase				:   List[str]								=   copy.deepcopy(__A     )

          _lowerCamelCase				:   Optional[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"     )

          _lowerCamelCase				:   Dict								=   state_dict.get("min_decay",self.min_decay     )
          if not isinstance(self.min_decay,__A     ):
                raise ValueError("Invalid min_decay"     )

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

          _lowerCamelCase				:   Any								=   state_dict.get("update_after_step",self.update_after_step     )
          if not isinstance(self.update_after_step,__A     ):
                raise ValueError("Invalid update_after_step"     )

          _lowerCamelCase				:   List[Any]								=   state_dict.get("use_ema_warmup",self.use_ema_warmup     )
          if not isinstance(self.use_ema_warmup,__A     ):
                raise ValueError("Invalid use_ema_warmup"     )

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

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

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("shadow_params",__A     )
          if shadow_params is not None:
                _lowerCamelCase				:   str								=   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"     )