



'''simple docstring'''






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class  UpperCAmelCase__						(       A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		StableDiffusionDiffEditPipeline
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
    lowerCAmelCase_				    =		frozenset(
        [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCAmelCase_				    =		frozenset([] )



    def   lowerCamelCase_  (	self      :       List[str]     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Dict								=   UNetaDConditionModel(
              block_out_channels=(3_2, 6_4),layers_per_block=2,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=__lowerCAmelCase,)
          _lowerCamelCase				:   Tuple								=   DDIMScheduler(
              beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear",clip_sample=__lowerCAmelCase,set_alpha_to_one=__lowerCAmelCase,)
          _lowerCamelCase				:   Any								=   DDIMInverseScheduler(
              beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear",clip_sample=__lowerCAmelCase,set_alpha_to_zero=__lowerCAmelCase,)
          torch.manual_seed(0     )
          _lowerCamelCase				:   int								=   AutoencoderKL(
              block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],latent_channels=4,sample_size=1_2_8,)
          torch.manual_seed(0     )
          _lowerCamelCase				:   Dict								=   CLIPTextConfig(
              bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act="gelu",projection_dim=5_1_2,)
          _lowerCamelCase				:   Any								=   CLIPTextModel(__lowerCAmelCase     )
          _lowerCamelCase				:   List[Any]								=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

          _lowerCamelCase				:   Tuple								=   {
              "unet": unet,
              "scheduler": scheduler,
              "inverse_scheduler": inverse_scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }

          return components



    def   lowerCamelCase_  (	self      :       str,__A      :       Tuple,__A      :       List[str]=0     ):
          _lowerCamelCase				:   Optional[int]								=   floats_tensor((1, 1_6, 1_6),rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
          _lowerCamelCase				:   str								=   floats_tensor((1, 2, 4, 1_6, 1_6),rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
          if str(__lowerCAmelCase     ).startswith("mps"     ):
                _lowerCamelCase				:   Union[str, Any]								=   torch.manual_seed(__lowerCAmelCase     )
          else:
                _lowerCamelCase				:   int								=   torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
          _lowerCamelCase				:   List[str]								=   {
              "prompt": "a dog and a newt",
              "mask_image": mask,
              "image_latents": latents,
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



    def   lowerCamelCase_  (	self      :       str,__A      :       Optional[int],__A      :       Optional[int]=0     ):
          _lowerCamelCase				:   Tuple								=   floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
          _lowerCamelCase				:   Dict								=   image.cpu().permute(0,2,3,1     )[0]
          _lowerCamelCase				:   str								=   Image.fromarray(np.uinta(__lowerCAmelCase     )     ).convert("RGB"     )
          if str(__lowerCAmelCase     ).startswith("mps"     ):
                _lowerCamelCase				:   Tuple								=   torch.manual_seed(__lowerCAmelCase     )
          else:
                _lowerCamelCase				:   Tuple								=   torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
          _lowerCamelCase				:   Tuple								=   {
              "image": image,
              "source_prompt": "a cat and a frog",
              "target_prompt": "a dog and a newt",
              "generator": generator,
              "num_inference_steps": 2,
              "num_maps_per_mask": 2,
              "mask_encode_strength": 1.0,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }

          return inputs



    def   lowerCamelCase_  (	self      :       int,__A      :       Optional[int],__A      :       Dict=0     ):
          _lowerCamelCase				:   Optional[int]								=   floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__lowerCAmelCase     )     ).to(__lowerCAmelCase     )
          _lowerCamelCase				:   Union[str, Any]								=   image.cpu().permute(0,2,3,1     )[0]
          _lowerCamelCase				:   List[str]								=   Image.fromarray(np.uinta(__lowerCAmelCase     )     ).convert("RGB"     )
          if str(__lowerCAmelCase     ).startswith("mps"     ):
                _lowerCamelCase				:   Union[str, Any]								=   torch.manual_seed(__lowerCAmelCase     )
          else:
                _lowerCamelCase				:   Tuple								=   torch.Generator(device=__lowerCAmelCase     ).manual_seed(__lowerCAmelCase     )
          _lowerCamelCase				:   List[Any]								=   {
              "image": image,
              "prompt": "a cat and a frog",
              "generator": generator,
              "num_inference_steps": 2,
              "inpaint_strength": 1.0,
              "guidance_scale": 6.0,
              "decode_latents": True,
              "output_type": "numpy",
          }
          return inputs



    def   lowerCamelCase_  (	self      :       List[str]     ):







          if not hasattr(self.pipeline_class,"_optional_components"     ):
                return

