


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

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Tuple=0.9_9_9_9     ):
            a_     :  Optional[int]				      =     UNetaDConditionModel.from_pretrained(self.model_id   ,				subfolder="""unet"""     )
            a_     :  Any				      =     unet.to(__SCREAMING_SNAKE_CASE     )
            a_     :  Dict				      =     EMAModel(unet.parameters()   ,				decay=__SCREAMING_SNAKE_CASE   ,				model_cls=__SCREAMING_SNAKE_CASE   ,				model_config=unet.config     )
            return unet, ema_unet

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Tuple				      =     torch.randn(
                self.batch_size   ,				self.num_in_channels   ,				self.latent_height   ,				self.latent_width   ,				generator=self.generator     ).to(__SCREAMING_SNAKE_CASE     )
            a_     :  Optional[int]				      =     torch.randint(0   ,				1_0_0_0   ,				size=(self.batch_size,)   ,				generator=self.generator     ).to(__SCREAMING_SNAKE_CASE     )
            a_     :  List[Any]				      =     torch.randn(
                self.batch_size   ,				self.prompt_length   ,				self.text_encoder_hidden_dim   ,				generator=self.generator     ).to(__SCREAMING_SNAKE_CASE     )
            return noisy_latents, timesteps, encoder_hidden_states

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  Union[str, Any]     ):
            a_     :  Dict				      =     {}
            for k, param in unet.state_dict().items():
                 a_     :  int				      =     torch.randn_like(__SCREAMING_SNAKE_CASE     ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE     ))
                 updated_state_dict.update({k: updated_param}     )
            unet.load_state_dict(__SCREAMING_SNAKE_CASE     )
            return unet

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Optional[Any]				      =     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   :  Union[str, Any]     ):
            a_     :  List[Any]				      =     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()     )
            a_     :  int				      =     list(unet.parameters()     )
            for s_param, param in zip(ema_unet.shadow_params   ,				__SCREAMING_SNAKE_CASE     ):
                 assert torch.allclose(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  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.
            a_     :  Union[str, Any]				      =     self.simulate_backprop(__SCREAMING_SNAKE_CASE     )

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

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

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Optional[int]				      =     self.get_models()

            # First backprop + EMA
            a_     :  Dict				      =     self.simulate_backprop(__SCREAMING_SNAKE_CASE     )
            ema_unet.step(unet_step_one.parameters()     )
            a_     :  List[str]				      =     ema_unet.shadow_params

            # Second backprop + EMA
            a_     :  str				      =     self.simulate_backprop(__SCREAMING_SNAKE_CASE     )
            ema_unet.step(unet_step_two.parameters()     )
            a_     :  List[Any]				      =     ema_unet.shadow_params

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

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  int				      =     self.get_models(decay=0.0     )
            a_     :  Optional[int]				      =     self.simulate_backprop(__SCREAMING_SNAKE_CASE     )
            ema_unet.step(unet_step_one.parameters()     )
            a_     :  Union[str, Any]				      =     ema_unet.shadow_params

            a_     :  Any				      =     self.simulate_backprop(__SCREAMING_SNAKE_CASE     )
            ema_unet.step(unet_step_two.parameters()     )
            a_     :  int				      =     ema_unet.shadow_params

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

       @skip_mps
       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  str				      =     self.get_models()
            a_     :  Union[str, Any]				      =     self.get_dummy_inputs()

            with tempfile.TemporaryDirectory() as tmpdir:
                 ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE     )
                 a_     :  Optional[int]				      =     UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE   ,				model_cls=__SCREAMING_SNAKE_CASE     )
                 a_     :  List[str]				      =     loaded_unet.to(unet.device     )

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

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







def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		len(SCREAMING_SNAKE_CASE_							)
       for i in range(length - 1							):
              UpperCamelCase     :						Optional[int] 						=		False
              for j in range(length - 1 - i							):
                     if collection[j] > collection[j + 1]:
                            UpperCamelCase     :						Optional[int] 						=		True
                            UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
  import doctest
  import time

  doctest.testmod()

  __UpperCAmelCase			:			Any			      =					input("Enter numbers separated by a comma:").strip()
  __UpperCAmelCase			:			List[str]			      =					[int(item) for item in user_input.split(",")]
  __UpperCAmelCase			:			List[Any]			      =					time.process_time()
  print(*bubble_sort(unsorted), sep=",")
  print(f'''Processing time: {(time.process_time() - start)%1E9 + 7}''')

