


'''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			(		lowercase_						,	lowercase_						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     List[Any]        =							AutoencoderKL
		A_      :     int        =							'''sample'''
		A_      :     List[Any]        =							1E-2







		@property
		def   __lowerCAmelCase						(   self						:		str     )    ->					List[Any]:
			__magic_name__			:       Optional[int]	      =	4
			__magic_name__			:       Union[str, Any]	      =	3
			__magic_name__			:       int	      =	(32, 32)

			__magic_name__			:       Any	      =	floats_tensor((batch_size, num_channels) + sizes     ).to(_A     )

			return {"sample": image}







		@property
		def   __lowerCAmelCase						(   self						:		Optional[int]     )    ->					int:
			return (3, 32, 32)







		@property
		def   __lowerCAmelCase						(   self						:		str     )    ->					str:
			return (3, 32, 32)







		def   __lowerCAmelCase						(   self						:		Optional[Any]     )    ->					Dict:
			__magic_name__			:       Union[str, 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,
			}
			__magic_name__			:       List[str]	      =	self.dummy_input
			return init_dict, inputs_dict







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







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







		@unittest.skipIf(torch_device == 'mps'    ,	'Gradient checkpointing skipped on MPS'     )
		def   __lowerCAmelCase						(   self						:		Union[str, Any]     )    ->					Any:
			# enable deterministic behavior for gradient checkpointing
			__magic_name__      ,							__magic_name__			:       List[str]	      =	self.prepare_init_args_and_inputs_for_common()
			__magic_name__			:       Optional[Any]	      =	self.model_class(**_A     )
			model.to(_A     )

			assert not model.is_gradient_checkpointing and model.training

			__magic_name__			:       Optional[Any]	      =	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()

			__magic_name__			:       Dict	      =	torch.randn_like(_A     )
			__magic_name__			:       Optional[int]	      =	(out - labels).mean()
			loss.backward()

			# re-instantiate the model now enabling gradient checkpointing
			__magic_name__			:       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

			__magic_name__			:       Optional[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()
			__magic_name__			:       str	      =	(out_a - labels).mean()
			loss_a.backward()

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

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







		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			__magic_name__			:       Any	      =	AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'     )
			__magic_name__			:       Tuple	      =	model.to(_A     )
			model.eval()

			if torch_device == "mps":
				__magic_name__			:       Optional[Any]	      =	torch.manual_seed(0     )
			else:
				__magic_name__			:       Tuple	      =	torch.Generator(device=_A     ).manual_seed(0     )

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

			__magic_name__			:       List[str]	      =	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":
				__magic_name__			:       Optional[int]	      =	torch.tensor(
				    [
				        -4.0_078E-01,
				        -3.8_323E-04,
				        -1.2_681E-01,
				        -1.1_462E-01,
				        2.0_095E-01,
				        1.0_893E-01,
				        -8.8_247E-02,
				        -3.0_361E-01,
				        -9.8_644E-03,
				    ]     )
			elif torch_device == "cpu":
				__magic_name__			:       Dict	      =	torch.tensor(
				    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]     )
			else:
				__magic_name__			:       Optional[int]	      =	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(_A    ,	_A    ,	rtol=1E-2     )     )







@slow
class _lowerCamelCase			(		unittest.TestCase				):



		'''simple docstring'''







		def   __lowerCAmelCase						(   self						:		str    ,	_A						:		Optional[int]    ,	_A						:		str     )    ->					int:
			return F'gaussian_noise_s={seed}_shape={"_".join([str(_A     ) for s in shape]     )}.npy'







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Dict:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def   __lowerCAmelCase						(   self						:		str    ,	_A						:		int=0    ,	_A						:		str=(4, 3, 512, 512)    ,	_A						:		int=False     )    ->					int:
			__magic_name__			:       Union[str, Any]	      =	torch.floataa if fpaa else torch.floataa
			__magic_name__			:       List[str]	      =	torch.from_numpy(load_hf_numpy(self.get_file_format(_A    ,	_A     )     )     ).to(_A     ).to(_A     )
			return image







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

			__magic_name__			:       List[Any]	      =	AutoencoderKL.from_pretrained(
			    _A    ,	subfolder='vae'    ,	torch_dtype=_A    ,	revision=_A    ,	)
			model.to(_A     ).eval()

			return model







		def   __lowerCAmelCase						(   self						:		Optional[int]    ,	_A						:		Tuple=0     )    ->					int:
			if torch_device == "mps":
				return torch.manual_seed(_A     )
			return torch.Generator(device=_A     ).manual_seed(_A     )







		@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   __lowerCAmelCase						(   self						:		Union[str, Any]    ,	_A						:		int    ,	_A						:		List[Any]    ,	_A						:		Union[str, Any]     )    ->					List[str]:
			__magic_name__			:       Dict	      =	self.get_sd_vae_model()
			__magic_name__			:       Any	      =	self.get_sd_image(_A     )
			__magic_name__			:       int	      =	self.get_generator(_A     )

			with torch.no_grad():
				__magic_name__			:       Optional[int]	      =	model(_A    ,	generator=_A    ,	sample_posterior=_A     ).sample

			assert sample.shape == image.shape

			__magic_name__			:       Dict	      =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
			__magic_name__			:       str	      =	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, [-0.0513, 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   __lowerCAmelCase						(   self						:		str    ,	_A						:		List[str]    ,	_A						:		int     )    ->					Any:
			__magic_name__			:       Optional[Any]	      =	self.get_sd_vae_model(fpaa=_A     )
			__magic_name__			:       Tuple	      =	self.get_sd_image(_A    ,	fpaa=_A     )
			__magic_name__			:       str	      =	self.get_generator(_A     )