          _lowerCamelCase				:   List[str]								=   self.get_dummy_components()
          _lowerCamelCase				:   str								=   self.pipeline_class(**__lowerCAmelCase     )
          pipe.to(__lowerCAmelCase     )
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          # set all optional components to None and update pipeline config accordingly
          for optional_component in pipe._optional_components:
                setattr(__lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase     )
          pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}     )

          _lowerCamelCase				:   Any								=   self.get_dummy_inputs(__lowerCAmelCase     )
          _lowerCamelCase				:   Union[str, Any]								=   pipe(**__lowerCAmelCase     )[0]

          with tempfile.TemporaryDirectory() as tmpdir:
                pipe.save_pretrained(__lowerCAmelCase     )
                _lowerCamelCase				:   Union[str, Any]								=   self.pipeline_class.from_pretrained(__lowerCAmelCase     )
                pipe_loaded.to(__lowerCAmelCase     )
                pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase     )

          for optional_component in pipe._optional_components:
                self.assertTrue(
                    getattr(__lowerCAmelCase,__lowerCAmelCase     ) is None,f'`{optional_component}` did not stay set to None after loading.',)

          _lowerCamelCase				:   List[Any]								=   self.get_dummy_inputs(__lowerCAmelCase     )
          _lowerCamelCase				:   List[Any]								=   pipe_loaded(**__lowerCAmelCase     )[0]

          _lowerCamelCase				:   Optional[Any]								=   np.abs(output - output_loaded     ).max()
          self.assertLess(__lowerCAmelCase,1e-4     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Dict								=   "cpu"

          _lowerCamelCase				:   List[Any]								=   self.get_dummy_components()
          _lowerCamelCase				:   Dict								=   self.pipeline_class(**__lowerCAmelCase     )
          pipe.to(__lowerCAmelCase     )
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          _lowerCamelCase				:   Optional[Any]								=   self.get_dummy_mask_inputs(__lowerCAmelCase     )
          _lowerCamelCase				:   Tuple								=   pipe.generate_mask(**__lowerCAmelCase     )
          _lowerCamelCase				:   Union[str, Any]								=   mask[0, -3:, -3:]

          self.assertEqual(mask.shape,(1, 1_6, 1_6)     )
          _lowerCamelCase				:   Optional[Any]								=   np.array([0] * 9     )
          _lowerCamelCase				:   List[Any]								=   np.abs(mask_slice.flatten() - expected_slice     ).max()
          self.assertLessEqual(__lowerCAmelCase,1e-3     )
          self.assertEqual(mask[0, -3, -4],0     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   "cpu"

          _lowerCamelCase				:   Dict								=   self.get_dummy_components()
          _lowerCamelCase				:   Union[str, Any]								=   self.pipeline_class(**__lowerCAmelCase     )
          pipe.to(__lowerCAmelCase     )
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          _lowerCamelCase				:   Dict								=   self.get_dummy_inversion_inputs(__lowerCAmelCase     )
          _lowerCamelCase				:   Tuple								=   pipe.invert(**__lowerCAmelCase     ).images
          _lowerCamelCase				:   str								=   image[0, -1, -3:, -3:]

          self.assertEqual(image.shape,(2, 3_2, 3_2, 3)     )
          _lowerCamelCase				:   Optional[Any]								=   np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],)
          _lowerCamelCase				:   List[Any]								=   np.abs(image_slice.flatten() - expected_slice     ).max()
          self.assertLessEqual(__lowerCAmelCase,1e-3     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          super().test_inference_batch_single_identical(expected_max_diff=5e-3     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   "cpu"

          _lowerCamelCase				:   Tuple								=   self.get_dummy_components()

          _lowerCamelCase				:   str								=   {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
          _lowerCamelCase				:   List[str]								=   DPMSolverMultistepScheduler(**__lowerCAmelCase     )
          _lowerCamelCase				:   str								=   DPMSolverMultistepInverseScheduler(**__lowerCAmelCase     )

