





'''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      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	AutoencoderKL
 UpperCAmelCase__									=	"""sample"""
 UpperCAmelCase__									=	1E-2
 @property
 def   A_			(   self						:   Any			)    ->			Dict:
     lowerCamelCase__					:   int      				=	4
     lowerCamelCase__					:   int      				=	3
     lowerCamelCase__					:   int      				=	(32, 32)

     lowerCamelCase__					:   Union[str, Any]      				=	floats_tensor((batch_size, num_channels) + sizes			).to(__a			)

     return {"sample": image}
 @property
 def   A_			(   self						:   Any			)    ->			Union[str, Any]:
     return (3, 32, 32)
 @property
 def   A_			(   self						:   int			)    ->			Optional[int]:
     return (3, 32, 32)
 def   A_			(   self						:   Any			)    ->			Any:
     lowerCamelCase__					:   List[Any]      				=	{
         '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__					:   str      				=	self.dummy_input
     return init_dict, inputs_dict
 def   A_			(   self						:   List[Any]			)    ->			Union[str, Any]:
     pass
 def   A_			(   self						:   int			)    ->			str:
     pass
 @unittest.skipIf(torch_device == 'mps'    ,  'Gradient checkpointing skipped on MPS'			)
 def   A_			(   self						:   List[Any]			)    ->			List[Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   List[Any]      				=	self.prepare_init_args_and_inputs_for_common()
     lowerCamelCase__					:   int      				=	self.model_class(**__a			)
     model.to(__a			)

     assert not model.is_gradient_checkpointing and model.training

     lowerCamelCase__					:   List[str]      				=	model(**__a			).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__					:   List[Any]      				=	torch.randn_like(__a			)
     lowerCamelCase__					:   Union[str, Any]      				=	(out - labels).mean()
     loss.backward()

     # re-instantiate the model now enabling gradient checkpointing
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_class(**__a			)
     # clone model
     model_a.load_state_dict(model.state_dict()			)
     model_a.to(__a			)
     model_a.enable_gradient_checkpointing()

     assert model_a.is_gradient_checkpointing and model_a.training

     lowerCamelCase__					:   Any      				=	model_a(**__a			).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__					:   Any      				=	(out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1e-5			)
     lowerCamelCase__					:   Optional[int]      				=	dict(model.named_parameters()			)
     lowerCamelCase__					:   List[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   A_			(   self						:   Optional[Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'    ,  output_loading_info=__a			)
     self.assertIsNotNone(__a			)
     self.assertEqual(len(loading_info['missing_keys']			)    ,  0			)

     model.to(__a			)
     lowerCamelCase__					:   Optional[Any]      				=	model(**self.dummy_input			)

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







 def   A_			(   self						:   List[str]			)    ->			Optional[int]:
     lowerCamelCase__					:   Any      				=	AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'			)
     lowerCamelCase__					:   int      				=	model.to(__a			)
     model.eval()

     if torch_device == "mps":
         lowerCamelCase__					:   Optional[int]      				=	torch.manual_seed(0			)
     else:
         lowerCamelCase__					:   List[str]      				=	torch.Generator(device=__a			).manual_seed(0			)

     lowerCamelCase__					:   Tuple      				=	torch.randn(
         1    ,  model.config.in_channels    ,  model.config.sample_size    ,  model.config.sample_size    ,  generator=torch.manual_seed(0			)    ,  )
     lowerCamelCase__					:   Any      				=	image.to(__a			)
     with torch.no_grad():
         lowerCamelCase__					:   List[str]      				=	model(__a    ,  sample_posterior=__a    ,  generator=__a			).sample

     lowerCamelCase__					:   Union[str, Any]      				=	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__					:   Tuple      				=	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__					:   int      				=	torch.tensor(
             [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]			)
     else:
         lowerCamelCase__					:   Optional[int]      				=	torch.tensor(
             [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]			)

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


@slow
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   List[str]			)    ->			Optional[Any]:
     return F"""gaussian_noise_s={seed}_shape={"_".join([str(__a			) for s in shape]			)}.npy"""
 def   A_			(   self						:   Dict			)    ->			Optional[int]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]=0    ,  UpperCAmelCase						:   Dict=(4, 3, 512, 512)    ,  UpperCAmelCase						:   Optional[int]=False			)    ->			str:
     lowerCamelCase__					:   Tuple      				=	torch.floataa if fpaa else torch.floataa
     lowerCamelCase__					:   Tuple      				=	torch.from_numpy(load_hf_numpy(self.get_file_format(__a    ,  __a			)			)			).to(__a			).to(__a			)
     return image
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]="CompVis/stable-diffusion-v1-4"    ,  UpperCAmelCase						:   List[Any]=False			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Dict      				=	'fp16' if fpaa else None
     lowerCamelCase__					:   List[str]      				=	torch.floataa if fpaa else torch.floataa

     lowerCamelCase__					:   Optional[Any]      				=	AutoencoderKL.from_pretrained(
         __a    ,  subfolder='vae'    ,  torch_dtype=__a    ,  revision=__a    ,  )
     model.to(__a			).eval()

     return model
 def   A_			(   self						:   List[Any]    ,  UpperCAmelCase						:   Dict=0			)    ->			Tuple:
     if torch_device == "mps":
         return torch.manual_seed(__a			)
     return torch.Generator(device=__a			).manual_seed(__a			)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
         [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
         # fmt: on
     ]			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Tuple			)    ->			Any:
     lowerCamelCase__					:   Optional[int]      				=	self.get_sd_vae_model()
     lowerCamelCase__					:   List[Any]      				=	self.get_sd_image(__a			)
     lowerCamelCase__					:   Optional[int]      				=	self.get_generator(__a			)

