

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       ( lowercase_			,						lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    AutoencoderKL
				__SCREAMING_SNAKE_CASE      :				Tuple     =    '''sample'''
				__SCREAMING_SNAKE_CASE      :				List[str]     =    1e-2




				@property
				def 					a							(      self ):
											snake_case_           =					4
											snake_case_           =					3
											snake_case_           =					(32, 32)

											snake_case_           =					floats_tensor((batch_size, num_channels) + sizes ).to(snake_case )

											return {"sample": image}




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




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




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




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											pass




				@unittest.skipIf(torch_device == 'mps'   ,     'Gradient checkpointing skipped on MPS' )
				def 					a							(      self ):
											# enable deterministic behavior for gradient checkpointing
											snake_case_       ,					snake_case_           =					self.prepare_init_args_and_inputs_for_common()
											snake_case_           =					self.model_class(**snake_case )
											model.to(snake_case )

											assert not model.is_gradient_checkpointing and model.training

											snake_case_           =					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()

											snake_case_           =					torch.randn_like(snake_case )
											snake_case_           =					(out - labels).mean()
											loss.backward()

											# re-instantiate the model now enabling gradient checkpointing
											snake_case_           =					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

											snake_case_           =					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()
											snake_case_           =					(out_a - labels).mean()
											loss_a.backward()

											# compare the output and parameters gradients
											self.assertTrue((loss - loss_a).abs() < 1e-5 )
											snake_case_           =					dict(model.named_parameters() )
											snake_case_           =					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 ):
											snake_case_       ,					snake_case_           =					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 )
											snake_case_           =					model(**self.dummy_input )

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




				def 					a							(      self ):
											snake_case_           =					AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' )
											snake_case_           =					model.to(snake_case )
											model.eval()

											if torch_device == "mps":
																		snake_case_           =					torch.manual_seed(0 )
											else:
																		snake_case_           =					torch.Generator(device=snake_case ).manual_seed(0 )

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

											snake_case_           =					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":
																		snake_case_           =					torch.tensor(
																		    [
																		        -4.0_0_7_8e-0_1,
																		        -3.8_3_2_3e-0_4,
																		        -1.2_6_8_1e-0_1,
																		        -1.1_4_6_2e-0_1,
																		        2.0_0_9_5e-0_1,
																		        1.0_8_9_3e-0_1,
																		        -8.8_2_4_7e-0_2,
																		        -3.0_3_6_1e-0_1,
																		        -9.8_6_4_4e-0_3,
																		    ] )
											elif torch_device == "cpu":
																		snake_case_           =					torch.tensor(
																		    [-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26] )
											else:
																		snake_case_           =					torch.tensor(
																		    [-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85] )

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







@slow
class 				lowercase       ( unittest.TestCase    ):




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											return F'''gaussian_noise_s={seed}_shape={"_".join([str(snake_case ) for s in shape] )}.npy'''




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




				def 					a							(      self   ,     snake_case=0   ,     snake_case=(4, 3, 512, 512)   ,     snake_case=False ):
											snake_case_           =					torch.floataa if fpaa else torch.floataa
											snake_case_           =					torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case   ,     snake_case ) ) ).to(snake_case ).to(snake_case )
											return image




				def 					a							(      self   ,     snake_case="CompVis/stable-diffusion-v1-4"   ,     snake_case=False ):
											snake_case_           =					'fp16' if fpaa else None
											snake_case_           =					torch.floataa if fpaa else torch.floataa

											snake_case_           =					AutoencoderKL.from_pretrained(
											    snake_case   ,     subfolder='vae'   ,     torch_dtype=snake_case   ,     revision=snake_case   ,     )
											model.to(snake_case ).eval()

											return model




				def 					a							(      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, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
				        [47, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
				        # fmt: on
				    ] )
				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model()
											snake_case_           =					self.get_sd_image(snake_case )
											snake_case_           =					self.get_generator(snake_case )

											with torch.no_grad():
																		snake_case_           =					model(snake_case   ,     generator=snake_case   ,     sample_posterior=snake_case ).sample

											assert sample.shape == image.shape

											snake_case_           =					sample[-1, -2:, -2:, :2].flatten().float().cpu()
											snake_case_           =					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, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
				        [47, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
				        # fmt: on
				    ] )
				@require_torch_gpu
				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model(fpaa=snake_case )
											snake_case_           =					self.get_sd_image(snake_case   ,     fpaa=snake_case )
											snake_case_           =					self.get_generator(snake_case )

											with torch.no_grad():
																		snake_case_           =					model(snake_case   ,     generator=snake_case   ,     sample_posterior=snake_case ).sample

											assert sample.shape == image.shape

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

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




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
				        [47, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
				        # fmt: on
				    ] )
				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model()
											snake_case_           =					self.get_sd_image(snake_case )

											with torch.no_grad():
																		snake_case_           =					model(snake_case ).sample

											assert sample.shape == image.shape

											snake_case_           =					sample[-1, -2:, -2:, :2].flatten().float().cpu()
											snake_case_           =					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, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
				        [37, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
				        # fmt: on
				    ] )
				@require_torch_gpu
				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model()
											snake_case_           =					self.get_sd_image(snake_case   ,     shape=(3, 4, 64, 64) )

											with torch.no_grad():
																		snake_case_           =					model.decode(snake_case ).sample

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

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

											assert torch_all_close(snake_case   ,     snake_case   ,     atol=1e-3 )




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
				        [16, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
				        # fmt: on
				    ] )
				@require_torch_gpu
				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model(fpaa=snake_case )
											snake_case_           =					self.get_sd_image(snake_case   ,     shape=(3, 4, 64, 64)   ,     fpaa=snake_case )

											with torch.no_grad():
																		snake_case_           =					model.decode(snake_case ).sample

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

											snake_case_           =					sample[-1, -2:, :2, -2:].flatten().float().cpu()
											snake_case_           =					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 					a							(      self   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model(fpaa=snake_case )
											snake_case_           =					self.get_sd_image(snake_case   ,     shape=(3, 4, 64, 64)   ,     fpaa=snake_case )

											with torch.no_grad():
																		snake_case_           =					model.decode(snake_case ).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		snake_case_           =					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 					a							(      self   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model()
											snake_case_           =					self.get_sd_image(snake_case   ,     shape=(3, 4, 64, 64) )

											with torch.no_grad():
																		snake_case_           =					model.decode(snake_case ).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		snake_case_           =					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, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
				        [47, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
				        # fmt: on
				    ] )
				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.get_sd_vae_model()
											snake_case_           =					self.get_sd_image(snake_case )
											snake_case_           =					self.get_generator(snake_case )

											with torch.no_grad():
																		snake_case_           =					model.encode(snake_case ).latent_dist
																		snake_case_           =					dist.sample(generator=snake_case )

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

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

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







def       __lowerCamelCase			(				UpperCamelCase__ = 50000000   ):







							'''simple docstring'''






							snake_case_           =					set()
							snake_case_           =					int((limit - 24) ** (1 / 2)   )

							snake_case_           =					set(range(3     ,							prime_square_limit + 1     ,							2   )   )
							primes.add(2   )
							for p in range(3     ,							prime_square_limit + 1     ,							2   ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p     ,							prime_square_limit + 1     ,							UpperCamelCase__   )   )   )

							for primea in primes:
														snake_case_           =					primea * primea
														for primea in primes:
																					snake_case_           =					primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												snake_case_           =					primea * primea * primea * primea
																												snake_case_           =					square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(UpperCamelCase__   )

							return len(UpperCamelCase__   )


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




