





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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
						A_						:    Any							=   AutoencoderKL
						A_						:    Tuple							=   'sample'
						A_						:    List[str]							=   1e-2


						@property
						def        a			(self	:				Optional[int]				):





									"""simple docstring"""



									__snake_case								=       4
									__snake_case								=       3
									__snake_case								=       (32, 32)

									__snake_case								=       floats_tensor((batch_size, num_channels) + sizes				).to(a__				)

									return {"sample": image}


						@property
						def        a			(self	:				str				):





									"""simple docstring"""



									return (3, 32, 32)


						@property
						def        a			(self	:				Any				):





									"""simple docstring"""



									return (3, 32, 32)


						def        a			(self	:				List[Any]				):





									"""simple docstring"""



									__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	:				List[Any]				):





									"""simple docstring"""



									pass


						def        a			(self	:				Any				):





									"""simple docstring"""



									pass


						@unittest.skipIf(torch_device == '''mps'''    ,      '''Gradient checkpointing skipped on MPS'''				)
						def        a			(self	:				List[str]				):





									"""simple docstring"""



									__snake_case	,     __snake_case								=       self.prepare_init_args_and_inputs_for_common()
									__snake_case								=       self.model_class(**a__				)
									model.to(a__				)

									assert not model.is_gradient_checkpointing and model.training

									__snake_case								=       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()

									__snake_case								=       torch.randn_like(a__				)
									__snake_case								=       (out - labels).mean()
									loss.backward()

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

									__snake_case								=       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()
									__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	:				Optional[Any]				):





									"""simple docstring"""



									__snake_case	,     __snake_case								=       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__				)
									__snake_case								=       model(**self.dummy_input				)

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


						def        a			(self	:				int				):





									"""simple docstring"""



									__snake_case								=       AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
									__snake_case								=       model.to(a__				)
									model.eval()

									if torch_device == "mps":
												__snake_case								=       torch.manual_seed(0				)
									else:
												__snake_case								=       torch.Generator(device=a__				).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(a__				)
									with torch.no_grad():
												__snake_case								=       model(a__    ,      sample_posterior=a__    ,      generator=a__				).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(
												    [
												        -4.0078E-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":
												__snake_case								=       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:
												__snake_case								=       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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


						def        a			(self	:				int    ,      a__	:				Dict    ,      a__	:				int				):





									"""simple docstring"""



									return f"""gaussian_noise_s={seed}_shape={'_'.join([str(a__				) for s in shape]				)}.npy"""


						def        a			(self	:				Optional[Any]				):





									"""simple docstring"""



									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


						def        a			(self	:				Union[str, Any]    ,      a__	:				List[Any]=0    ,      a__	:				Union[str, Any]=(4, 3, 512, 512)    ,      a__	:				List[Any]=False				):





									"""simple docstring"""



									__snake_case								=       torch.floataa if fpaa else torch.floataa
									__snake_case								=       torch.from_numpy(load_hf_numpy(self.get_file_format(a__    ,      a__				)				)				).to(a__				).to(a__				)
									return image


						def        a			(self	:				int    ,      a__	:				List[str]="CompVis/stable-diffusion-v1-4"    ,      a__	:				Tuple=False				):





									"""simple docstring"""



									__snake_case								=       '''fp16''' if fpaa else None
									__snake_case								=       torch.floataa if fpaa else torch.floataa

									__snake_case								=       AutoencoderKL.from_pretrained(
									    a__    ,      subfolder='''vae'''    ,      torch_dtype=a__    ,      revision=a__    ,      )
									model.to(a__				).eval()

									return model


						def        a			(self	:				str    ,      a__	:				Tuple=0				):





									"""simple docstring"""



									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        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				Optional[Any]    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model()
									__snake_case								=       self.get_sd_image(a__				)
									__snake_case								=       self.get_generator(a__				)

									with torch.no_grad():
												__snake_case								=       model(a__    ,      generator=a__    ,      sample_posterior=a__				).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(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        a			(self	:				Optional[Any]    ,      a__	:				List[str]    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model(fpaa=a__				)
									__snake_case								=       self.get_sd_image(a__    ,      fpaa=a__				)
									__snake_case								=       self.get_generator(a__				)

									with torch.no_grad():
												__snake_case								=       model(a__    ,      generator=a__    ,      sample_posterior=a__				).sample

									assert sample.shape == image.shape

									__snake_case								=       sample[-1, -2:, :2, -2:].flatten().float().cpu()
									__snake_case								=       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        a			(self	:				str    ,      a__	:				Optional[int]    ,      a__	:				Optional[int]    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model()
									__snake_case								=       self.get_sd_image(a__				)

									with torch.no_grad():
												__snake_case								=       model(a__				).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(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        a			(self	:				List[str]    ,      a__	:				str    ,      a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model()
									__snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)				)

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

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

									__snake_case								=       sample[-1, -2:, :2, -2:].flatten().cpu()
									__snake_case								=       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        a			(self	:				int    ,      a__	:				Optional[int]    ,      a__	:				str				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model(fpaa=a__				)
									__snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)    ,      fpaa=a__				)

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

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

									__snake_case								=       sample[-1, -2:, :2, -2:].flatten().float().cpu()
									__snake_case								=       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        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model(fpaa=a__				)
									__snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)    ,      fpaa=a__				)

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

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
												__snake_case								=       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        a			(self	:				Optional[Any]    ,      a__	:				int				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model()
									__snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)				)

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

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
												__snake_case								=       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, [-0.3_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        a			(self	:				Optional[int]    ,      a__	:				List[Any]    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       self.get_sd_vae_model()
									__snake_case								=       self.get_sd_image(a__				)
									__snake_case								=       self.get_generator(a__				)

									with torch.no_grad():
												__snake_case								=       model.encode(a__				).latent_dist
												__snake_case								=       dist.sample(generator=a__				)

									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(a__				)

									__snake_case								=       3E-3 if torch_device != '''mps''' else 1E-2
									assert torch_all_close(a__    ,      a__    ,      atol=a__				)








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

