
"""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		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
					lowercase						       =						AutoencoderKL
					lowercase						       =						"""sample"""
					lowercase						       =						1e-2





					@property
					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	4
						__UpperCamelCase      =	3
						__UpperCamelCase      =	(32, 32)

						__UpperCamelCase      =	floats_tensor((batch_size, num_channels) + sizes				).to(_A				)

						return {"sample": image}





					@property
					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						return (3, 32, 32)





					@property
					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						return (3, 32, 32)





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




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





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						pass





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						pass





					@unittest.skipIf(torch_device == 'mps'     ,					'Gradient checkpointing skipped on MPS'				)
					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	self.prepare_init_args_and_inputs_for_common()
						__UpperCamelCase      =	self.model_class(**_A				)
						model.to(_A				)

						assert not model.is_gradient_checkpointing and model.training

						__UpperCamelCase      =	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()

						__UpperCamelCase      =	torch.randn_like(_A				)
						__UpperCamelCase      =	(out - labels).mean()
						loss.backward()

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

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

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1E-5				)
						__UpperCamelCase      =	dict(model.named_parameters()				)
						__UpperCamelCase      =	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 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	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				)
						__UpperCamelCase      =	model(**self.dummy_input				)

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







					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




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

						if torch_device == "mps":
							__UpperCamelCase      =	torch.manual_seed(0				)
						else:
							__UpperCamelCase      =	torch.Generator(device=_A				).manual_seed(0				)

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

						__UpperCamelCase      =	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":
							__UpperCamelCase      =	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":
							__UpperCamelCase      =	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:
							__UpperCamelCase      =	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 __lowerCAmelCase		(				unittest.TestCase      ):





					def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




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





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




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





					def 						UpperCAmelCase (     self     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=(4, 3, 512, 512)     ,					__UpperCAmelCase=False				):

						'''simple docstring'''




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





					def 						UpperCAmelCase (     self     ,					__UpperCAmelCase="CompVis/stable-diffusion-v1-4"     ,					__UpperCAmelCase=False				):

						'''simple docstring'''




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

						__UpperCamelCase      =	AutoencoderKL.from_pretrained(
						    _A     ,					subfolder='vae'     ,					torch_dtype=_A     ,					revision=_A     ,					)
						model.to(_A				).eval()

						return model





					def 						UpperCAmelCase (     self     ,					__UpperCAmelCase=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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model()
						__UpperCamelCase      =	self.get_sd_image(_A				)
						__UpperCamelCase      =	self.get_generator(_A				)

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

						assert sample.shape == image.shape

						__UpperCamelCase      =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
						__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model(fpaa=_A				)
						__UpperCamelCase      =	self.get_sd_image(_A     ,					fpaa=_A				)
						__UpperCamelCase      =	self.get_generator(_A				)

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

						assert sample.shape == image.shape

						__UpperCamelCase      =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
						__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model()
						__UpperCamelCase      =	self.get_sd_image(_A				)

						with torch.no_grad():
							__UpperCamelCase      =	model(_A				).sample

						assert sample.shape == image.shape

						__UpperCamelCase      =	sample[-1, -2:, -2:, :2].flatten().float().cpu()
						__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model()
						__UpperCamelCase      =	self.get_sd_image(_A     ,					shape=(3, 4, 64, 64)				)

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

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

						__UpperCamelCase      =	sample[-1, -2:, :2, -2:].flatten().cpu()
						__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model(fpaa=_A				)
						__UpperCamelCase      =	self.get_sd_image(_A     ,					shape=(3, 4, 64, 64)     ,					fpaa=_A				)

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

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

						__UpperCamelCase      =	sample[-1, -2:, :2, -2:].flatten().float().cpu()
						__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model(fpaa=_A				)
						__UpperCamelCase      =	self.get_sd_image(_A     ,					shape=(3, 4, 64, 64)     ,					fpaa=_A				)

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

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
							__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model()
						__UpperCamelCase      =	self.get_sd_image(_A     ,					shape=(3, 4, 64, 64)				)

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

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
							__UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

						'''simple docstring'''




						__UpperCamelCase      =	self.get_sd_vae_model()
						__UpperCamelCase      =	self.get_sd_image(_A				)
						__UpperCamelCase      =	self.get_generator(_A				)

						with torch.no_grad():
							__UpperCamelCase      =	model.encode(_A				).latent_dist
							__UpperCamelCase      =	dist.sample(generator=_A				)

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

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

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





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



