



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			1
												UpperCAmelCase					=			3
												UpperCAmelCase					=			(32, 32)

												UpperCAmelCase					=			floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(lowercase_ )
												return image






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												torch.manual_seed(0 )
												UpperCAmelCase					=			UNetaDConditionModel(
												    block_out_channels=(32, 32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=7       ,  out_channels=4       ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')       ,  up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')       ,  cross_attention_dim=32       ,  attention_head_dim=8       ,  use_linear_projection=lowercase_       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
												torch.manual_seed(0 )
												UpperCAmelCase					=			AutoencoderKL(
												    block_out_channels=[32, 32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			CLIPTextConfig(
												    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=10_00       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
												return CLIPTextModel(lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												UpperCAmelCase					=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			sd_pipe(
												    2 * [prompt]       ,  image=2 * [low_res_image]       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2






						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU' )
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# put models in fp16, except vae as it overflows in fp16
												UpperCAmelCase					=			unet.half()
												UpperCAmelCase					=			text_encoder.half()

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(lowercase_ )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 1E-3






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat_fp16.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 5E-1






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  output_type='np'       ,  )

												UpperCAmelCase					=			torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 10**9










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







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:Iterable[torch.nn.Parameter]       ,  lowercase_			:float = 0.9999       ,  lowercase_			:float = 0.0       ,  lowercase_			:int = 0       ,  lowercase_			:bool = False       ,  lowercase_			:Union[float, int] = 1.0       ,  lowercase_			:Union[float, int] = 2 / 3       ,  lowercase_			:Optional[Any] = None       ,  lowercase_			:Dict[str, Any] = None       ,  **lowercase_			:Dict       ,  )    ->      Optional[int]:

												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    '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'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

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

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

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

												UpperCAmelCase					=			list(lowercase_ )
												UpperCAmelCase					=			[p.clone().detach() for p in parameters]

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

												UpperCAmelCase					=			None

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

												UpperCAmelCase					=			model_cls
												UpperCAmelCase					=			model_config






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Any )    ->      "EMAModel":
												UpperCAmelCase				,   UpperCAmelCase					=			model_cls.load_config(lowercase_       ,  return_unused_kwargs=lowercase_ )
												UpperCAmelCase					=			model_cls.from_pretrained(lowercase_ )

												UpperCAmelCase					=			cls(model.parameters()       ,  model_cls=lowercase_       ,  model_config=model.config )

												ema_model.load_state_dict(lowercase_ )
												return ema_model






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[str] )    ->      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__.' )

												UpperCAmelCase					=			self.model_cls.from_config(self.model_config )
												UpperCAmelCase					=			self.state_dict()
												state_dict.pop('shadow_params'       ,  lowercase_ )

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






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:int )    ->      float:
												UpperCAmelCase					=			max(0       ,  optimization_step - self.update_after_step - 1 )

												if step <= 0:
																		return 0.0

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

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






						@torch.no_grad()
						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      Optional[int]:
												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    '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'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

												UpperCAmelCase					=			list(lowercase_ )

												self.optimization_step += 1

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

												UpperCAmelCase					=			contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																		import deepspeed

												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																								UpperCAmelCase					=			deepspeed.zero.GatheredParameters(lowercase_       ,  modifier_rank=lowercase_ )

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






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			list(lowercase_ )
												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		param.data.copy_(s_param.to(param.device ).data )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple=None       ,  lowercase_			:Union[str, Any]=None )    ->      None:
												UpperCAmelCase					=			[
												    p.to(device=lowercase_       ,  dtype=lowercase_ ) if p.is_floating_point() else p.to(device=lowercase_ )
												    for p in self.shadow_params
												]






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      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      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			[param.detach().cpu().clone() for param in parameters]






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      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       ,  lowercase_ ):
																		param.data.copy_(c_param.data )

												# Better memory-wise.
												UpperCAmelCase					=			None






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:dict )    ->      None:
												UpperCAmelCase					=			copy.deepcopy(lowercase_ )

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

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

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

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

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

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

												UpperCAmelCase					=			state_dict.get('shadow_params'       ,  lowercase_ )
												if shadow_params is not None:
																		UpperCAmelCase					=			shadow_params
																		if not isinstance(self.shadow_params       ,  lowercase_ ):
																								raise ValueError('shadow_params must be a list' )
																		if not all(isinstance(lowercase_       ,  torch.Tensor ) for p in self.shadow_params ):
																								raise ValueError('shadow_params must all be Tensors' )





