






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)





'''simple docstring'''

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput





@dataclass
class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =42
   lowerCamelCase_            =None
   lowerCamelCase_            =None





class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Tuple)   ->       Dict:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(x.a ,		30)
         self.assertIsNone(x.b)
         self.assertIsNone(x.c)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.d


   def __UpperCAmelCase							( self					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["b"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["c"]

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["c"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["b"]


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(list(x.values()) ,		[30])
         self.assertEqual(list(x.items()) ,		[("a", 30)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a"])

         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("b", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "b"])

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(list(x.keys()) ,		["a", "c"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("c", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "c"])

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.update({"d": 20})
         with self.assertRaises(__lowerCAmelCase):
               del x["a"]
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.pop("a")
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.setdefault("d" ,		32)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Optional[int]:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         lowercase_     			=   ModelOutputTest({"a": 30, "b": 10})
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)


   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         lowercase_     			=   ModelOutputTest([("a", 30), ("b", 10)])
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   ModelOutputTest([("a", 30), (10, 10)])

         lowercase_     			=   ModelOutputTest(a=(30, 30))
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(x.a ,		(30, 30))






