

"""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,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=StableDiffusionInstructPixaPixPipeline
  lowerCamelCase   :      Optional[int]										=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
  lowerCamelCase   :      str										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCamelCase   :      Tuple										=IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowerCamelCase   :      int										=IMAGE_TO_IMAGE_IMAGE_PARAMS







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     torch.manual_seed(0   )
     __lowerCAmelCase  :      Union[str, Any]    =						UNetaDConditionModel(
         block_out_channels=(32, 64)	,							layers_per_block=2	,							sample_size=32	,							in_channels=8	,							out_channels=4	,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,							cross_attention_dim=32	,							)
     __lowerCAmelCase  :      Any    =						PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
     torch.manual_seed(0   )
     __lowerCAmelCase  :      Optional[int]    =						AutoencoderKL(
         block_out_channels=[32, 64]	,							in_channels=3	,							out_channels=3	,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,							latent_channels=4	,							)
     torch.manual_seed(0   )
     __lowerCAmelCase  :      List[Any]    =						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	,							)
     __lowerCAmelCase  :      str    =						CLIPTextModel(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

     __lowerCAmelCase  :      Optional[Any]    =						{
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   List[Any]=0   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						floats_tensor((1, 3, 32, 32)	,							rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						image.cpu().permute(0	,							2	,							3	,							1   )[0]
     __lowerCAmelCase  :      Optional[Any]    =						Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert("""RGB"""   )
     if str(lowerCAmelCase   ).startswith("""mps"""   ):
        __lowerCAmelCase  :      Tuple    =						torch.manual_seed(lowerCAmelCase   )
     else:
        __lowerCAmelCase  :      int    =						torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						{
         """prompt""": """A painting of a squirrel eating a burger""",
         """image""": image,
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """image_guidance_scale""": 1,
         """output_type""": """numpy""",
     }
     return inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      List[Any]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Union[str, Any]    =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						sd_pipe.to(lowerCAmelCase   )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						sd_pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      Tuple    =						image[0, -3:, -3:, -1]
     assert image.shape == (1, 32, 32, 3)
     __lowerCAmelCase  :      int    =						np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      Optional[int]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Dict    =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						sd_pipe.to(lowerCAmelCase   )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						"""french fries"""
     __lowerCAmelCase  :      Any    =						sd_pipe(**lowerCAmelCase	,							negative_prompt=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						output.images
     __lowerCAmelCase  :      List[Any]    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __lowerCAmelCase  :      Tuple    =						np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      Union[str, Any]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Optional[int]    =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						sd_pipe.to(lowerCAmelCase   )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[inputs["""prompt"""]] * 2

     __lowerCAmelCase  :      Union[str, Any]    =						np.array(inputs["""image"""]   ).astype(np.floataa   ) / 255.0
     __lowerCAmelCase  :      int    =						torch.from_numpy(lowerCAmelCase   ).unsqueeze(0   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						image / 2 + 0.5
     __lowerCAmelCase  :      Dict    =						image.permute(0	,							3	,							1	,							2   )
     __lowerCAmelCase  :      Union[str, Any]    =						image.repeat(2	,							1	,							1	,							1   )

     __lowerCAmelCase  :      Optional[int]    =						sd_pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      Dict    =						image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     __lowerCAmelCase  :      int    =						np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      List[str]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Dict    =						EulerAncestralDiscreteScheduler(
         beta_start=0.0_0085	,							beta_end=0.012	,							beta_schedule="""scaled_linear"""   )
     __lowerCAmelCase  :      Any    =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						sd_pipe.to(lowerCAmelCase   )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						sd_pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      List[str]    =						image[0, -3:, -3:, -1]

     __lowerCAmelCase  :      Any    =						[round(lowerCAmelCase	,							4   ) for x in image_slice.flatten().tolist()]
     print(""",""".join([str(lowerCAmelCase   ) for x in slice]   )   )

     assert image.shape == (1, 32, 32, 3)
     __lowerCAmelCase  :      List[str]    =						np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     super().test_inference_batch_single_identical(expected_max_diff=3e-3   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Optional[int]    =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						VaeImageProcessor(do_resize=lowerCAmelCase	,							do_normalize=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase	,							input_image_type="""pt"""   )   )[0]

     __lowerCAmelCase  :      Optional[int]    =						components["""vae"""]
     __lowerCAmelCase  :      Dict    =						self.get_dummy_inputs_by_type(lowerCAmelCase	,							input_image_type="""pt"""   )

     for image_param in self.image_latents_params:
        if image_param in inputs.keys():
           __lowerCAmelCase  :      Any    =						vae.encode(inputs[image_param]   ).latent_dist.mode()

     __lowerCAmelCase  :      List[str]    =						pipe(**lowerCAmelCase   )[0]

     __lowerCAmelCase  :      Union[str, Any]    =						np.abs(out - out_latents_inputs   ).max()
     self.assertLess(lowerCAmelCase	,							1e-4	,							"""passing latents as image input generate different result from passing image"""   )






