


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 A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Any      =AutoencoderKL
      UpperCamelCase__							:  Union[str, Any]      ='sample'
      UpperCamelCase__							:  Tuple      =1E-2






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=4
             _lowerCamelCase       :		Optional[Any]		=3
             _lowerCamelCase       :		Union[str, Any]		=(32, 32)

             _lowerCamelCase       :		List[str]		=floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )

             return {"sample": image}






      @property
      def        lowerCamelCase       (					self						:     str )					->						Optional[int]:
             """simple docstring"""




             return (3, 32, 32)






      @property
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             return (3, 32, 32)






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		={
                 'block_out_channels': [32, 64],
                 'in_channels': 3,
                 'out_channels': 3,
                 'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
                 'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
                 'latent_channels': 4,
             }
             _lowerCamelCase       :		Tuple		=self.dummy_input
             return init_dict, inputs_dict






      def        lowerCamelCase       (					self						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             pass






      @unittest.skipIf(torch_device == 'mps'			,   'Gradient checkpointing skipped on MPS' )
      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		Tuple		=self.prepare_init_args_and_inputs_for_common()
             _lowerCamelCase       :		int		=self.model_class(**lowercase_ )
             model.to(lowercase_ )

             assert not model.is_gradient_checkpointing and model.training

             _lowerCamelCase       :		Dict		=model(**lowercase_ ).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()

             _lowerCamelCase       :		str		=torch.randn_like(lowercase_ )
             _lowerCamelCase       :		Any		=(out - labels).mean()
             loss.backward()

             # re-instantiate the model now enabling gradient checkpointing
             _lowerCamelCase       :		List[Any]		=self.model_class(**lowercase_ )
             # clone model
             model_a.load_state_dict(model.state_dict() )
             model_a.to(lowercase_ )
             model_a.enable_gradient_checkpointing()

             assert model_a.is_gradient_checkpointing and model_a.training

             _lowerCamelCase       :		List[Any]		=model_a(**lowercase_ ).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()
             _lowerCamelCase       :		Optional[int]		=(out_a - labels).mean()
             loss_a.backward()

             # compare the output and parameters gradients
             self.assertTrue((loss - loss_a).abs() < 1E-5 )
             _lowerCamelCase       :		Tuple		=dict(model.named_parameters() )
             _lowerCamelCase       :		Union[str, Any]		=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        lowerCamelCase       (					self						:     Tuple )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		Any		=AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'			,   output_loading_info=lowercase_ )
             self.assertIsNotNone(lowercase_ )
             self.assertEqual(len(loading_info['missing_keys'] )			,   0 )

             model.to(lowercase_ )
             _lowerCamelCase       :		List[str]		=model(**self.dummy_input )

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






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' )
             _lowerCamelCase       :		List[str]		=model.to(lowercase_ )
             model.eval()

             if torch_device == "mps":
                    _lowerCamelCase       :		Dict		=torch.manual_seed(0 )
             else:
                    _lowerCamelCase       :		Optional[int]		=torch.Generator(device=lowercase_ ).manual_seed(0 )

             _lowerCamelCase       :		Dict		=torch.randn(
                 1			,   model.config.in_channels			,   model.config.sample_size			,   model.config.sample_size			,   generator=torch.manual_seed(0 )			,   )
             _lowerCamelCase       :		Union[str, Any]		=image.to(lowercase_ )
             with torch.no_grad():
                    _lowerCamelCase       :		List[Any]		=model(lowercase_			,   sample_posterior=lowercase_			,   generator=lowercase_ ).sample

             _lowerCamelCase       :		Optional[int]		=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":
                    _lowerCamelCase       :		List[str]		=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":
                    _lowerCamelCase       :		Any		=torch.tensor(
                        [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
             else:
                    _lowerCamelCase       :		Optional[Any]		=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(lowercase_			,   lowercase_			,   rtol=1E-2 ) )




@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     List[str]			,   lowercase_						:     int )					->						List[Any]:
             """simple docstring"""




             return F'''gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy'''






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     Any=0			,   lowercase_						:     List[str]=(4, 3, 512, 512)			,   lowercase_						:     Tuple=False )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=torch.floataa if fpaa else torch.floataa
             _lowerCamelCase       :		List[str]		=torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_			,   lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
             return image






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     Tuple="CompVis/stable-diffusion-v1-4"			,   lowercase_						:     List[str]=False )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		='fp16' if fpaa else None
             _lowerCamelCase       :		Optional[int]		=torch.floataa if fpaa else torch.floataa

