'''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       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


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







class lowerCamelCase							:




  '''simple docstring'''



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

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

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

        UpperCAmelCase_            =  list(UpperCAmelCase__  )
        UpperCAmelCase_            =  [p.clone().detach() for p in parameters]

        if kwargs.get('''device'''  ,							UpperCAmelCase__  ) is not None:
              UpperCAmelCase_            =  '''The `device` argument is deprecated. Please use `to` instead.'''
              deprecate('''device'''  ,							'''1.0.0'''  ,							UpperCAmelCase__  ,							standard_warn=UpperCAmelCase__  )
              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  lowerCAmelCase__ (      cls			:       Union[str, Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       List[Any]  )				->"EMAModel":
        UpperCAmelCase_      ,		UpperCAmelCase_            =  model_cls.load_config(UpperCAmelCase__  ,							return_unused_kwargs=UpperCAmelCase__  )
        UpperCAmelCase_            =  model_cls.from_pretrained(UpperCAmelCase__  )

        UpperCAmelCase_            =  cls(model.parameters()  ,							model_cls=UpperCAmelCase__  ,							model_config=model.config  )

        ema_model.load_state_dict(UpperCAmelCase__  )
        return ema_model






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Tuple:
        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'''  ,							UpperCAmelCase__  )

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






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       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(UpperCAmelCase__  ,							self.decay  )
        # make sure decay is not smaller than min_decay
        UpperCAmelCase_            =  max(UpperCAmelCase__  ,							self.min_decay  )
        return cur_decay_value






  @torch.no_grad()
  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Iterable[torch.nn.Parameter]  )				->Optional[Any]:
        if isinstance(UpperCAmelCase__  ,							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'''  ,							UpperCAmelCase__  ,							standard_warn=UpperCAmelCase__  ,							)
              UpperCAmelCase_            =  parameters.parameters()

        UpperCAmelCase_            =  list(UpperCAmelCase__  )

        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  ,							UpperCAmelCase__  ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                    UpperCAmelCase_            =  deepspeed.zero.GatheredParameters(UpperCAmelCase__  ,							modifier_rank=UpperCAmelCase__  )

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






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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       List[Any]=None  ,							UpperCAmelCase__			:       str=None  )				->None:
        UpperCAmelCase_            =  [
            p.to(device=UpperCAmelCase__  ,							dtype=UpperCAmelCase__  ) if p.is_floating_point() else p.to(device=UpperCAmelCase__  )
            for p in self.shadow_params
        ]






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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       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  ,							UpperCAmelCase__  ):
              param.data.copy_(c_param.data  )

        # Better memory-wise.
        UpperCAmelCase_            =  None






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       dict  )				->None:
        UpperCAmelCase_            =  copy.deepcopy(UpperCAmelCase__  )

        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  ,							UpperCAmelCase__  ):
              raise ValueError('''Invalid min_decay'''  )

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

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

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






'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







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




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def  lowerCAmelCase__ (      self			:       str  )				->List[str]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]  )

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






  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-1






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[int]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_dpmpp_2m'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							num_inference_steps=15  ,							output_type='''np'''  ,							use_karras_sigmas=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array(
            [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]  )

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





