





"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

enable_full_determinism()

class 		lowercase					(					lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      UNetaDModel
      _SCREAMING_SNAKE_CASE					=      'sample'





      @property
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       3
            lowerCAmelCase  						=       (32, 32)

            lowerCAmelCase  						=       floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor([10] ).to(__lowerCAmelCase )

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





      @property
      def     _snake_case			(       self )		->     Tuple:
            return (3, 32, 32)





      @property
      def     _snake_case			(       self )		->     Tuple:
            return (3, 32, 32)





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       {
                """block_out_channels""": (32, 64),
                """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""": 32,
            }
            lowerCAmelCase  						=       self.dummy_input
            return init_dict, inputs_dict







class 		lowercase					(					lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      UNetaDModel
      _SCREAMING_SNAKE_CASE					=      'sample'





      @property
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       (32, 32)

            lowerCAmelCase  						=       floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor([10] ).to(__lowerCAmelCase )

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





      @property
      def     _snake_case			(       self )		->     int:
            return (4, 32, 32)





      @property
      def     _snake_case			(       self )		->     Optional[int]:
            return (4, 32, 32)





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       {
                """sample_size""": 32,
                """in_channels""": 4,
                """out_channels""": 4,
                """layers_per_block""": 2,
                """block_out_channels""": (32, 64),
                """attention_head_dim""": 32,
                """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
                """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
            }
            lowerCAmelCase  						=       self.dummy_input
            return init_dict, inputs_dict





      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase						,		lowerCAmelCase  						=       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 )
            lowerCAmelCase  						=       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     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,						output_loading_info=__lowerCAmelCase )
            model.to(__lowerCAmelCase )
            lowerCAmelCase  						=       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     _snake_case			(       self )		->     List[Any]:
            # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
            lowerCAmelCase						,		lowerCAmelCase  						=       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,						output_loading_info=__lowerCAmelCase )
            model_accelerate.to(__lowerCAmelCase )
            model_accelerate.eval()

            lowerCAmelCase  						=       torch.randn(
                1      ,						model_accelerate.config.in_channels      ,						model_accelerate.config.sample_size      ,						model_accelerate.config.sample_size      ,						generator=torch.manual_seed(0 )      ,						)
            lowerCAmelCase  						=       noise.to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor([10] * noise.shape[0] ).to(__lowerCAmelCase )

            lowerCAmelCase  						=       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()

            lowerCAmelCase						,		lowerCAmelCase  						=       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()
            lowerCAmelCase  						=       model_normal_load(__lowerCAmelCase      ,						__lowerCAmelCase )["""sample"""]

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





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" )
            model.eval()
            model.to(__lowerCAmelCase )

            lowerCAmelCase  						=       torch.randn(
                1      ,						model.config.in_channels      ,						model.config.sample_size      ,						model.config.sample_size      ,						generator=torch.manual_seed(0 )      ,						)
            lowerCAmelCase  						=       noise.to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor([10] * noise.shape[0] ).to(__lowerCAmelCase )

            with torch.no_grad():
                  lowerCAmelCase  						=       model(__lowerCAmelCase      ,						__lowerCAmelCase ).sample

            lowerCAmelCase  						=       output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            lowerCAmelCase  						=       torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800] )
            # fmt: on

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







class 		lowercase					(					lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      UNetaDModel
      _SCREAMING_SNAKE_CASE					=      'sample'





      @property
      def     _snake_case			(       self      ,						lowercase=(32, 32) )		->     str:
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       3

            lowerCAmelCase  						=       floats_tensor((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor(batch_size * [10] ).to(dtype=torch.intaa      ,						device=__lowerCAmelCase )

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





      @property
      def     _snake_case			(       self )		->     Tuple:
            return (3, 32, 32)





      @property
      def     _snake_case			(       self )		->     List[str]:
            return (3, 32, 32)





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       {
                """block_out_channels""": [32, 64, 64, 64],
                """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""",
                ],
            }
            lowerCAmelCase  						=       self.dummy_input
            return init_dict, inputs_dict





      @slow
      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       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 )
            lowerCAmelCase  						=       self.dummy_input
            lowerCAmelCase  						=       floats_tensor((4, 3) + (256, 256) ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       noise
            lowerCAmelCase  						=       model(**__lowerCAmelCase )

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





      @slow
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256""" )
            model.to(__lowerCAmelCase )

            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       3
            lowerCAmelCase  						=       (256, 256)

            lowerCAmelCase  						=       torch.ones((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor(batch_size * [1e-4] ).to(__lowerCAmelCase )

            with torch.no_grad():
                  lowerCAmelCase  						=       model(__lowerCAmelCase      ,						__lowerCAmelCase ).sample

            lowerCAmelCase  						=       output[0, -3:, -3:, -1].flatten().cpu()
            # fmt: off
            lowerCAmelCase  						=       torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608] )
            # fmt: on

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





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update""" )
            model.to(__lowerCAmelCase )

