



'''simple docstring'''




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      UpperCamelCase_					(				enum.Enum     ):
	lowercase		=					'''timm'''
	lowercase		=					'''transformers'''




def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     Optional[Any]:

							if stage_names is None:
														raise ValueError("""Stage_names must be set for transformers backbones"""						)

							if out_features is not None:
														if not isinstance(lowercase_      ,							(list,)						):
																					raise ValueError(F'''out_features must be a list {type(lowercase_						)}'''						)
														if any(feat not in stage_names for feat in out_features						):
																					raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''						)

							if out_indices is not None:
														if not isinstance(lowercase_      ,							(list, tuple)						):
																					raise ValueError(F'''out_indices must be a list or tuple, got {type(lowercase_						)}'''						)
														if any(idx >= len(lowercase_						) for idx in out_indices						):
																					raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""						)

							if out_features is not None and out_indices is not None:
														if len(lowercase_						) != len(lowercase_						):
																					raise ValueError("""out_features and out_indices should have the same length if both are set"""						)
														if out_features != [stage_names[idx] for idx in out_indices]:
																					raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							) ->     Union[str, Any]:

							if out_indices is None and out_features is None:
														UpperCAmelCase	:						str					=						[len(lowercase_						) - 1]
														UpperCAmelCase	:						Tuple					=						[stage_names[-1]]
							elif out_indices is None and out_features is not None:
														UpperCAmelCase	:						List[Any]					=						[stage_names.index(lowercase_						) for layer in out_features]
							elif out_features is None and out_indices is not None:
														UpperCAmelCase	:						Any					=						[stage_names[idx] for idx in out_indices]
							return out_features, out_indices



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							) ->     Tuple[List[str], List[int]]:
							verify_out_features_out_indices(out_features=lowercase_      ,							out_indices=lowercase_      ,							stage_names=lowercase_						)
							UpperCAmelCase	,	UpperCAmelCase	:						Optional[Any]					=						_align_output_features_output_indices(
							    out_features=lowercase_      ,							out_indices=lowercase_      ,							stage_names=lowercase_						)
							# Verify that the aligned out_features and out_indices are valid
							verify_out_features_out_indices(out_features=lowercase_      ,							out_indices=lowercase_      ,							stage_names=lowercase_						)
							return output_features, output_indices

class      UpperCamelCase_					:
	lowercase		=					None





	def 					_lowercase(    self						,						A       )     ->     None:
								if getattr(self						,						"""_backbone"""						,						lowerCAmelCase__       ) is None:
															raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""       )

								# These will diagree with the defaults for the transformers models e.g. for resnet50
								# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
								# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
								UpperCAmelCase	:						Tuple					=						[stage["""module"""] for stage in self._backbone.feature_info.info]
								UpperCAmelCase	:						List[str]					=						[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
								UpperCAmelCase	:						Dict					=						self._backbone.feature_info.out_indices
								UpperCAmelCase	:						int					=						self._backbone.feature_info.module_name()

								# We verify the out indices and out features are valid
								verify_out_features_out_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=self.stage_names       )
								UpperCAmelCase	,	UpperCAmelCase	:						Optional[Any]					=						out_features, out_indices





	def 					_lowercase(    self						,						A       )     ->     None:
								UpperCAmelCase	:						Tuple					=						getattr(lowerCAmelCase__						,						"""stage_names"""       )
								UpperCAmelCase	:						Optional[Any]					=						getattr(lowerCAmelCase__						,						"""out_features"""						,						lowerCAmelCase__       )
								UpperCAmelCase	:						Any					=						getattr(lowerCAmelCase__						,						"""out_indices"""						,						lowerCAmelCase__       )

								UpperCAmelCase	:						List[Any]					=						stage_names
								UpperCAmelCase	,	UpperCAmelCase	:						Any					=						get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=lowerCAmelCase__       )
								# Number of channels for each stage. This is set in the transformer backbone model init
								UpperCAmelCase	:						Dict					=						None





	def 					_lowercase(    self						,						A       )     ->     None:
								UpperCAmelCase	:						Tuple					=						config

								UpperCAmelCase	:						int					=						getattr(lowerCAmelCase__						,						"""use_timm_backbone"""						,						lowerCAmelCase__       )
								UpperCAmelCase	:						int					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

								if self.backbone_type == BackboneType.TIMM:
															self._init_timm_backbone(lowerCAmelCase__       )
								elif self.backbone_type == BackboneType.TRANSFORMERS:
															self._init_transformers_backbone(lowerCAmelCase__       )
								else:
															raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''       )





	@property
	def 					_lowercase(    self       )     ->     Optional[int]:
								return self._out_features





	@out_features.setter
	def 					_lowercase(    self						,						A       )     ->     Dict:
								UpperCAmelCase	,	UpperCAmelCase	:						Dict					=						get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     List[str]:
								return self._out_indices





	@out_indices.setter
	def 					_lowercase(    self						,						A       )     ->     Optional[int]:
								UpperCAmelCase	,	UpperCAmelCase	:						Optional[Any]					=						get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     Optional[Any]:
								return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names       )}





	@property
	def 					_lowercase(    self       )     ->     List[str]:
								return [self.out_feature_channels[name] for name in self.out_features]





	def 					_lowercase(    self						,						*A						,						**A       )     ->     Tuple:
								UpperCAmelCase	:						List[str]					=						dict(inspect.signature(self.forward       ).parameters       )
								UpperCAmelCase	:						Any					=						{k: v for k, v in kwargs.items() if k in signature}
								return self(*lowerCAmelCase__						,						**lowerCAmelCase__       )





	def 					_lowercase(    self						,						A						,						A = None						,						A = None						,						A = None						,						)     ->     Union[str, Any]:
								raise NotImplementedError("""This method should be implemented by the derived class."""       )






	def 					_lowercase(    self       )     ->     Dict:
								UpperCAmelCase	:						Union[str, Any]					=						super().to_dict()
								UpperCAmelCase	:						Optional[int]					=						output.pop("""_out_features"""       )
								UpperCAmelCase	:						Tuple					=						output.pop("""_out_indices"""       )
								return output




class      UpperCamelCase_					:





	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return self._out_features





	@out_features.setter
	def 					_lowercase(    self						,						A       )     ->     List[str]:
								UpperCAmelCase	,	UpperCAmelCase	:						int					=						get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     List[str]:
								return self._out_indices





	@out_indices.setter
	def 					_lowercase(    self						,						A       )     ->     Dict:
								UpperCAmelCase	,	UpperCAmelCase	:						str					=						get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__						,						out_indices=lowerCAmelCase__						,						stage_names=self.stage_names       )






	def 					_lowercase(    self       )     ->     Tuple:
								UpperCAmelCase	:						List[Any]					=						super().to_dict()
								UpperCAmelCase	:						Any					=						output.pop("""_out_features"""       )
								UpperCAmelCase	:						Dict					=						output.pop("""_out_indices"""       )
								return output









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)




