



'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					'upernet'







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       str=None	,_UpperCAmelCase					:       List[Any]=512	,_UpperCAmelCase					:       int=0.02	,_UpperCAmelCase					:       int=[1, 2, 3, 6]	,_UpperCAmelCase					:       int=True	,_UpperCAmelCase					:       Dict=0.4	,_UpperCAmelCase					:       Optional[int]=384	,_UpperCAmelCase					:       Optional[Any]=256	,_UpperCAmelCase					:       List[Any]=1	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       Tuple=255	,**_UpperCAmelCase					:       int	,):
									super().__init__(**_UpperCAmelCase		)

									if backbone_config is None:
											logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
											_a     :      List[str]        =							CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
									elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      List[Any]        =							backbone_config.get('model_type'		)
											_a     :      List[str]        =							CONFIG_MAPPING[backbone_model_type]
											_a     :      Optional[int]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Optional[int]        =							backbone_config
									_a     :      Union[str, Any]        =							hidden_size
									_a     :      str        =							initializer_range
									_a     :      Any        =							pool_scales
									_a     :      str        =							use_auxiliary_head
									_a     :      Tuple        =							auxiliary_loss_weight
									_a     :      Optional[Any]        =							auxiliary_in_channels
									_a     :      Union[str, Any]        =							auxiliary_channels
									_a     :      List[str]        =							auxiliary_num_convs
									_a     :      List[str]        =							auxiliary_concat_input
									_a     :      Any        =							loss_ignore_index







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      int        =							copy.deepcopy(self.__dict__		)
									_a     :      List[Any]        =							self.backbone_config.to_dict()
									_a     :      Dict        =							self.__class__.model_type
									return output





"""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
