




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 a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[Any]       	=		AutoencoderKL
						UpperCamelCase_	:       str       	=		'sample'
						UpperCamelCase_	:       List[str]       	=		1e-2






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    4
													__lowercase      =    3
													__lowercase      =    (32, 32)

													__lowercase      =    floats_tensor((batch_size, num_channels) + sizes				).to(lowerCamelCase__				)

													return {"sample": image}






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						int:
													"""simple docstring"""


													return (3, 32, 32)






						@property
						def     UpperCAmelCase_      ( self :     Dict				)    ->						Any:
													"""simple docstring"""


													return (3, 32, 32)






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    {
													    '''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      =    self.dummy_input
													return init_dict, inputs_dict






						def     UpperCAmelCase_      ( self :     int				)    ->						str:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Dict:
													"""simple docstring"""


													pass






						@unittest.skipIf(torch_device == '''mps'''				,							'''Gradient checkpointing skipped on MPS'''				)
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						str:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.prepare_init_args_and_inputs_for_common()
													__lowercase      =    self.model_class(**lowerCamelCase__				)
													model.to(lowerCamelCase__				)

													assert not model.is_gradient_checkpointing and model.training

													__lowercase      =    model(**lowerCamelCase__				).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      =    torch.randn_like(lowerCamelCase__				)
													__lowercase      =    (out - labels).mean()
													loss.backward()

													# re-instantiate the model now enabling gradient checkpointing
													__lowercase      =    self.model_class(**lowerCamelCase__				)
													# clone model
													model_a.load_state_dict(model.state_dict()				)
													model_a.to(lowerCamelCase__				)
													model_a.enable_gradient_checkpointing()

													assert model_a.is_gradient_checkpointing and model_a.training

													__lowercase      =    model_a(**lowerCamelCase__				).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      =    (out_a - labels).mean()
													loss_a.backward()

													# compare the output and parameters gradients
													self.assertTrue((loss - loss_a).abs() < 1e-5				)
													__lowercase      =    dict(model.named_parameters()				)
													__lowercase      =    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 :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				,							output_loading_info=lowerCamelCase__				)
													self.assertIsNotNone(lowerCamelCase__				)
													self.assertEqual(len(loading_info['''missing_keys''']				)				,							0				)

													model.to(lowerCamelCase__				)
													__lowercase      =    model(**self.dummy_input				)

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






						def     UpperCAmelCase_      ( self :     str				)    ->						Dict:
													"""simple docstring"""


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

													if torch_device == "mps":
																				__lowercase      =    torch.manual_seed(0				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(0				)

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

													__lowercase      =    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      =    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":
																				__lowercase      =    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:
																				__lowercase      =    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(lowerCamelCase__				,							lowerCamelCase__				,							rtol=1e-2				)				)







@slow
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     str				)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Tuple=0				,							lowerCamelCase__ :     Dict=(4, 3, 512, 512)				,							lowerCamelCase__ :     Tuple=False				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[Any]="CompVis/stable-diffusion-v1-4"				,							lowerCamelCase__ :     List[str]=False				)    ->						Optional[int]:
													"""simple docstring"""


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

													__lowercase      =    AutoencoderKL.from_pretrained(
													    lowerCamelCase__				,							subfolder='''vae'''				,							torch_dtype=lowerCamelCase__				,							revision=lowerCamelCase__				,							)
													model.to(lowerCamelCase__				).eval()

													return model






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Dict=0				)    ->						Dict:
													"""simple docstring"""


													if torch_device == "mps":
																				return torch.manual_seed(lowerCamelCase__				)
													return torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)






						@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 :     str				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model()
													__lowercase      =    self.get_sd_image(lowerCamelCase__				)
													__lowercase      =    self.get_generator(lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model(lowerCamelCase__				,							generator=lowerCamelCase__				,							sample_posterior=lowerCamelCase__				).sample

													assert sample.shape == image.shape

													__lowercase      =    sample[-1, -2:, -2:, :2].flatten().float().cpu()
													__lowercase      =    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Optional[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model(fpaa=lowerCamelCase__				)
													__lowercase      =    self.get_sd_image(lowerCamelCase__				,							fpaa=lowerCamelCase__				)
													__lowercase      =    self.get_generator(lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model(lowerCamelCase__				,							generator=lowerCamelCase__				,							sample_posterior=lowerCamelCase__				).sample

													assert sample.shape == image.shape

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

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Tuple				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Any				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model()
													__lowercase      =    self.get_sd_image(lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model(lowerCamelCase__				).sample

													assert sample.shape == image.shape

													__lowercase      =    sample[-1, -2:, -2:, :2].flatten().float().cpu()
													__lowercase      =    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Optional[Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Optional[int]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model()
													__lowercase      =    self.get_sd_image(lowerCamelCase__				,							shape=(3, 4, 64, 64)				)

													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

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

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

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Union[str, Any]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Any				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model(fpaa=lowerCamelCase__				)
													__lowercase      =    self.get_sd_image(lowerCamelCase__				,							shape=(3, 4, 64, 64)				,							fpaa=lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

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

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

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Union[str, Any]				,							lowerCamelCase__ :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model(fpaa=lowerCamelCase__				)
													__lowercase      =    self.get_sd_image(lowerCamelCase__				,							shape=(3, 4, 64, 64)				,							fpaa=lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

													model.enable_xformers_memory_efficient_attention()
													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

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

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     Union[str, Any]				,							lowerCamelCase__ :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model()
													__lowercase      =    self.get_sd_image(lowerCamelCase__				,							shape=(3, 4, 64, 64)				)

													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

													model.enable_xformers_memory_efficient_attention()
													with torch.no_grad():
																				__lowercase      =    model.decode(lowerCamelCase__				).sample

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

													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							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 :     str				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     int				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.get_sd_vae_model()
													__lowercase      =    self.get_sd_image(lowerCamelCase__				)
													__lowercase      =    self.get_generator(lowerCamelCase__				)

													with torch.no_grad():
																				__lowercase      =    model.encode(lowerCamelCase__				).latent_dist
																				__lowercase      =    dist.sample(generator=lowerCamelCase__				)

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

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

													__lowercase      =    3e-3 if torch_device != '''mps''' else 1e-2
													assert torch_all_close(lowerCamelCase__				,							lowerCamelCase__				,							atol=lowerCamelCase__				)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__				     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