			with torch.no_grad():
				__magic_name__			:       Tuple	      =	model(_A    ,	generator=_A    ,	sample_posterior=_A     ).sample

			assert sample.shape == image.shape

			__magic_name__			:       Any	      =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
			__magic_name__			:       List[Any]	      =	torch.tensor(_A     )

			assert torch_all_close(_A    ,	_A    ,	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   __lowerCAmelCase						(   self						:		List[str]    ,	_A						:		Tuple    ,	_A						:		List[str]    ,	_A						:		List[Any]     )    ->					Tuple:
			__magic_name__			:       str	      =	self.get_sd_vae_model()
			__magic_name__			:       List[Any]	      =	self.get_sd_image(_A     )

			with torch.no_grad():
				__magic_name__			:       Union[str, Any]	      =	model(_A     ).sample

			assert sample.shape == image.shape

			__magic_name__			:       Dict	      =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
			__magic_name__			:       List[str]	      =	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, [-0.2051, -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   __lowerCAmelCase						(   self						:		List[str]    ,	_A						:		Dict    ,	_A						:		Dict     )    ->					Optional[Any]:
			__magic_name__			:       Dict	      =	self.get_sd_vae_model()
			__magic_name__			:       Optional[int]	      =	self.get_sd_image(_A    ,	shape=(3, 4, 64, 64)     )

			with torch.no_grad():
				__magic_name__			:       List[Any]	      =	model.decode(_A     ).sample

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

			__magic_name__			:       Union[str, Any]	      =	sample[-1, -2:, :2, -2:].flatten().cpu()
			__magic_name__			:       Any	      =	torch.tensor(_A     )

			assert torch_all_close(_A    ,	_A    ,	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   __lowerCAmelCase						(   self						:		List[Any]    ,	_A						:		str    ,	_A						:		Dict     )    ->					Union[str, Any]:
			__magic_name__			:       int	      =	self.get_sd_vae_model(fpaa=_A     )
			__magic_name__			:       Dict	      =	self.get_sd_image(_A    ,	shape=(3, 4, 64, 64)    ,	fpaa=_A     )

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

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

			__magic_name__			:       List[str]	      =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
			__magic_name__			:       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   __lowerCAmelCase						(   self						:		Tuple    ,	_A						:		Optional[Any]     )    ->					Union[str, Any]:
			__magic_name__			:       Tuple	      =	self.get_sd_vae_model(fpaa=_A     )
			__magic_name__			:       int	      =	self.get_sd_image(_A    ,	shape=(3, 4, 64, 64)    ,	fpaa=_A     )

			with torch.no_grad():
				__magic_name__			:       List[Any]	      =	model.decode(_A     ).sample

			model.enable_xformers_memory_efficient_attention()
			with torch.no_grad():
				__magic_name__			:       int	      =	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   __lowerCAmelCase						(   self						:		Optional[Any]    ,	_A						:		Any     )    ->					int:
			__magic_name__			:       Optional[Any]	      =	self.get_sd_vae_model()
			__magic_name__			:       Dict	      =	self.get_sd_image(_A    ,	shape=(3, 4, 64, 64)     )

			with torch.no_grad():
				__magic_name__			:       Tuple	      =	model.decode(_A     ).sample

			model.enable_xformers_memory_efficient_attention()
			with torch.no_grad():
				__magic_name__			:       Any	      =	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, [-0.3001, 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   __lowerCAmelCase						(   self						:		List[Any]    ,	_A						:		List[Any]    ,	_A						:		Optional[Any]     )    ->					List[str]:
			__magic_name__			:       List[Any]	      =	self.get_sd_vae_model()
			__magic_name__			:       int	      =	self.get_sd_image(_A     )
			__magic_name__			:       List[str]	      =	self.get_generator(_A     )

			with torch.no_grad():
				__magic_name__			:       Optional[int]	      =	model.encode(_A     ).latent_dist
				__magic_name__			:       Optional[Any]	      =	dist.sample(generator=_A     )

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

			__magic_name__			:       Union[str, Any]	      =	sample[0, -1, -3:, -3:].flatten().cpu()
			__magic_name__			:       str	      =	torch.tensor(_A     )

			__magic_name__			:       List[str]	      =	3E-3 if torch_device != 'mps' else 1E-2
			assert torch_all_close(_A    ,	_A    ,	atol=_A     )


import string





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       ''
							for i in sequence:
														snake_case_     							=       ord(UpperCamelCase__ )
														if 65 <= extract <= 90:
																					output += chr(155 - extract )
														elif 97 <= extract <= 122:
																					output += chr(219 - extract )
														else:
																					output += i
							return output





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       string.ascii_letters
							snake_case_     							=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(UpperCamelCase__ )] if c in letters else c for c in sequence )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							from timeit import timeit

							print('Running performance benchmarks...' )
							snake_case_     							=       'from string import printable ; from __main__ import atbash, atbash_slow'
							print(F'''> atbash_slow(): {timeit("atbash_slow(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )
							print(F'''>      atbash(): {timeit("atbash(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )


if __name__ == "__main__":
					for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
										print(F'''{example} encrypted in atbash: {atbash(example)}''')
					benchmark()




