





'''simple docstring'''






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class 		UpperCAmelCase (					lowercase_ ,  lowercase_ ,  unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_											=					AutoencoderKL
  lowerCAmelCase_											=					"""sample"""
  lowerCAmelCase_											=					1e-2


  @property
  def 	UpperCamelCase__   (					self     :       Dict	)  ->    Tuple:
    _UpperCamelCase							      =4
    _UpperCamelCase							      =3
    _UpperCamelCase							      =(32, 32)

    _UpperCamelCase							      =floats_tensor((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)

    return {"sample": image}


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


  @property
  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Optional[Any]:
    return (3, 32, 32)


  def 	UpperCamelCase__   (					self     :       Dict	)  ->    str:
    _UpperCamelCase							      ={
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 4,
    }
    _UpperCamelCase							      =self.dummy_input
    return init_dict, inputs_dict


  def 	UpperCamelCase__   (					self     :       int	)  ->    Dict:
    pass


  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    int:
    pass


  @unittest.skipIf(torch_device == '''mps'''				,						'''Gradient checkpointing skipped on MPS'''	)
  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Tuple:
    _UpperCamelCase      ,  _UpperCamelCase							      =self.prepare_init_args_and_inputs_for_common()
    _UpperCamelCase							      =self.model_class(**UpperCamelCase__	)
    model.to(UpperCamelCase__	)

    assert not model.is_gradient_checkpointing and model.training

    _UpperCamelCase							      =model(**UpperCamelCase__	).sample
    # run the backwards pass on the model. For backwards pass, for simplicity purpose,
    # we won't calculate the loss and rather backprop on out.sum()
    model.zero_grad()

    _UpperCamelCase							      =torch.randn_like(UpperCamelCase__	)
    _UpperCamelCase							      =(out - labels).mean()
    loss.backward()

    # re-instantiate the model now enabling gradient checkpointing
    _UpperCamelCase							      =self.model_class(**UpperCamelCase__	)
    # clone model
    model_a.load_state_dict(model.state_dict()	)
    model_a.to(UpperCamelCase__	)
    model_a.enable_gradient_checkpointing()

    assert model_a.is_gradient_checkpointing and model_a.training

    _UpperCamelCase							      =model_a(**UpperCamelCase__	).sample
    # run the backwards pass on the model. For backwards pass, for simplicity purpose,
    # we won't calculate the loss and rather backprop on out.sum()
    model_a.zero_grad()
    _UpperCamelCase							      =(out_a - labels).mean()
    loss_a.backward()

    # compare the output and parameters gradients
    self.assertTrue((loss - loss_a).abs() < 1E-5	)
    _UpperCamelCase							      =dict(model.named_parameters()	)
    _UpperCamelCase							      =dict(model_a.named_parameters()	)
    for name, param in named_params.items():
      self.assertTrue(torch_all_close(param.grad.data				,						named_params_a[name].grad.data				,						atol=5E-5	)	)


  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Tuple:
    _UpperCamelCase      ,  _UpperCamelCase							      =AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				,						output_loading_info=UpperCamelCase__	)
    self.assertIsNotNone(UpperCamelCase__	)
    self.assertEqual(len(loading_info['''missing_keys''']	)				,						0	)

    model.to(UpperCamelCase__	)
    _UpperCamelCase							      =model(**self.dummy_input	)

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




  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Any:
    _UpperCamelCase							      =AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''	)
    _UpperCamelCase							      =model.to(UpperCamelCase__	)
    model.eval()

    if torch_device == "mps":
      _UpperCamelCase							      =torch.manual_seed(0	)
    else:
      _UpperCamelCase							      =torch.Generator(device=UpperCamelCase__	).manual_seed(0	)

    _UpperCamelCase							      =torch.randn(
        1				,						model.config.in_channels				,						model.config.sample_size				,						model.config.sample_size				,						generator=torch.manual_seed(0	)				,						)
    _UpperCamelCase							      =image.to(UpperCamelCase__	)
    with torch.no_grad():
      _UpperCamelCase							      =model(UpperCamelCase__				,						sample_posterior=UpperCamelCase__				,						generator=UpperCamelCase__	).sample

    _UpperCamelCase							      =output[0, -1, -3:, -3:].flatten().cpu()

    # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
    # the expected output slices are not the same for CPU and GPU.
    if torch_device == "mps":
      _UpperCamelCase							      =torch.tensor(
          [
              -4.0078E-01,
              -3.8323E-04,
              -1.2681E-01,
              -1.1462E-01,
              2.0095E-01,
              1.0893E-01,
              -8.8247E-02,
              -3.0361E-01,
              -9.8644E-03,
          ]	)
    elif torch_device == "cpu":
      _UpperCamelCase							      =torch.tensor(
          [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]	)
    else:
      _UpperCamelCase							      =torch.tensor(
          [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]	)

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







@slow
class 		UpperCAmelCase (					unittest.TestCase):


  """simple docstring"""







  def 	UpperCamelCase__   (					self     :       Dict				,						UpperCamelCase__     :       int				,						UpperCamelCase__     :       Dict	)  ->    Optional[Any]:
    return F'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase__	) for s in shape]	)}.npy'''


  def 	UpperCamelCase__   (					self     :       Optional[int]	)  ->    List[str]:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


  def 	UpperCamelCase__   (					self     :       Optional[Any]				,						UpperCamelCase__     :       Any=0				,						UpperCamelCase__     :       str=(4, 3, 512, 512)				,						UpperCamelCase__     :       List[str]=False	)  ->    int:
    _UpperCamelCase							      =torch.floataa if fpaa else torch.floataa
    _UpperCamelCase							      =torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCamelCase__				,						UpperCamelCase__	)	)	).to(UpperCamelCase__	).to(UpperCamelCase__	)
    return image


  def 	UpperCamelCase__   (					self     :       Union[str, Any]				,						UpperCamelCase__     :       Any="CompVis/stable-diffusion-v1-4"				,						UpperCamelCase__     :       Union[str, Any]=False	)  ->    Any:
    _UpperCamelCase							      ='''fp16''' if fpaa else None
    _UpperCamelCase							      =torch.floataa if fpaa else torch.floataa

    _UpperCamelCase							      =AutoencoderKL.from_pretrained(
        UpperCamelCase__				,						subfolder='''vae'''				,						torch_dtype=UpperCamelCase__				,						revision=UpperCamelCase__				,						)
    model.to(UpperCamelCase__	).eval()

    return model


  def 	UpperCamelCase__   (					self     :       Optional[int]				,						UpperCamelCase__     :       int=0	)  ->    List[str]:






    if torch_device == "mps":
      return torch.manual_seed(UpperCamelCase__	)
    return torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)


  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
          [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
          # fmt: on
      ]	)
  def 	UpperCamelCase__   (					self     :       Any				,						UpperCamelCase__     :       List[str]				,						UpperCamelCase__     :       Union[str, Any]				,						UpperCamelCase__     :       Union[str, Any]	)  ->    Dict:
    _UpperCamelCase							      =self.get_sd_vae_model()
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__	)
    _UpperCamelCase							      =self.get_generator(UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model(UpperCamelCase__				,						generator=UpperCamelCase__				,						sample_posterior=UpperCamelCase__	).sample

    assert sample.shape == image.shape

    _UpperCamelCase							      =sample[-1, -2:, -2:, :2].flatten().float().cpu()
    _UpperCamelCase							      =torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=3E-3	)


  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
          [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def 	UpperCamelCase__   (					self     :       Union[str, Any]				,						UpperCamelCase__     :       Union[str, Any]				,						UpperCamelCase__     :       List[Any]	)  ->    Optional[int]:
    _UpperCamelCase							      =self.get_sd_vae_model(fpaa=UpperCamelCase__	)
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__				,						fpaa=UpperCamelCase__	)
    _UpperCamelCase							      =self.get_generator(UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model(UpperCamelCase__				,						generator=UpperCamelCase__				,						sample_posterior=UpperCamelCase__	).sample

    assert sample.shape == image.shape

    _UpperCamelCase							      =sample[-1, -2:, :2, -2:].flatten().float().cpu()
    _UpperCamelCase							      =torch.tensor(UpperCamelCase__	)

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=1E-2	)


