'''simple docstring'''






import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a										=					logging.get_logger(__name__)

enable_full_determinism()





class     A__							(       lowerCamelCase__					,		lowerCamelCase__					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						List[Any]    				=     UNetaDModel
   UpperCamelCase_       :						List[str]    				=     '''sample'''




   @property
   def 			_lowerCAmelCase		( self     :		str			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   4
      _UpperCAmelCase							:	Optional[int]							=   3
      _UpperCAmelCase							:	Optional[int]							=   (3_2, 3_2)

      _UpperCAmelCase							:	int							=   floats_tensor((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	int							=   torch.tensor([1_0]			).to(lowerCAmelCase__			)

      return {"sample": noise, "timestep": time_step}




   @property
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Any:







      """simple docstring"""




      return (3, 3_2, 3_2)




   @property
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Union[str, Any]:







      """simple docstring"""




      return (3, 3_2, 3_2)




   def 			_lowerCAmelCase		( self     :		int			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   {
          """block_out_channels""": (3_2, 6_4),
          """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
          """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
          """attention_head_dim""": 3,
          """out_channels""": 3,
          """in_channels""": 3,
          """layers_per_block""": 2,
          """sample_size""": 3_2,
      }
      _UpperCAmelCase							:	Optional[Any]							=   self.dummy_input
      return init_dict, inputs_dict





class     A__							(       lowerCamelCase__					,		lowerCamelCase__					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						str    				=     UNetaDModel
   UpperCamelCase_       :						Union[str, Any]    				=     '''sample'''




   @property
   def 			_lowerCAmelCase		( self     :		int			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   4
      _UpperCAmelCase							:	Union[str, Any]							=   4
      _UpperCAmelCase							:	Union[str, Any]							=   (3_2, 3_2)

      _UpperCAmelCase							:	Optional[Any]							=   floats_tensor((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Union[str, Any]							=   torch.tensor([1_0]			).to(lowerCAmelCase__			)

      return {"sample": noise, "timestep": time_step}




   @property
   def 			_lowerCAmelCase		( self     :		Any			)       ->    str:







      """simple docstring"""




      return (4, 3_2, 3_2)




   @property
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    List[str]:







      """simple docstring"""




      return (4, 3_2, 3_2)




   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   {
          """sample_size""": 3_2,
          """in_channels""": 4,
          """out_channels""": 4,
          """layers_per_block""": 2,
          """block_out_channels""": (3_2, 6_4),
          """attention_head_dim""": 3_2,
          """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
          """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
      }
      _UpperCAmelCase							:	Optional[int]							=   self.dummy_input
      return init_dict, inputs_dict




   def 			_lowerCAmelCase		( self     :		int			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=lowerCAmelCase__			)

      self.assertIsNotNone(lowerCAmelCase__			)
      self.assertEqual(len(loading_info["missing_keys"]			)						,     0			)

      model.to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Dict							=   model(**self.dummy_input			).sample

      assert image is not None, "Make sure output is not None"




   @unittest.skipIf(torch_device != "cuda"						,     "This test is supposed to run on GPU"			)
   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=lowerCAmelCase__			)
      model.to(lowerCAmelCase__			)
      _UpperCAmelCase							:	int							=   model(**self.dummy_input			).sample

      assert image is not None, "Make sure output is not None"




   @unittest.skipIf(torch_device != "cuda"						,     "This test is supposed to run on GPU"			)
   def 			_lowerCAmelCase		( self     :		Any			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=lowerCAmelCase__			)
      model_accelerate.to(lowerCAmelCase__			)
      model_accelerate.eval()

      _UpperCAmelCase							:	Tuple							=   torch.randn(
          1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0			)						,     )
      _UpperCAmelCase							:	List[Any]							=   noise.to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Any							=   torch.tensor([1_0] * noise.shape[0]			).to(lowerCAmelCase__			)

      _UpperCAmelCase							:	Tuple							=   model_accelerate(lowerCAmelCase__						,     lowerCAmelCase__			)["""sample"""]

      # two models don't need to stay in the device at the same time
      del model_accelerate
      torch.cuda.empty_cache()
      gc.collect()

      _UpperCAmelCase							:	str							=   UNetaDModel.from_pretrained(
          "fusing/unet-ldm-dummy-update"						,     output_loading_info=lowerCAmelCase__						,     low_cpu_mem_usage=lowerCAmelCase__			)
      model_normal_load.to(lowerCAmelCase__			)
      model_normal_load.eval()
      _UpperCAmelCase							:	Optional[int]							=   model_normal_load(lowerCAmelCase__						,     lowerCAmelCase__			)["""sample"""]

      assert torch_all_close(lowerCAmelCase__						,     lowerCAmelCase__						,     rtol=1e-3			)




   def 			_lowerCAmelCase		( self     :		str			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"			)
      model.eval()
      model.to(lowerCAmelCase__			)

