






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				( snake_case  ,	snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					AutoencoderKL
							A_						  =					"sample"
							A_						  =					1e-2

							@property
							def       __A (  self:   int   )    ->				Union[str, Any]:
												_A								= 4
												_A								= 3
												_A								= (32, 32)

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

												return {"sample": image}

							@property
							def       __A (  self:   Optional[Any]   )    ->				List[str]:
												return (3, 32, 32)

							@property
							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												return (3, 32, 32)

							def       __A (  self:   str   )    ->				Any:
												_A								= {
												    '''block_out_channels''': [32, 64],
												    '''in_channels''': 3,
												    '''out_channels''': 3,
												    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
												    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
												    '''latent_channels''': 4,
												}
												_A								= self.dummy_input
												return init_dict, inputs_dict

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

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

							@unittest.skipIf(torch_device == '''mps'''		,     '''Gradient checkpointing skipped on MPS'''   )
							def       __A (  self:   Any   )    ->				Tuple:
												# enable deterministic behavior for gradient checkpointing
												_A							,_A								= self.prepare_init_args_and_inputs_for_common()
												_A								= self.model_class(**__A   )
												model.to(__A   )

												assert not model.is_gradient_checkpointing and model.training

												_A								= 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()

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

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

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

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

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



							def       __A (  self:   Dict   )    ->				Optional[Any]:
												_A								= AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''   )
												_A								= model.to(__A   )
												model.eval()

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

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

												_A								= 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":
																	_A								= torch.tensor(
																	    [
																	        -4.00_78e-01,
																	        -3.83_23e-04,
																	        -1.26_81e-01,
																	        -1.14_62e-01,
																	        2.00_95e-01,
																	        1.08_93e-01,
																	        -8.82_47e-02,
																	        -3.03_61e-01,
																	        -9.86_44e-03,
																	    ]   )
												elif torch_device == "cpu":
																	_A								= torch.tensor(
																	    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]   )
												else:
																	_A								= torch.tensor(
																	    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]   )

												self.assertTrue(torch_all_close(__A		,     __A		,     rtol=1e-2   )   )





@slow
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   Any		,     __A:   List[Any]		,     __A:   Optional[Any]   )    ->				Any:
												return f"""gaussian_noise_s={seed}_shape={"_".join([str(__A   ) for s in shape]   )}.npy"""

							def       __A (  self:   List[Any]   )    ->				List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

							def       __A (  self:   List[Any]		,     __A:   Tuple=0		,     __A:   Any=(4, 3, 5_12, 5_12)		,     __A:   Dict=False   )    ->				Union[str, Any]:
												_A								= torch.floataa if fpaa else torch.floataa
												_A								= torch.from_numpy(load_hf_numpy(self.get_file_format(__A		,     __A   )   )   ).to(__A   ).to(__A   )
												return image

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

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

												return model

							def       __A (  self:   Any		,     __A:   str=0   )    ->				Tuple:
												if torch_device == "mps":
																	return torch.manual_seed(__A   )
												return torch.Generator(device=__A   ).manual_seed(__A   )

							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]   )
							def       __A (  self:   Union[str, Any]		,     __A:   Any		,     __A:   str		,     __A:   Any   )    ->				Tuple:
												_A								= self.get_sd_vae_model()
												_A								= self.get_sd_image(__A   )
												_A								= self.get_generator(__A   )

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

												assert sample.shape == image.shape

												_A								= sample[-1, -2:, -2:, :2].flatten().float().cpu()
												_A								= 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_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
							        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
							        # fmt: on
							    ]   )
							@require_torch_gpu
							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   int   )    ->				Tuple:
												_A								= self.get_sd_vae_model(fpaa=__A   )
												_A								= self.get_sd_image(__A		,     fpaa=__A   )
												_A								= self.get_generator(__A   )

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

												assert sample.shape == image.shape

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

												assert torch_all_close(__A		,     __A		,     atol=1e-2   )

							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]   )
							def       __A (  self:   List[str]		,     __A:   str		,     __A:   Tuple		,     __A:   Optional[Any]   )    ->				Union[str, Any]:
												_A								= self.get_sd_vae_model()
												_A								= self.get_sd_image(__A   )

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

												assert sample.shape == image.shape

												_A								= sample[-1, -2:, -2:, :2].flatten().float().cpu()
												_A								= 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_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
							        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
							        # fmt: on
							    ]   )
							@require_torch_gpu
							def       __A (  self:   str		,     __A:   Tuple		,     __A:   str   )    ->				str:
												_A								= self.get_sd_vae_model()
												_A								= self.get_sd_image(__A		,     shape=(3, 4, 64, 64)   )

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

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

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

												assert torch_all_close(__A		,     __A		,     atol=1e-3   )

							@parameterized.expand(
							    [
							        # fmt: off
							        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
							        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
							        # fmt: on
							    ]   )
							@require_torch_gpu
							def       __A (  self:   str		,     __A:   int		,     __A:   Optional[int]   )    ->				int:
												_A								= self.get_sd_vae_model(fpaa=__A   )
												_A								= self.get_sd_image(__A		,     shape=(3, 4, 64, 64)		,     fpaa=__A   )

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

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

												_A								= sample[-1, -2:, :2, -2:].flatten().float().cpu()
												_A								= 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:   Any		,     __A:   Dict   )    ->				Union[str, Any]:
												_A								= self.get_sd_vae_model(fpaa=__A   )
												_A								= self.get_sd_image(__A		,     shape=(3, 4, 64, 64)		,     fpaa=__A   )

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

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

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

												model.enable_xformers_memory_efficient_attention()
												with torch.no_grad():
																	_A								= 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_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
							        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
							        # fmt: on
							    ]   )
							def       __A (  self:   str		,     __A:   Dict		,     __A:   Any   )    ->				Tuple:
												_A								= self.get_sd_vae_model()
												_A								= self.get_sd_image(__A   )
												_A								= self.get_generator(__A   )

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

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

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

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

				__A      =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