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

     assert sample.shape == image.shape

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

     assert torch_all_close(__a    ,  __a    ,  atol=3e-3			)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
         [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
         # fmt: on
     ]			)
 @require_torch_gpu
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   int			)    ->			List[Any]:
     lowerCamelCase__					:   Any      				=	self.get_sd_vae_model(fpaa=__a			)
     lowerCamelCase__					:   str      				=	self.get_sd_image(__a    ,  fpaa=__a			)
     lowerCamelCase__					:   List[Any]      				=	self.get_generator(__a			)

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

     assert sample.shape == image.shape

     lowerCamelCase__					:   Tuple      				=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
     lowerCamelCase__					:   Tuple      				=	torch.tensor(__a			)

     assert torch_all_close(__a    ,  __a    ,  atol=1e-2			)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
         [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
         # fmt: on
     ]			)
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     lowerCamelCase__					:   int      				=	self.get_sd_vae_model()
     lowerCamelCase__					:   Union[str, Any]      				=	self.get_sd_image(__a			)

     with torch.no_grad():
         lowerCamelCase__					:   Tuple      				=	model(__a			).sample

     assert sample.shape == image.shape

     lowerCamelCase__					:   List[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(__a    ,  __a    ,  atol=3e-3			)
 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
         [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
         # fmt: on
     ]			)
 @require_torch_gpu
 def   A_			(   self						:   int    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Any      				=	self.get_sd_vae_model()
     lowerCamelCase__					:   List[str]      				=	self.get_sd_image(__a    ,  shape=(3, 4, 64, 64)			)

     with torch.no_grad():
         lowerCamelCase__					:   Any      				=	model.decode(__a			).sample

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

     lowerCamelCase__					:   List[str]      				=	sample[-1, -2:, :2, -2:].flatten().cpu()
     lowerCamelCase__					:   Union[str, Any]      				=	torch.tensor(__a			)

     assert torch_all_close(__a    ,  __a    ,  atol=1e-3			)
 @parameterized.expand(
     [
         # fmt: off
         [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
         [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
         # fmt: on
     ]			)
 @require_torch_gpu
 def   A_			(   self						:   Optional[int]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   str			)    ->			Optional[Any]:
     lowerCamelCase__					:   Dict      				=	self.get_sd_vae_model(fpaa=__a			)
     lowerCamelCase__					:   Union[str, Any]      				=	self.get_sd_image(__a    ,  shape=(3, 4, 64, 64)    ,  fpaa=__a			)

     with torch.no_grad():
         lowerCamelCase__					:   Union[str, Any]      				=	model.decode(__a			).sample

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

     lowerCamelCase__					:   Dict      				=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
     lowerCamelCase__					:   List[Any]      				=	torch.tensor(__a			)

     assert torch_all_close(__a    ,  __a    ,  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   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   str			)    ->			str:
     lowerCamelCase__					:   List[str]      				=	self.get_sd_vae_model(fpaa=__a			)
     lowerCamelCase__					:   Tuple      				=	self.get_sd_image(__a    ,  shape=(3, 4, 64, 64)    ,  fpaa=__a			)

     with torch.no_grad():
         lowerCamelCase__					:   str      				=	model.decode(__a			).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         lowerCamelCase__					:   Any      				=	model.decode(__a			).sample

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

     assert torch_all_close(__a    ,  __a    ,  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   A_			(   self						:   str    ,  UpperCAmelCase						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Tuple      				=	self.get_sd_vae_model()
     lowerCamelCase__					:   Optional[int]      				=	self.get_sd_image(__a    ,  shape=(3, 4, 64, 64)			)

     with torch.no_grad():
         lowerCamelCase__					:   List[Any]      				=	model.decode(__a			).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         lowerCamelCase__					:   List[str]      				=	model.decode(__a			).sample

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

     assert torch_all_close(__a    ,  __a    ,  atol=1e-2			)







 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
         [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
         # fmt: on
     ]			)
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Tuple			)    ->			List[Any]:
     lowerCamelCase__					:   Optional[int]      				=	self.get_sd_vae_model()
     lowerCamelCase__					:   Tuple      				=	self.get_sd_image(__a			)
     lowerCamelCase__					:   Any      				=	self.get_generator(__a			)

     with torch.no_grad():
         lowerCamelCase__					:   Dict      				=	model.encode(__a			).latent_dist
         lowerCamelCase__					:   Optional[Any]      				=	dist.sample(generator=__a			)

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

     lowerCamelCase__					:   Tuple      				=	sample[0, -1, -3:, -3:].flatten().cpu()
     lowerCamelCase__					:   Dict      				=	torch.tensor(__a			)

     lowerCamelCase__					:   str      				=	3e-3 if torch_device != 'mps' else 1e-2
     assert torch_all_close(__a    ,  __a    ,  atol=__a			)












import enum
import shutil
import sys


_UpperCAmelCase     ,_UpperCAmelCase       : List[Any]			     =       shutil.get_terminal_size()

_UpperCAmelCase       : Union[str, Any]			     =       {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}

class      lowerCAmelCase							(      enum.Enum    ):
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  int:
    sys.stdout.write(str(_UpperCAmelCase				) + end				)
    sys.stdout.flush()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  Union[str, Any]:
    forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    forceWrite('\r'				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""				)


def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    forceWrite(' ' * TERMINAL_WIDTH				)
    reset_cursor()


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    reset_cursor()
    forceWrite('-' * TERMINAL_WIDTH				)





