




'''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     A					(	_a  ,_a  ,unittest.TestCase					):
							lowercase_           =       AutoencoderKL
							lowercase_           =       'sample'
							lowercase_           =       1e-2







							@property
							def      __lowerCAmelCase    (						self :						Any      )    ->							int:


														"""simple docstring"""


														_a			  =       4
														_a			  =       3
														_a			  =       (32, 32)

														_a			  =       floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCAmelCase_      )

														return {"sample": image}







							@property
							def      __lowerCAmelCase    (						self :						str      )    ->							int:


														"""simple docstring"""


														return (3, 32, 32)







							@property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							Optional[int]:


														"""simple docstring"""


														return (3, 32, 32)







							def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						Dict      )    ->							str:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														_a  ,							_a			  =       self.prepare_init_args_and_inputs_for_common()
														_a			  =       self.model_class(**lowerCAmelCase_      )
														model.to(lowerCAmelCase_      )

														assert not model.is_gradient_checkpointing and model.training

														_a			  =       model(**lowerCAmelCase_      ).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()

														_a			  =       torch.randn_like(lowerCAmelCase_      )
														_a			  =       (out - labels).mean()
														loss.backward()

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

														assert model_a.is_gradient_checkpointing and model_a.training

														_a			  =       model_a(**lowerCAmelCase_      ).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()
														_a			  =       (out_a - labels).mean()
														loss_a.backward()

														# compare the output and parameters gradients
														self.assertTrue((loss - loss_a).abs() < 1e-5      )
														_a			  =       dict(model.named_parameters()      )
														_a			  =       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 :						Optional[int]      )    ->							Optional[int]:


														"""simple docstring"""


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

														model.to(lowerCAmelCase_      )
														_a			  =       model(**self.dummy_input      )

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







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''      )
														_a			  =       model.to(lowerCAmelCase_      )
														model.eval()

														if torch_device == "mps":
																					_a			  =       torch.manual_seed(0      )
														else:
																					_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(0      )

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

														_a			  =       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":
																					_a			  =       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":
																					_a			  =       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:
																					_a			  =       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(lowerCAmelCase_							,    lowerCAmelCase_							,    rtol=1e-2      )      )



@slow
class     A					(	unittest.TestCase					):







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						List[Any]      )    ->							Dict:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						str=0							,    lowerCAmelCase_ :						Optional[Any]=(4, 3, 5_12, 5_12)							,    lowerCAmelCase_ :						Union[str, Any]=False      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       torch.floataa if fpaa else torch.floataa
														_a			  =       torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCAmelCase_							,    lowerCAmelCase_      )      )      ).to(lowerCAmelCase_      ).to(lowerCAmelCase_      )
														return image







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Tuple="CompVis/stable-diffusion-v1-4"							,    lowerCAmelCase_ :						List[str]=False      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       '''fp16''' if fpaa else None
														_a			  =       torch.floataa if fpaa else torch.floataa

														_a			  =       AutoencoderKL.from_pretrained(
														    lowerCAmelCase_							,    subfolder='''vae'''							,    torch_dtype=lowerCAmelCase_							,    revision=lowerCAmelCase_							,    )
														model.to(lowerCAmelCase_      ).eval()

														return model







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						Union[str, Any]=0      )    ->							Optional[Any]:


														"""simple docstring"""


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







							@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      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Dict      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model()
														_a			  =       self.get_sd_image(lowerCAmelCase_      )
														_a			  =       self.get_generator(lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model(lowerCAmelCase_							,    generator=lowerCAmelCase_							,    sample_posterior=lowerCAmelCase_      ).sample

														assert sample.shape == image.shape

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

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Any      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model(fpaa=lowerCAmelCase_      )
														_a			  =       self.get_sd_image(lowerCAmelCase_							,    fpaa=lowerCAmelCase_      )
														_a			  =       self.get_generator(lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model(lowerCAmelCase_							,    generator=lowerCAmelCase_							,    sample_posterior=lowerCAmelCase_      ).sample

														assert sample.shape == image.shape

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

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						List[str]      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model()
														_a			  =       self.get_sd_image(lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model(lowerCAmelCase_      ).sample

														assert sample.shape == image.shape

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

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Any      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model()
														_a			  =       self.get_sd_image(lowerCAmelCase_							,    shape=(3, 4, 64, 64)      )

														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

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

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Any      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model(fpaa=lowerCAmelCase_      )
														_a			  =       self.get_sd_image(lowerCAmelCase_							,    shape=(3, 4, 64, 64)							,    fpaa=lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

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

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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 :						str							,    lowerCAmelCase_ :						Dict      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model(fpaa=lowerCAmelCase_      )
														_a			  =       self.get_sd_image(lowerCAmelCase_							,    shape=(3, 4, 64, 64)							,    fpaa=lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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 :						Tuple							,    lowerCAmelCase_ :						Dict      )    ->							str:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model()
														_a			  =       self.get_sd_image(lowerCAmelCase_							,    shape=(3, 4, 64, 64)      )

														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					_a			  =       model.decode(lowerCAmelCase_      ).sample

														assert list(sample.shape      ) == [3, 3, 5_12, 5_12]

														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    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      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						int      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.get_sd_vae_model()
														_a			  =       self.get_sd_image(lowerCAmelCase_      )
														_a			  =       self.get_generator(lowerCAmelCase_      )

														with torch.no_grad():
																					_a			  =       model.encode(lowerCAmelCase_      ).latent_dist
																					_a			  =       dist.sample(generator=lowerCAmelCase_      )

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

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

														_a			  =       3e-3 if torch_device != '''mps''' else 1e-2
														assert torch_all_close(lowerCAmelCase_							,    lowerCAmelCase_							,    atol=lowerCAmelCase_      )







'''simple docstring'''


def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							if n == 1 or not isinstance(UpperCamelCase				,		UpperCamelCase   ):
														return 0
							elif n == 2:
														return 1
							else:
														_a			  =       [0, 1]
														for i in range(2				,		n + 1   ):
																					sequence.append(sequence[i - 1] + sequence[i - 2]   )

														return sequence[n]
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       0
							_a			  =       2

							while digits < n:
														index += 1
														_a			  =       len(str(fibonacci(UpperCamelCase   )   )   )

							return index
def 	snake_case_					(UpperCamelCase :						int = 1000   ):





							'''simple docstring'''


							return fibonacci_digits_index(UpperCamelCase   )


if __name__ == "__main__":
				print(solution(int(str(input()).strip())))

