


"""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 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =AutoencoderKL
							__UpperCAmelCase		:					Any          ="""sample"""
							__UpperCAmelCase		:					Optional[Any]          =1E-2

							@property
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    4
										__lowerCAmelCase       					=    3
										__lowerCAmelCase       					=    (32, 32)

										__lowerCAmelCase       					=    floats_tensor((batch_size, num_channels) + sizes			).to(__a			)

										return {"sample": image}

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

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

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

							def     snake_case       (  self			):
										pass

							def     snake_case       (  self			):
										pass

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

										assert not model.is_gradient_checkpointing and model.training

										__lowerCAmelCase       					=    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()

										__lowerCAmelCase       					=    torch.randn_like(__a			)
										__lowerCAmelCase       					=    (out - labels).mean()
										loss.backward()

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

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

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

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"			)
										__lowerCAmelCase       					=    model.to(__a			)
										model.eval()

										if torch_device == "mps":
													__lowerCAmelCase       					=    torch.manual_seed(0			)
										else:
													__lowerCAmelCase       					=    torch.Generator(device=__a			).manual_seed(0			)

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

										__lowerCAmelCase       					=    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":
													__lowerCAmelCase       					=    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":
													__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    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(__a					,     __a					,     rtol=1e-2			)			)
@slow
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







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

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

							def     snake_case       (  self					,     __a=0					,     __a=(4, 3, 5_12, 5_12)					,     __a=False			):
										__lowerCAmelCase       					=    torch.floataa if fpaa else torch.floataa
										__lowerCAmelCase       					=    torch.from_numpy(load_hf_numpy(self.get_file_format(__a					,     __a			)			)			).to(__a			).to(__a			)
										return image

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

										__lowerCAmelCase       					=    AutoencoderKL.from_pretrained(
										    __a					,     subfolder="vae"					,     torch_dtype=__a					,     revision=__a					,     )
										model.to(__a			).eval()

										return model

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

							@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     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model()
										__lowerCAmelCase       					=    self.get_sd_image(__a			)
										__lowerCAmelCase       					=    self.get_generator(__a			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model(__a					,     generator=__a					,     sample_posterior=__a			).sample

										assert sample.shape == image.shape

										__lowerCAmelCase       					=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
										__lowerCAmelCase       					=    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, [-0.0_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     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model(fpaa=__a			)
										__lowerCAmelCase       					=    self.get_sd_image(__a					,     fpaa=__a			)
										__lowerCAmelCase       					=    self.get_generator(__a			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model(__a					,     generator=__a					,     sample_posterior=__a			).sample

										assert sample.shape == image.shape

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

										assert torch_all_close(__a					,     __a					,     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     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model()
										__lowerCAmelCase       					=    self.get_sd_image(__a			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model(__a			).sample

										assert sample.shape == image.shape

										__lowerCAmelCase       					=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
										__lowerCAmelCase       					=    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, [-0.2_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     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model()
										__lowerCAmelCase       					=    self.get_sd_image(__a					,     shape=(3, 4, 64, 64)			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

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

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

										assert torch_all_close(__a					,     __a					,     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     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model(fpaa=__a			)
										__lowerCAmelCase       					=    self.get_sd_image(__a					,     shape=(3, 4, 64, 64)					,     fpaa=__a			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

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

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

										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

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

										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     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model()
										__lowerCAmelCase       					=    self.get_sd_image(__a					,     shape=(3, 4, 64, 64)			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

										model.enable_xformers_memory_efficient_attention()
										with torch.no_grad():
													__lowerCAmelCase       					=    model.decode(__a			).sample

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

										assert torch_all_close(__a					,     __a					,     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     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.get_sd_vae_model()
										__lowerCAmelCase       					=    self.get_sd_image(__a			)
										__lowerCAmelCase       					=    self.get_generator(__a			)

										with torch.no_grad():
													__lowerCAmelCase       					=    model.encode(__a			).latent_dist
													__lowerCAmelCase       					=    dist.sample(generator=__a			)

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

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

										__lowerCAmelCase       					=    3e-3 if torch_device != "mps" else 1e-2
										assert torch_all_close(__a					,     __a					,     atol=__a			)








"""simple docstring"""


import string


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    ""
			for i in sequence:
						__lowerCAmelCase       					=    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'''




			__lowerCAmelCase       					=    string.ascii_letters
			__lowerCAmelCase       					=    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..."   )
			__lowerCAmelCase       					=    "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()




