






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 	a_(		lowercase__			,							lowercase__	):



     """simple docstring"""



     __snake_case		:				str  					='''convnext'''





     def __init__(		self					:	List[str]  ,  lowerCAmelCase__					:	List[Any]=3  ,  lowerCAmelCase__					:	Any=4  ,  lowerCAmelCase__					:	int=4  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[str]=None  ,  lowerCAmelCase__					:	int="gelu"  ,  lowerCAmelCase__					:	List[str]=0.02  ,  lowerCAmelCase__					:	Optional[int]=1e-12  ,  lowerCAmelCase__					:	List[str]=1e-6  ,  lowerCAmelCase__					:	List[Any]=0.0  ,  lowerCAmelCase__					:	Optional[int]=2_2_4  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	List[Any]=None  ,  **lowerCAmelCase__					:	Union[str, Any]  ,  )							->	Union[str, Any]:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		patch_size
      SCREAMING_SNAKE_CASE				    =		num_stages
      SCREAMING_SNAKE_CASE				    =		[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE				    =		[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps
      SCREAMING_SNAKE_CASE				    =		layer_scale_init_value
      SCREAMING_SNAKE_CASE				    =		drop_path_rate
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		['stem'] + [f'''stage{idx}''' for idx in range(1  ,  len(self.depths) + 1)]
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)

class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Optional[int]  					=version.parse('''1.11'''	)





     @property
     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Mapping[str, Mapping[int, str]]:

      """simple docstring"""
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ])





     @property
     def     __UpperCamelCase						(		self					:	Dict)							->	float:

      """simple docstring"""
      return 1e-5












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				str  					='''hf-internal-testing/tiny-stable-diffusion-pipe'''
     __snake_case		:				Union[str, Any]  					=1
     __snake_case		:				str  					=77
     __snake_case		:				int  					=32
     __snake_case		:				List[str]  					=4
     __snake_case		:				Any  					=64
     __snake_case		:				Optional[int]  					=torch.manual_seed(0	)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	str=0.99_99)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		UNetaDConditionModel.from_pretrained(self.model_id  ,  subfolder='unet')
      SCREAMING_SNAKE_CASE				    =		unet.to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		EMAModel(unet.parameters()  ,  decay=lowerCAmelCase__  ,  model_cls=lowerCAmelCase__  ,  model_config=unet.config)
      return unet, ema_unet





     def     __UpperCamelCase						(		self					:	List[str])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		torch.randn(
          self.batch_size  ,  self.num_in_channels  ,  self.latent_height  ,  self.latent_width  ,  generator=self.generator).to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		torch.randint(0  ,  1_0_0_0  ,  size=(self.batch_size,)  ,  generator=self.generator).to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		torch.randn(
          self.batch_size  ,  self.prompt_length  ,  self.text_encoder_hidden_dim  ,  generator=self.generator).to(lowerCAmelCase__)
      return noisy_latents, timesteps, encoder_hidden_states





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	Tuple)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{}
      for k, param in unet.state_dict().items():
       SCREAMING_SNAKE_CASE				    =		torch.randn_like(lowerCAmelCase__) + (param * torch.randn_like(lowerCAmelCase__))
       updated_state_dict.update({k: updated_param})
      unet.load_state_dict(lowerCAmelCase__)
      return unet





     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Take the first (hypothetical) EMA step.
      ema_unet.step(unet.parameters())
      assert ema_unet.optimization_step == 1

      # Take two more.
      for _ in range(2):
       ema_unet.step(unet.parameters())
      assert ema_unet.optimization_step == 3





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Since the `unet` is not being updated (i.e., backprop'd)
      # there won't be any difference between the `params` of `unet`
      # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
      ema_unet.step(unet.parameters())
      SCREAMING_SNAKE_CASE				    =		list(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)

      # The above holds true even if we call `ema.step()` multiple times since
      # `unet` params are still not being updated.
      for _ in range(4):
       ema_unet.step(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Here we simulate the parameter updates for `unet`. Since there might
      # be some parameters which are initialized to zero we take extra care to
      # initialize their values to something non-zero before the multiplication.
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)

      # Take the EMA step.
      ema_unet.step(unet_pseudo_updated_step_one.parameters())

      # Now the EMA'd parameters won't be equal to the original model parameters.
      SCREAMING_SNAKE_CASE				    =		list(unet_pseudo_updated_step_one.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)

      # Ensure this is the case when we take multiple EMA steps.
      for _ in range(4):
       ema_unet.step(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Dict:

      """simple docstring"""
      # If we call EMA step after a backpropagation consecutively for two times,
      # the shadow params from those two steps should be different.
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()

      # First backprop + EMA
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_one.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      # Second backprop + EMA
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_two.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase__  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





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

      """simple docstring"""
      # If there's no decay even if there are backprops, EMA steps
      # won't take any effect i.e., the shadow params would remain the
      # same.
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models(decay=0.0)
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_one.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_two.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase__  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     @skip_mps
     def     __UpperCamelCase						(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
       ema_unet.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		UNetaDConditionModel.from_pretrained(lowerCAmelCase__  ,  model_cls=lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		loaded_unet.to(unet.device)

      # Since no EMA step has been performed the outputs should match.
      SCREAMING_SNAKE_CASE				    =		unet(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__).sample
      SCREAMING_SNAKE_CASE				    =		loaded_unet(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__).sample

      assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__  ,  atol=1e-4)