  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
          [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
          # fmt: on
      ]	)
  def 	UpperCamelCase__   (					self     :       Tuple				,						UpperCamelCase__     :       Dict				,						UpperCamelCase__     :       str				,						UpperCamelCase__     :       Optional[int]	)  ->    Union[str, Any]:
    _UpperCamelCase							      =self.get_sd_vae_model()
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model(UpperCamelCase__	).sample

    assert sample.shape == image.shape

    _UpperCamelCase							      =sample[-1, -2:, -2:, :2].flatten().float().cpu()
    _UpperCamelCase							      =torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice	)

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=3E-3	)


  @parameterized.expand(
      [
          # fmt: off
          [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
          [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def 	UpperCamelCase__   (					self     :       str				,						UpperCamelCase__     :       Tuple				,						UpperCamelCase__     :       Union[str, Any]	)  ->    List[str]:
    _UpperCamelCase							      =self.get_sd_vae_model()
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__				,						shape=(3, 4, 64, 64)	)

    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    assert list(sample.shape	) == [3, 3, 512, 512]

    _UpperCamelCase							      =sample[-1, -2:, :2, -2:].flatten().cpu()
    _UpperCamelCase							      =torch.tensor(UpperCamelCase__	)

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=1E-3	)


  @parameterized.expand(
      [
          # fmt: off
          [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
          [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def 	UpperCamelCase__   (					self     :       List[Any]				,						UpperCamelCase__     :       List[str]				,						UpperCamelCase__     :       str	)  ->    str:
    _UpperCamelCase							      =self.get_sd_vae_model(fpaa=UpperCamelCase__	)
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__				,						shape=(3, 4, 64, 64)				,						fpaa=UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    assert list(sample.shape	) == [3, 3, 512, 512]

    _UpperCamelCase							      =sample[-1, -2:, :2, -2:].flatten().float().cpu()
    _UpperCamelCase							      =torch.tensor(UpperCamelCase__	)

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=5E-3	)


  @parameterized.expand([(13,), (16,), (27,)]	)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()				,						reason='''xformers is not required when using PyTorch 2.0.'''	)
  def 	UpperCamelCase__   (					self     :       Tuple				,						UpperCamelCase__     :       Any	)  ->    Any:
    _UpperCamelCase							      =self.get_sd_vae_model(fpaa=UpperCamelCase__	)
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__				,						shape=(3, 4, 64, 64)				,						fpaa=UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    model.enable_xformers_memory_efficient_attention()
    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    assert list(sample.shape	) == [3, 3, 512, 512]

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=1E-1	)


  @parameterized.expand([(13,), (16,), (37,)]	)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()				,						reason='''xformers is not required when using PyTorch 2.0.'''	)
  def 	UpperCamelCase__   (					self     :       int				,						UpperCamelCase__     :       Optional[int]	)  ->    int:
    _UpperCamelCase							      =self.get_sd_vae_model()
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__				,						shape=(3, 4, 64, 64)	)

    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    model.enable_xformers_memory_efficient_attention()
    with torch.no_grad():
      _UpperCamelCase							      =model.decode(UpperCamelCase__	).sample

    assert list(sample.shape	) == [3, 3, 512, 512]

    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=1E-2	)




  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
          [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
          # fmt: on
      ]	)
  def 	UpperCamelCase__   (					self     :       str				,						UpperCamelCase__     :       Union[str, Any]				,						UpperCamelCase__     :       Tuple	)  ->    List[str]:
    _UpperCamelCase							      =self.get_sd_vae_model()
    _UpperCamelCase							      =self.get_sd_image(UpperCamelCase__	)
    _UpperCamelCase							      =self.get_generator(UpperCamelCase__	)

    with torch.no_grad():
      _UpperCamelCase							      =model.encode(UpperCamelCase__	).latent_dist
      _UpperCamelCase							      =dist.sample(generator=UpperCamelCase__	)

    assert list(sample.shape	) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

    _UpperCamelCase							      =sample[0, -1, -3:, -3:].flatten().cpu()
    _UpperCamelCase							      =torch.tensor(UpperCamelCase__	)

    _UpperCamelCase							      =3E-3 if torch_device != '''mps''' else 1E-2
    assert torch_all_close(UpperCamelCase__				,						UpperCamelCase__				,						atol=UpperCamelCase__	)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=num_frames

        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=initializer_range
        _lowercase      			=layer_norm_eps
        _lowercase      			=qkv_bias

        _lowercase      			=attention_type
        _lowercase      			=drop_path_rate


