
"""simple docstring"""







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 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




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




 def   a      ( self       : List[str]     ,   a_       : Dict=0.9999 )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  UNetaDConditionModel.from_pretrained(self.model_id     ,   subfolder="""unet""" )
      UpperCAmelCase_						:    List[Any]    					=  unet.to(a_ )
      UpperCAmelCase_						:    Union[str, Any]    					=  EMAModel(unet.parameters()     ,   decay=a_     ,   model_cls=a_     ,   model_config=unet.config )
      return unet, ema_unet




 def   a      ( self       : int )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  torch.randn(
          self.batch_size     ,   self.num_in_channels     ,   self.latent_height     ,   self.latent_width     ,   generator=self.generator ).to(a_ )
      UpperCAmelCase_						:    Optional[Any]    					=  torch.randint(0     ,   10_00     ,   size=(self.batch_size,)     ,   generator=self.generator ).to(a_ )
      UpperCAmelCase_						:    str    					=  torch.randn(
          self.batch_size     ,   self.prompt_length     ,   self.text_encoder_hidden_dim     ,   generator=self.generator ).to(a_ )
      return noisy_latents, timesteps, encoder_hidden_states




 def   a      ( self       : List[str]     ,   a_       : Optional[int] )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  {}
      for k, param in unet.state_dict().items():
           UpperCAmelCase_						:    Optional[int]    					=  torch.randn_like(a_ ) + (param * torch.randn_like(a_ ))
           updated_state_dict.update({k: updated_param} )
      unet.load_state_dict(a_ )
      return unet




 def   a      ( self       : int )->					int:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Dict    					=  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   a      ( self       : Any )->					str:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    int    					=  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() )
      UpperCAmelCase_						:    Union[str, Any]    					=  list(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params     ,   a_ ):
           assert torch.allclose(a_     ,   a_ )

      # 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     ,   a_ ):
           assert torch.allclose(a_     ,   a_ )




 def   a      ( self       : int )->					Dict:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Any    					=  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.
      UpperCAmelCase_						:    int    					=  self.simulate_backprop(a_ )

      # 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.
      UpperCAmelCase_						:    str    					=  list(unet_pseudo_updated_step_one.parameters() )
      for s_param, param in zip(ema_unet.shadow_params     ,   a_ ):
           assert ~torch.allclose(a_     ,   a_ )

      # 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     ,   a_ ):
           assert ~torch.allclose(a_     ,   a_ )




 def   a      ( self       : Dict )->					int:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Dict    					=  self.get_models()

      # First backprop + EMA
      UpperCAmelCase_						:    Optional[Any]    					=  self.simulate_backprop(a_ )
      ema_unet.step(unet_step_one.parameters() )
      UpperCAmelCase_						:    int    					=  ema_unet.shadow_params

      # Second backprop + EMA
      UpperCAmelCase_						:    Dict    					=  self.simulate_backprop(a_ )
      ema_unet.step(unet_step_two.parameters() )
      UpperCAmelCase_						:    int    					=  ema_unet.shadow_params

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




 def   a      ( self       : str )->					Dict:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Optional[int]    					=  self.get_models(decay=0.0 )
      UpperCAmelCase_						:    Optional[int]    					=  self.simulate_backprop(a_ )
      ema_unet.step(unet_step_one.parameters() )
      UpperCAmelCase_						:    str    					=  ema_unet.shadow_params

      UpperCAmelCase_						:    List[str]    					=  self.simulate_backprop(a_ )
      ema_unet.step(unet_step_two.parameters() )
      UpperCAmelCase_						:    Tuple    					=  ema_unet.shadow_params

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






 @skip_mps
 def   a      ( self       : List[Any] )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    int    					=  self.get_models()
      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    Tuple    					=  self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(a_ )
           UpperCAmelCase_						:    Dict    					=  UNetaDConditionModel.from_pretrained(a_     ,   model_cls=a_ )
           UpperCAmelCase_						:    str    					=  loaded_unet.to(unet.device )

      # Since no EMA step has been performed the outputs should match.
      UpperCAmelCase_						:    Optional[Any]    					=  unet(a_     ,   a_     ,   a_ ).sample
      UpperCAmelCase_						:    Union[str, Any]    					=  loaded_unet(a_     ,   a_     ,   a_ ).sample

      assert torch.allclose(a_     ,   a_     ,   atol=1E-4 )



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




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



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