            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       3
            lowerCAmelCase  						=       (32, 32)

            lowerCAmelCase  						=       torch.ones((batch_size, num_channels) + sizes ).to(__lowerCAmelCase )
            lowerCAmelCase  						=       torch.tensor(batch_size * [1e-4] ).to(__lowerCAmelCase )

            with torch.no_grad():
                  lowerCAmelCase  						=       model(__lowerCAmelCase      ,						__lowerCAmelCase ).sample

            lowerCAmelCase  						=       output[0, -3:, -3:, -1].flatten().cpu()
            # fmt: off
            lowerCAmelCase  						=       torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256] )
            # fmt: on

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





      def     _snake_case			(       self )		->     Union[str, Any]:
            # not required for this model
            pass







"""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


UpperCAmelCase__     						=  logging.get_logger(__name__)

enable_full_determinism()


class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							UNetaDModel
    _snake_case   :     List[str]   							=							'sample'


    @property
    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					3
      _UpperCAmelCase         =					(32, 32)

      _UpperCAmelCase         =					floats_tensor((batch_size, num_channels) + sizes    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor([10]    ).to(__lowerCAmelCase    )

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


    @property
    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      return (3, 32, 32)


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return (3, 32, 32)


    def    lowerCAmelCase_     (    self		:     Any    ):
      _UpperCAmelCase         =					{
          """block_out_channels""": (32, 64),
          """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""": 32,
      }
      _UpperCAmelCase         =					self.dummy_input
      return init_dict, inputs_dict



class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     int   							=							UNetaDModel
    _snake_case   :     Optional[Any]   							=							'sample'


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					(32, 32)

      _UpperCAmelCase         =					floats_tensor((batch_size, num_channels) + sizes    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor([10]    ).to(__lowerCAmelCase    )

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


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return (4, 32, 32)


    @property
    def    lowerCAmelCase_     (    self		:     Dict    ):
      return (4, 32, 32)


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					{
          """sample_size""": 32,
          """in_channels""": 4,
          """out_channels""": 4,
          """layers_per_block""": 2,
          """block_out_channels""": (32, 64),
          """attention_head_dim""": 32,
          """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
          """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
      }
      _UpperCAmelCase         =					self.dummy_input
      return init_dict, inputs_dict


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase       , _UpperCAmelCase         =					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         =					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		:     Optional[int]    ):
      _UpperCAmelCase       , _UpperCAmelCase         =					UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      , output_loading_info=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      _UpperCAmelCase         =					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		:     str    ):
      # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
      _UpperCAmelCase       , _UpperCAmelCase         =					UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      , output_loading_info=__lowerCAmelCase    )
      model_accelerate.to(__lowerCAmelCase    )
      model_accelerate.eval()

      _UpperCAmelCase         =					torch.randn(
          1      , model_accelerate.config.in_channels      , model_accelerate.config.sample_size      , model_accelerate.config.sample_size      , generator=torch.manual_seed(0    )      , )
      _UpperCAmelCase         =					noise.to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor([10] * noise.shape[0]    ).to(__lowerCAmelCase    )

      _UpperCAmelCase         =					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       , _UpperCAmelCase         =					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         =					model_normal_load(__lowerCAmelCase      , __lowerCAmelCase    )["""sample"""]

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


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""    )
      model.eval()
      model.to(__lowerCAmelCase    )

      _UpperCAmelCase         =					torch.randn(
          1      , model.config.in_channels      , model.config.sample_size      , model.config.sample_size      , generator=torch.manual_seed(0    )      , )
      _UpperCAmelCase         =					noise.to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor([10] * noise.shape[0]    ).to(__lowerCAmelCase    )

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

      _UpperCAmelCase         =					output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      _UpperCAmelCase         =					torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]    )
      # fmt: on

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



class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[Any]   							=							UNetaDModel
    _snake_case   :     str   							=							'sample'


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     str=(32, 32)    ):
      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					3

      _UpperCAmelCase         =					floats_tensor((batch_size, num_channels) + sizes    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(batch_size * [10]    ).to(dtype=torch.intaa      , device=__lowerCAmelCase    )

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


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return (3, 32, 32)


    @property
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return (3, 32, 32)


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					{
          """block_out_channels""": [32, 64, 64, 64],
          """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         =					self.dummy_input
      return init_dict, inputs_dict


    @slow
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase       , _UpperCAmelCase         =					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         =					self.dummy_input
      _UpperCAmelCase         =					floats_tensor((4, 3) + (256, 256)    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					noise
      _UpperCAmelCase         =					model(**__lowerCAmelCase    )

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


    @slow
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""    )
      model.to(__lowerCAmelCase    )

      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					3
      _UpperCAmelCase         =					(256, 256)

      _UpperCAmelCase         =					torch.ones((batch_size, num_channels) + sizes    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(batch_size * [1e-4]    ).to(__lowerCAmelCase    )

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

      _UpperCAmelCase         =					output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      _UpperCAmelCase         =					torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]    )
      # fmt: on

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


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""    )
      model.to(__lowerCAmelCase    )

      _UpperCAmelCase         =					4
      _UpperCAmelCase         =					3
      _UpperCAmelCase         =					(32, 32)

      _UpperCAmelCase         =					torch.ones((batch_size, num_channels) + sizes    ).to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(batch_size * [1e-4]    ).to(__lowerCAmelCase    )

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

      _UpperCAmelCase         =					output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      _UpperCAmelCase         =					torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]    )
      # fmt: on

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


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      # not required for this model
      pass