             _lowerCamelCase       :		Any		=AutoencoderKL.from_pretrained(
                 lowercase_			,   subfolder='vae'			,   torch_dtype=lowercase_			,   revision=lowercase_			,   )
             model.to(lowercase_ ).eval()

             return model






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     Optional[int]=0 )					->						int:
             """simple docstring"""




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






      @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        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     Optional[Any]			,   lowercase_						:     int			,   lowercase_						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.get_sd_vae_model()
             _lowerCamelCase       :		List[str]		=self.get_sd_image(lowercase_ )
             _lowerCamelCase       :		List[str]		=self.get_generator(lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		List[Any]		=model(lowercase_			,   generator=lowercase_			,   sample_posterior=lowercase_ ).sample

             assert sample.shape == image.shape

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

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.get_sd_vae_model(fpaa=lowercase_ )
             _lowerCamelCase       :		List[Any]		=self.get_sd_image(lowercase_			,   fpaa=lowercase_ )
             _lowerCamelCase       :		Tuple		=self.get_generator(lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   generator=lowercase_			,   sample_posterior=lowercase_ ).sample

             assert sample.shape == image.shape

             _lowerCamelCase       :		Any		=sample[-1, -2:, :2, -2:].flatten().float().cpu()
             _lowerCamelCase       :		int		=torch.tensor(lowercase_ )

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     int			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=self.get_sd_vae_model()
             _lowerCamelCase       :		str		=self.get_sd_image(lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		Dict		=model(lowercase_ ).sample

             assert sample.shape == image.shape

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

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.get_sd_vae_model()
             _lowerCamelCase       :		str		=self.get_sd_image(lowercase_			,   shape=(3, 4, 64, 64) )

             with torch.no_grad():
                    _lowerCamelCase       :		Optional[Any]		=model.decode(lowercase_ ).sample

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

             _lowerCamelCase       :		Any		=sample[-1, -2:, :2, -2:].flatten().cpu()
             _lowerCamelCase       :		Dict		=torch.tensor(lowercase_ )

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     int			,   lowercase_						:     int )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.get_sd_vae_model(fpaa=lowercase_ )
             _lowerCamelCase       :		str		=self.get_sd_image(lowercase_			,   shape=(3, 4, 64, 64)			,   fpaa=lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		Optional[Any]		=model.decode(lowercase_ ).sample

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

             _lowerCamelCase       :		Any		=sample[-1, -2:, :2, -2:].flatten().float().cpu()
             _lowerCamelCase       :		str		=torch.tensor(lowercase_ )

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Dict )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.get_sd_vae_model(fpaa=lowercase_ )
             _lowerCamelCase       :		List[str]		=self.get_sd_image(lowercase_			,   shape=(3, 4, 64, 64)			,   fpaa=lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		Any		=model.decode(lowercase_ ).sample

             model.enable_xformers_memory_efficient_attention()
             with torch.no_grad():
                    _lowerCamelCase       :		List[Any]		=model.decode(lowercase_ ).sample

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

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.get_sd_vae_model()
             _lowerCamelCase       :		Any		=self.get_sd_image(lowercase_			,   shape=(3, 4, 64, 64) )

             with torch.no_grad():
                    _lowerCamelCase       :		Optional[Any]		=model.decode(lowercase_ ).sample

             model.enable_xformers_memory_efficient_attention()
             with torch.no_grad():
                    _lowerCamelCase       :		List[Any]		=model.decode(lowercase_ ).sample

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

             assert torch_all_close(lowercase_			,   lowercase_			,   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        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     int			,   lowercase_						:     Tuple )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.get_sd_vae_model()
             _lowerCamelCase       :		str		=self.get_sd_image(lowercase_ )
             _lowerCamelCase       :		Tuple		=self.get_generator(lowercase_ )

             with torch.no_grad():
                    _lowerCamelCase       :		Optional[int]		=model.encode(lowercase_ ).latent_dist
                    _lowerCamelCase       :		Optional[int]		=dist.sample(generator=lowercase_ )

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

             _lowerCamelCase       :		Optional[Any]		=sample[0, -1, -3:, -3:].flatten().cpu()
             _lowerCamelCase       :		Union[str, Any]		=torch.tensor(lowercase_ )

             _lowerCamelCase       :		List[str]		=3E-3 if torch_device != 'mps' else 1E-2
             assert torch_all_close(lowercase_			,   lowercase_			,   atol=lowercase_ )






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

lowerCamelCase											=       'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str=None     ):
       '''simple docstring'''





       require_version(deps[pkg]		,			SCREAMING_SNAKE_CASE__     )