@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     str:






     """simple docstring"""







     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   List[str]=0   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						torch.manual_seed(lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						load_image(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""   )
     __lowerCAmelCase  :      List[str]    =						{
         """prompt""": """turn him into a cyborg""",
         """image""": image,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """image_guidance_scale""": 1.0,
         """output_type""": """numpy""",
     }
     return inputs







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""	,							safety_checker=lowerCAmelCase   )
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()

     __lowerCAmelCase  :      Optional[int]    =						self.get_inputs()
     __lowerCAmelCase  :      Union[str, Any]    =						pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      Optional[Any]    =						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     __lowerCAmelCase  :      str    =						np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]   )

     assert np.abs(expected_slice - image_slice   ).max() < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""	,							safety_checker=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()

     __lowerCAmelCase  :      Optional[int]    =						self.get_inputs()
     __lowerCAmelCase  :      int    =						pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      Any    =						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     __lowerCAmelCase  :      Union[str, Any]    =						np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]   )

     assert np.abs(expected_slice - image_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""	,							safety_checker=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						DDIMScheduler.from_config(pipe.scheduler.config   )
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()

     __lowerCAmelCase  :      Optional[int]    =						self.get_inputs()
     __lowerCAmelCase  :      List[str]    =						pipe(**lowerCAmelCase   ).images
     __lowerCAmelCase  :      str    =						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     __lowerCAmelCase  :      int    =						np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]   )

     assert np.abs(expected_slice - image_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						0

     def callback_fn(lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   torch.FloatTensor   ) -> None:
        __lowerCAmelCase  :      Any    =						True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           __lowerCAmelCase  :      str    =						latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           __lowerCAmelCase  :      List[str]    =						latents[0, -3:, -3:, -1]
           __lowerCAmelCase  :      Dict    =						np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]   )

           assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2
        elif step == 2:
           __lowerCAmelCase  :      Tuple    =						latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           __lowerCAmelCase  :      str    =						latents[0, -3:, -3:, -1]
           __lowerCAmelCase  :      List[Any]    =						np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]   )

           assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2

     __lowerCAmelCase  :      List[Any]    =						False

     __lowerCAmelCase  :      Optional[int]    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""	,							safety_checker=lowerCAmelCase	,							torch_dtype=torch.floataa   )
     __lowerCAmelCase  :      Dict    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()

     __lowerCAmelCase  :      Dict    =						self.get_inputs()
     pipe(**lowerCAmelCase	,							callback=lowerCAmelCase	,							callback_steps=1   )
     assert callback_fn.has_been_called
     assert number_of_steps == 3







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Any:






     """simple docstring"""







     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __lowerCAmelCase  :      Union[str, Any]    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""	,							safety_checker=lowerCAmelCase	,							torch_dtype=torch.floataa   )
     __lowerCAmelCase  :      Optional[Any]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing(1   )
     pipe.enable_sequential_cpu_offload()

     __lowerCAmelCase  :      Tuple    =						self.get_inputs()
     __lowerCAmelCase  :      Union[str, Any]    =						pipe(**lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 10**9







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     __lowerCAmelCase  :      Tuple    =						inputs["""image"""].resize((5_04, 5_04)   )

     __lowerCAmelCase  :      str    =						"""timbrooks/instruct-pix2pix"""
     __lowerCAmelCase  :      Union[str, Any]    =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowerCAmelCase	,							safety_checker=lowerCAmelCase	,							)
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()

     __lowerCAmelCase  :      Dict    =						pipe(**lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						output.images[0]

     __lowerCAmelCase  :      str    =						image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     __lowerCAmelCase  :      Optional[int]    =						np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3





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      snake_case_				(__A					:						int					)      ->     List[Any]:
   random.seed(__A					)
   np.random.seed(__A					)
   torch.manual_seed(__A					)
   torch.cuda.manual_seed_all(__A					)
   # ^^ safe to call this function even if cuda is not available



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							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			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """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	,							)
        __lowerCAmelCase  :      int    =						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"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[Any]    =						"""The `max_value` argument is deprecated. Please use `decay` instead."""
        deprecate("""max_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						kwargs["""max_value"""]

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

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

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

     __lowerCAmelCase  :      List[Any]    =						None

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

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     int:






     """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__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

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

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







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """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	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

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

     __lowerCAmelCase  :      str    =						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():
           __lowerCAmelCase  :      List[Any]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						list(lowerCAmelCase   )
     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        param.data.copy_(s_param.to(param.device   ).data   )







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






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[param.detach().cpu().clone() for param in parameters]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							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.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						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  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, 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"""   )

     __lowerCAmelCase  :      List[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"""   )

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

     __lowerCAmelCase  :      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"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						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"""   )


