



"""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:   str								=		logging.get_logger(__name__)

enable_full_determinism()



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Optional[Any]						=						UNetaDModel
   __lowerCAmelCase  :   Tuple						=						'sample'

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	4
    UpperCAmelCase	:					Dict	=	3
    UpperCAmelCase	:					Optional[Any]	=	(32, 32)

    UpperCAmelCase	:					Optional[int]	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return (3, 32, 32)

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





    '''simple docstring'''




    return (3, 32, 32)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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	:					List[str]	=	self.dummy_input
    return init_dict, inputs_dict



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						UNetaDModel
   __lowerCAmelCase  :   List[str]						=						'sample'

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	4
    UpperCAmelCase	:					str	=	4
    UpperCAmelCase	:					Any	=	(32, 32)

    UpperCAmelCase	:					int	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return (4, 32, 32)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return (4, 32, 32)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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	:					Tuple	=	self.dummy_input
    return init_dict, inputs_dict

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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

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

    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    model_accelerate.to(_SCREAMING_SNAKE_CASE						)
    model_accelerate.eval()

    UpperCAmelCase	:					Union[str, Any]	=	torch.randn(
        1      ,     model_accelerate.config.in_channels      ,     model_accelerate.config.sample_size      ,     model_accelerate.config.sample_size      ,     generator=torch.manual_seed(0						)      ,     )
    UpperCAmelCase	:					Dict	=	noise.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	torch.tensor([10] * noise.shape[0]						).to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					int	=	model_accelerate(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)["""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	:					Union[str, Any]	=	UNetaDModel.from_pretrained(
        """fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE      ,     low_cpu_mem_usage=_SCREAMING_SNAKE_CASE						)
    model_normal_load.to(_SCREAMING_SNAKE_CASE						)
    model_normal_load.eval()
    UpperCAmelCase	:					int	=	model_normal_load(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)["""sample"""]

    assert torch_all_close(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-3						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
    model.eval()
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	torch.randn(
        1      ,     model.config.in_channels      ,     model.config.sample_size      ,     model.config.sample_size      ,     generator=torch.manual_seed(0						)      ,     )
    UpperCAmelCase	:					Optional[Any]	=	noise.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	torch.tensor([10] * noise.shape[0]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					List[Any]	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Tuple	=	output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					List[Any]	=	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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-3						)						)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						UNetaDModel
   __lowerCAmelCase  :   List[str]						=						'sample'

   @property
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=(32, 32)						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	4
    UpperCAmelCase	:					Tuple	=	3

    UpperCAmelCase	:					List[str]	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	torch.tensor(batch_size * [10]						).to(dtype=torch.intaa      ,     device=_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return (3, 32, 32)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return (3, 32, 32)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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	:					Optional[Any]	=	self.dummy_input
    return init_dict, inputs_dict

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)
    self.assertEqual(len(loading_info["""missing_keys"""]						)      ,     0						)

    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	self.dummy_input
    UpperCAmelCase	:					int	=	floats_tensor((4, 3) + (256, 256)						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	noise
    UpperCAmelCase	:					Optional[int]	=	model(**_SCREAMING_SNAKE_CASE						)

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

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	4
    UpperCAmelCase	:					Optional[Any]	=	3
    UpperCAmelCase	:					List[Any]	=	(256, 256)

    UpperCAmelCase	:					int	=	torch.ones((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	torch.tensor(batch_size * [1E-4]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					str	=	output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					Optional[int]	=	torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]						)
    # fmt: on

    self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-2						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	4
    UpperCAmelCase	:					Optional[int]	=	3
    UpperCAmelCase	:					Optional[Any]	=	(32, 32)

    UpperCAmelCase	:					List[Any]	=	torch.ones((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	torch.tensor(batch_size * [1E-4]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Any	=	output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					int	=	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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-2						)						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    pass







import numpy as np
def      _A       (		SCREAMING_SNAKE_CASE     :   np.array						):





  """simple docstring"""



  return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
     import doctest

     doctest.testmod()