      _UpperCAmelCase							:	List[Any]							=   torch.randn(
          1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0			)						,     )
      _UpperCAmelCase							:	Union[str, Any]							=   noise.to(lowerCAmelCase__			)
      _UpperCAmelCase							:	List[Any]							=   torch.tensor([1_0] * noise.shape[0]			).to(lowerCAmelCase__			)

      with torch.no_grad():
         _UpperCAmelCase							:	Union[str, Any]							=   model(lowerCAmelCase__						,     lowerCAmelCase__			).sample

      _UpperCAmelCase							:	List[Any]							=   output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      _UpperCAmelCase							:	List[str]							=   torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]			)
      # fmt: on

      self.assertTrue(torch_all_close(lowerCAmelCase__						,     lowerCAmelCase__						,     rtol=1e-3			)			)





class     A__							(       lowerCamelCase__					,		lowerCamelCase__					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						int    				=     UNetaDModel
   UpperCamelCase_       :						Tuple    				=     '''sample'''




   @property
   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		Any=(3_2, 3_2)			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   4
      _UpperCAmelCase							:	Union[str, Any]							=   3

      _UpperCAmelCase							:	Any							=   floats_tensor((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	List[Any]							=   torch.tensor(batch_size * [1_0]			).to(dtype=torch.intaa						,     device=lowerCAmelCase__			)

      return {"sample": noise, "timestep": time_step}




   @property
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Tuple:







      """simple docstring"""




      return (3, 3_2, 3_2)




   @property
   def 			_lowerCAmelCase		( self     :		Any			)       ->    Optional[Any]:







      """simple docstring"""




      return (3, 3_2, 3_2)




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







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   {
          """block_out_channels""": [3_2, 6_4, 6_4, 6_4],
          """in_channels""": 3,
          """layers_per_block""": 1,
          """out_channels""": 3,
          """time_embedding_type""": """fourier""",
          """norm_eps""": 1e-6,
          """mid_block_scale_factor""": math.sqrt(2.0			),
          """norm_num_groups""": None,
          """down_block_types""": [
              """SkipDownBlock2D""",
              """AttnSkipDownBlock2D""",
              """SkipDownBlock2D""",
              """SkipDownBlock2D""",
          ],
          """up_block_types""": [
              """SkipUpBlock2D""",
              """SkipUpBlock2D""",
              """AttnSkipUpBlock2D""",
              """SkipUpBlock2D""",
          ],
      }
      _UpperCAmelCase							:	Tuple							=   self.dummy_input
      return init_dict, inputs_dict




   @slow
   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"						,     output_loading_info=lowerCAmelCase__			)
      self.assertIsNotNone(lowerCAmelCase__			)
      self.assertEqual(len(loading_info["missing_keys"]			)						,     0			)

      model.to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Optional[Any]							=   self.dummy_input
      _UpperCAmelCase							:	List[str]							=   floats_tensor((4, 3) + (2_5_6, 2_5_6)			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Optional[Any]							=   noise
      _UpperCAmelCase							:	List[str]							=   model(**lowerCAmelCase__			)

      assert image is not None, "Make sure output is not None"




   @slow
   def 			_lowerCAmelCase		( self     :		str			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"			)
      model.to(lowerCAmelCase__			)

      _UpperCAmelCase							:	Any							=   4
      _UpperCAmelCase							:	str							=   3
      _UpperCAmelCase							:	List[str]							=   (2_5_6, 2_5_6)

      _UpperCAmelCase							:	Union[str, Any]							=   torch.ones((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	int							=   torch.tensor(batch_size * [1e-4]			).to(lowerCAmelCase__			)

      with torch.no_grad():
         _UpperCAmelCase							:	Optional[int]							=   model(lowerCAmelCase__						,     lowerCAmelCase__			).sample

      _UpperCAmelCase							:	int							=   output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      _UpperCAmelCase							:	Union[str, Any]							=   torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]			)
      # fmt: on

      self.assertTrue(torch_all_close(lowerCAmelCase__						,     lowerCAmelCase__						,     rtol=1e-2			)			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"			)
      model.to(lowerCAmelCase__			)

      _UpperCAmelCase							:	Union[str, Any]							=   4
      _UpperCAmelCase							:	Union[str, Any]							=   3
      _UpperCAmelCase							:	List[Any]							=   (3_2, 3_2)

      _UpperCAmelCase							:	Union[str, Any]							=   torch.ones((batch_size, num_channels) + sizes			).to(lowerCAmelCase__			)
      _UpperCAmelCase							:	Union[str, Any]							=   torch.tensor(batch_size * [1e-4]			).to(lowerCAmelCase__			)

      with torch.no_grad():
         _UpperCAmelCase							:	Any							=   model(lowerCAmelCase__						,     lowerCAmelCase__			).sample

      _UpperCAmelCase							:	Optional[int]							=   output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      _UpperCAmelCase							:	Tuple							=   torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]			)
      # fmt: on

      self.assertTrue(torch_all_close(lowerCAmelCase__						,     lowerCAmelCase__						,     rtol=1e-2			)			)




   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    int:







      """simple docstring"""




      pass




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]