'''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     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       AutoencoderKL
			UpperCamelCase__				=       '''sample'''
			UpperCamelCase__				=       1e-2







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Tuple										=       4
										lowercase_					:			List[Any]										=       3
										lowercase_					:			List[Any]										=       (32, 32)

										lowercase_					:			Dict										=       floats_tensor((batch_size, num_channels) + sizes    ).to(lowercase_    )

										return {"sample": image}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										return (3, 32, 32)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										return (3, 32, 32)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Optional[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,
										}
										lowercase_					:			int										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										pass







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										pass







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

										assert not model.is_gradient_checkpointing and model.training

										lowercase_					:			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()

										lowercase_					:			Union[str, Any]										=       torch.randn_like(lowercase_    )
										lowercase_					:			Union[str, Any]										=       (out - labels).mean()
										loss.backward()

										# re-instantiate the model now enabling gradient checkpointing
										lowercase_					:			Tuple										=       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

										lowercase_					:			Optional[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()
										lowercase_					:			Any										=       (out_a - labels).mean()
										loss_a.backward()

										# compare the output and parameters gradients
										self.assertTrue((loss - loss_a).abs() < 1E-5    )
										lowercase_					:			Optional[Any]										=       dict(model.named_parameters()    )
										lowercase_					:			List[str]										=       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 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_						,						lowercase_					:			Optional[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_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Tuple										=       AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""    )
										lowercase_					:			str										=       model.to(lowercase_    )
										model.eval()

										if torch_device == "mps":
																	lowercase_					:			Dict										=       torch.manual_seed(0    )
										else:
																	lowercase_					:			str										=       torch.Generator(device=lowercase_    ).manual_seed(0    )

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

										lowercase_					:			Optional[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":
																	lowercase_					:			Optional[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":
																	lowercase_					:			List[str]										=       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:
																	lowercase_					:			Optional[Any]										=       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(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]    ):
										return f'''gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_    ) for s in shape]    )}.npy'''







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  List[Any]=0				,			lowercase_				:  str=(4, 3, 512, 512)				,			lowercase_				:  Union[str, Any]=False    ):
										lowercase_					:			str										=       torch.floataa if fpaa else torch.floataa
										lowercase_					:			List[str]										=       torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_				,			lowercase_    )    )    ).to(lowercase_    ).to(lowercase_    )
										return image







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

										lowercase_					:			str										=       AutoencoderKL.from_pretrained(
										    lowercase_				,			subfolder="""vae"""				,			torch_dtype=lowercase_				,			revision=lowercase_				,			)
										model.to(lowercase_    ).eval()

										return model







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Any=0    ):
										if torch_device == "mps":
																	return torch.manual_seed(lowercase_    )
										return torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )







			@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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]    ):
										lowercase_					:			str										=       self.get_sd_vae_model()
										lowercase_					:			List[str]										=       self.get_sd_image(lowercase_    )
										lowercase_					:			str										=       self.get_generator(lowercase_    )

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

										assert sample.shape == image.shape

										lowercase_					:			Optional[int]										=       sample[-1, -2:, -2:, :2].flatten().float().cpu()
										lowercase_					:			Tuple										=       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, [-0.05_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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       self.get_sd_vae_model(fpaa=lowercase_    )
										lowercase_					:			Dict										=       self.get_sd_image(lowercase_				,			fpaa=lowercase_    )
										lowercase_					:			List[Any]										=       self.get_generator(lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Dict										=       model(lowercase_				,			generator=lowercase_				,			sample_posterior=lowercase_    ).sample

										assert sample.shape == image.shape

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

										assert torch_all_close(lowercase_				,			lowercase_				,			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 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  Any				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]    ):
										lowercase_					:			Union[str, Any]										=       self.get_sd_vae_model()
										lowercase_					:			Union[str, Any]										=       self.get_sd_image(lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(lowercase_    ).sample

										assert sample.shape == image.shape

										lowercase_					:			Any										=       sample[-1, -2:, -2:, :2].flatten().float().cpu()
										lowercase_					:			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
			        [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 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  str    ):
										lowercase_					:			Union[str, Any]										=       self.get_sd_vae_model()
										lowercase_					:			Dict										=       self.get_sd_image(lowercase_				,			shape=(3, 4, 64, 64)    )

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

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

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

										assert torch_all_close(lowercase_				,			lowercase_				,			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 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Dict										=       self.get_sd_vae_model(fpaa=lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_sd_image(lowercase_				,			shape=(3, 4, 64, 64)				,			fpaa=lowercase_    )

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

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

										lowercase_					:			str										=       sample[-1, -2:, :2, -2:].flatten().float().cpu()
										lowercase_					:			Any										=       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 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										lowercase_					:			List[Any]										=       self.get_sd_vae_model(fpaa=lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_sd_image(lowercase_				,			shape=(3, 4, 64, 64)				,			fpaa=lowercase_    )

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

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
																	lowercase_					:			Dict										=       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 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  int    ):
										lowercase_					:			List[str]										=       self.get_sd_vae_model()
										lowercase_					:			Any										=       self.get_sd_image(lowercase_				,			shape=(3, 4, 64, 64)    )

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

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
																	lowercase_					:			Optional[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, [-0.30_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 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple				,			lowercase_				:  List[str]    ):
										lowercase_					:			Tuple										=       self.get_sd_vae_model()
										lowercase_					:			int										=       self.get_sd_image(lowercase_    )
										lowercase_					:			Tuple										=       self.get_generator(lowercase_    )

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

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

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

										lowercase_					:			Dict										=       3E-3 if torch_device != """mps""" else 1E-2
										assert torch_all_close(lowercase_				,			lowercase_				,			atol=lowercase_    )






'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					list )			->      list:
							if any(not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) or x < 0 for x in sequence ):
														raise TypeError("""Sequence must be list of non-negative integers""" )
							for _ in range(len(UpperCAmelCase__ ) ):
														for i, (rod_upper, rod_lower) in enumerate(zip(UpperCAmelCase__      ,      sequence[1:] ) ):
																					if rod_upper > rod_lower:
																												sequence[i] -= rod_upper - rod_lower
																												sequence[i + 1] += rod_upper - rod_lower
							return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





