


'''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     lowercase__  (						_snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      int      	=  AutoencoderKL
							A_							:      Optional[Any]      	=  """sample"""
							A_							:      Dict      	=  1E-2






							@property
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      4
											_SCREAMING_SNAKE_CASE :       Any     		=      3
											_SCREAMING_SNAKE_CASE :       List[str]     		=      (32, 32)

											_SCREAMING_SNAKE_CASE :       Any     		=      floats_tensor((batch_size, num_channels) + sizes  ).to(__snake_case  )

											return {"sample": image}






							@property
							def UpperCAmelCase_	(    self  ):
											return (3, 32, 32)






							@property
							def UpperCAmelCase_	(    self  ):
											return (3, 32, 32)






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       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,
											}
											_SCREAMING_SNAKE_CASE :       str     		=      self.dummy_input
											return init_dict, inputs_dict






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skipIf(torch_device == """mps"""    ,					"""Gradient checkpointing skipped on MPS"""  )
							def UpperCAmelCase_	(    self  ):
											# enable deterministic behavior for gradient checkpointing
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       str     		=      self.prepare_init_args_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE :       str     		=      self.model_class(**__snake_case  )
											model.to(__snake_case  )

											assert not model.is_gradient_checkpointing and model.training

											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(**__snake_case  ).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()

											_SCREAMING_SNAKE_CASE :       List[str]     		=      torch.randn_like(__snake_case  )
											_SCREAMING_SNAKE_CASE :       str     		=      (out - labels).mean()
											loss.backward()

											# re-instantiate the model now enabling gradient checkpointing
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.model_class(**__snake_case  )
											# clone model
											model_a.load_state_dict(model.state_dict()  )
											model_a.to(__snake_case  )
											model_a.enable_gradient_checkpointing()

											assert model_a.is_gradient_checkpointing and model_a.training

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      model_a(**__snake_case  ).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()
											_SCREAMING_SNAKE_CASE :       Any     		=      (out_a - labels).mean()
											loss_a.backward()

											# compare the output and parameters gradients
											self.assertTrue((loss - loss_a).abs() < 1e-5  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      dict(model.named_parameters()  )
											_SCREAMING_SNAKE_CASE :       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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""    ,					output_loading_info=__snake_case  )
											self.assertIsNotNone(__snake_case  )
											self.assertEqual(len(loading_info["""missing_keys"""]  )    ,					0  )

											model.to(__snake_case  )
											_SCREAMING_SNAKE_CASE :       str     		=      model(**self.dummy_input  )

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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      model.to(__snake_case  )
											model.eval()

											if torch_device == "mps":
															_SCREAMING_SNAKE_CASE :       int     		=      torch.manual_seed(0  )
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.Generator(device=__snake_case  ).manual_seed(0  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.randn(
											    1    ,					model.config.in_channels    ,					model.config.sample_size    ,					model.config.sample_size    ,					generator=torch.manual_seed(0  )    ,					)
											_SCREAMING_SNAKE_CASE :       int     		=      image.to(__snake_case  )
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       str     		=      model(__snake_case    ,					sample_posterior=__snake_case    ,					generator=__snake_case  ).sample

											_SCREAMING_SNAKE_CASE :       Tuple     		=      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":
															_SCREAMING_SNAKE_CASE :       Any     		=      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":
															_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(
															    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]  )
											else:
															_SCREAMING_SNAKE_CASE :       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(__snake_case    ,					__snake_case    ,					rtol=1e-2  )  )





@slow
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											return f"""gaussian_noise_s={seed}_shape={"_".join([str(__snake_case  ) for s in shape]  )}.npy"""






							def UpperCAmelCase_	(    self  ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_	(    self    ,					__snake_case=0    ,					__snake_case=(4, 3, 512, 512)    ,					__snake_case=False  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.floataa if fpaa else torch.floataa
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.from_numpy(load_hf_numpy(self.get_file_format(__snake_case    ,					__snake_case  )  )  ).to(__snake_case  ).to(__snake_case  )
											return image






							def UpperCAmelCase_	(    self    ,					__snake_case="CompVis/stable-diffusion-v1-4"    ,					__snake_case=False  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      """fp16""" if fpaa else None
											_SCREAMING_SNAKE_CASE :       str     		=      torch.floataa if fpaa else torch.floataa

											_SCREAMING_SNAKE_CASE :       Tuple     		=      AutoencoderKL.from_pretrained(
											    __snake_case    ,					subfolder="""vae"""    ,					torch_dtype=__snake_case    ,					revision=__snake_case    ,					)
											model.to(__snake_case  ).eval()

											return model






							def UpperCAmelCase_	(    self    ,					__snake_case=0  ):
											if torch_device == "mps":
															return torch.manual_seed(__snake_case  )
											return torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )






							@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    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_sd_vae_model()
											_SCREAMING_SNAKE_CASE :       Any     		=      self.get_sd_image(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.get_generator(__snake_case  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(__snake_case    ,					generator=__snake_case    ,					sample_posterior=__snake_case  ).sample

											assert sample.shape == image.shape

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

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_sd_vae_model(fpaa=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_sd_image(__snake_case    ,					fpaa=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.get_generator(__snake_case  )

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

											assert sample.shape == image.shape

											_SCREAMING_SNAKE_CASE :       List[str]     		=      sample[-1, -2:, :2, -2:].flatten().float().cpu()
											_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(__snake_case  )

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_sd_vae_model()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.get_sd_image(__snake_case  )

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

											assert sample.shape == image.shape

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

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_sd_vae_model()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.get_sd_image(__snake_case    ,					shape=(3, 4, 64, 64)  )

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

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

											_SCREAMING_SNAKE_CASE :       List[str]     		=      sample[-1, -2:, :2, -2:].flatten().cpu()
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.tensor(__snake_case  )

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      self.get_sd_vae_model(fpaa=__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.get_sd_image(__snake_case    ,					shape=(3, 4, 64, 64)    ,					fpaa=__snake_case  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Dict     		=      model.decode(__snake_case  ).sample

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

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

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_sd_vae_model(fpaa=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.get_sd_image(__snake_case    ,					shape=(3, 4, 64, 64)    ,					fpaa=__snake_case  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model.decode(__snake_case  ).sample

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

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

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_sd_vae_model()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_sd_image(__snake_case    ,					shape=(3, 4, 64, 64)  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Tuple     		=      model.decode(__snake_case  ).sample

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

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

											assert torch_all_close(__snake_case    ,					__snake_case    ,					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    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_sd_vae_model()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_sd_image(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.get_generator(__snake_case  )

											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model.encode(__snake_case  ).latent_dist
															_SCREAMING_SNAKE_CASE :       str     		=      dist.sample(generator=__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       Dict     		=      sample[0, -1, -3:, -3:].flatten().cpu()
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.tensor(__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      3e-3 if torch_device != """mps""" else 1e-2
											assert torch_all_close(__snake_case    ,					__snake_case    ,					atol=__snake_case  )








'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
				print(F"{solution() = }")