          _lowerCamelCase				:   List[str]								=   self.pipeline_class(**__lowerCAmelCase     )
          pipe.to(__lowerCAmelCase     )
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          _lowerCamelCase				:   str								=   self.get_dummy_inversion_inputs(__lowerCAmelCase     )
          _lowerCamelCase				:   List[Any]								=   pipe.invert(**__lowerCAmelCase     ).images
          _lowerCamelCase				:   Optional[int]								=   image[0, -1, -3:, -3:]

          self.assertEqual(image.shape,(2, 3_2, 3_2, 3)     )
          _lowerCamelCase				:   Dict								=   np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],)
          _lowerCamelCase				:   List[Any]								=   np.abs(image_slice.flatten() - expected_slice     ).max()
          self.assertLessEqual(__lowerCAmelCase,1e-3     )


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



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



    @classmethod
    def   lowerCamelCase_  (	cls      :       Optional[Any]     ):
          _lowerCamelCase				:   Tuple								=   load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"     )

          _lowerCamelCase				:   Dict								=   raw_image.convert("RGB"     ).resize((7_6_8, 7_6_8)     )

          _lowerCamelCase				:   Optional[Any]								=   raw_image



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   torch.manual_seed(0     )

          _lowerCamelCase				:   Any								=   StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1",safety_checker=__lowerCAmelCase,torch_dtype=torch.floataa     )
          _lowerCamelCase				:   Union[str, Any]								=   DDIMScheduler.from_config(pipe.scheduler.config     )
          _lowerCamelCase				:   Optional[int]								=   DDIMInverseScheduler.from_config(pipe.scheduler.config     )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          _lowerCamelCase				:   List[str]								=   "a bowl of fruit"
          _lowerCamelCase				:   Optional[Any]								=   "a bowl of pears"

          _lowerCamelCase				:   List[str]								=   pipe.generate_mask(
              image=self.raw_image,source_prompt=__lowerCAmelCase,target_prompt=__lowerCAmelCase,generator=__lowerCAmelCase,)

          _lowerCamelCase				:   List[str]								=   pipe.invert(
              prompt=__lowerCAmelCase,image=self.raw_image,inpaint_strength=0.7,generator=__lowerCAmelCase     ).latents

          _lowerCamelCase				:   Optional[Any]								=   pipe(
              prompt=__lowerCAmelCase,mask_image=__lowerCAmelCase,image_latents=__lowerCAmelCase,generator=__lowerCAmelCase,negative_prompt=__lowerCAmelCase,inpaint_strength=0.7,output_type="numpy",).images[0]

          _lowerCamelCase				:   Optional[Any]								=   (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"     ).resize((7_6_8, 7_6_8)     )     )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()     ) < 5e-1



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[int]								=   torch.manual_seed(0     )

          _lowerCamelCase				:   int								=   StableDiffusionDiffEditPipeline.from_pretrained(
              "stabilityai/stable-diffusion-2-1",safety_checker=__lowerCAmelCase,torch_dtype=torch.floataa     )
          _lowerCamelCase				:   str								=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
          _lowerCamelCase				:   Any								=   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=__lowerCAmelCase     )

          _lowerCamelCase				:   List[str]								=   "a bowl of fruit"
          _lowerCamelCase				:   str								=   "a bowl of pears"

          _lowerCamelCase				:   Tuple								=   pipe.generate_mask(
              image=self.raw_image,source_prompt=__lowerCAmelCase,target_prompt=__lowerCAmelCase,generator=__lowerCAmelCase,)

          _lowerCamelCase				:   List[Any]								=   pipe.invert(
              prompt=__lowerCAmelCase,image=self.raw_image,inpaint_strength=0.7,generator=__lowerCAmelCase,num_inference_steps=2_5,).latents

          _lowerCamelCase				:   Optional[int]								=   pipe(
              prompt=__lowerCAmelCase,mask_image=__lowerCAmelCase,image_latents=__lowerCAmelCase,generator=__lowerCAmelCase,negative_prompt=__lowerCAmelCase,inpaint_strength=0.7,num_inference_steps=2_5,output_type="numpy",).images[0]

          _lowerCamelCase				:   Optional[int]								=   (
              np.array(
                  load_image(
                      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                      "/diffedit/pears.png"     ).resize((7_6_8, 7_6_8)     )     )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()     ) < 5e-1

'''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"     )