

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 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     AutoencoderKL
				__SCREAMING_SNAKE_CASE   :					int			     =     '''sample'''
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     1e-2




				@property
				def        a				(   self			):
											snake_case_     							=       4
											snake_case_     							=       3
											snake_case_     							=       (32, 32)

											snake_case_     							=       floats_tensor((batch_size, num_channels) + sizes			).to(snake_case			)

											return {"sample": image}




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




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




				def        a				(   self			):
											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			):
											pass




				def        a				(   self			):
											pass




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

											assert not model.is_gradient_checkpointing and model.training

											snake_case_     							=       model(**snake_case			).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(snake_case			)
											snake_case_     							=       (out - labels).mean()
											loss.backward()

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

											assert model_a.is_gradient_checkpointing and model_a.training

											snake_case_     							=       model_a(**snake_case			).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			):
											snake_case_							,       snake_case_     							=       AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' ,  output_loading_info=snake_case			)
											self.assertIsNotNone(snake_case			)
											self.assertEqual(len(loading_info['missing_keys']			) ,  0			)

											model.to(snake_case			)
											snake_case_     							=       model(**self.dummy_input			)

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




				def        a				(   self			):
											snake_case_     							=       AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'			)
											snake_case_     							=       model.to(snake_case			)
											model.eval()

											if torch_device == "mps":
																		snake_case_     							=       torch.manual_seed(0			)
											else:
																		snake_case_     							=       torch.Generator(device=snake_case			).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(snake_case			)
											with torch.no_grad():
																		snake_case_     							=       model(snake_case ,  sample_posterior=snake_case ,  generator=snake_case			).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(
																		    [
																		        -4.0_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":
																		snake_case_     							=       torch.tensor(
																		    [-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26]			)
											else:
																		snake_case_     							=       torch.tensor(
																		    [-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85]			)

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







@slow
class 					lowercase							(			unittest.TestCase     ):




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




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




				def        a				(   self ,  snake_case=0 ,  snake_case=(4, 3, 512, 512) ,  snake_case=False			):
											snake_case_     							=       torch.floataa if fpaa else torch.floataa
											snake_case_     							=       torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case ,  snake_case			)			)			).to(snake_case			).to(snake_case			)
											return image




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

											snake_case_     							=       AutoencoderKL.from_pretrained(
											    snake_case ,  subfolder='vae' ,  torch_dtype=snake_case ,  revision=snake_case ,  )
											model.to(snake_case			).eval()

											return model




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




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
				        [47, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
				        # fmt: on
				    ]			)
				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model()
											snake_case_     							=       self.get_sd_image(snake_case			)
											snake_case_     							=       self.get_generator(snake_case			)

											with torch.no_grad():
																		snake_case_     							=       model(snake_case ,  generator=snake_case ,  sample_posterior=snake_case			).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(snake_case ,  snake_case ,  atol=3e-3			)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
				        [47, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
				        # fmt: on
				    ]			)
				@require_torch_gpu
				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model(fpaa=snake_case			)
											snake_case_     							=       self.get_sd_image(snake_case ,  fpaa=snake_case			)
											snake_case_     							=       self.get_generator(snake_case			)

											with torch.no_grad():
																		snake_case_     							=       model(snake_case ,  generator=snake_case ,  sample_posterior=snake_case			).sample

											assert sample.shape == image.shape

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

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




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
				        [47, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
				        # fmt: on
				    ]			)
				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model()
											snake_case_     							=       self.get_sd_image(snake_case			)

											with torch.no_grad():
																		snake_case_     							=       model(snake_case			).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(snake_case ,  snake_case ,  atol=3e-3			)




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
				        [37, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
				        # fmt: on
				    ]			)
				@require_torch_gpu
				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model()
											snake_case_     							=       self.get_sd_image(snake_case ,  shape=(3, 4, 64, 64)			)

											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

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

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

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




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
				        [16, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
				        # fmt: on
				    ]			)
				@require_torch_gpu
				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model(fpaa=snake_case			)
											snake_case_     							=       self.get_sd_image(snake_case ,  shape=(3, 4, 64, 64) ,  fpaa=snake_case			)

											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

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

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

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

											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

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

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

											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		snake_case_     							=       model.decode(snake_case			).sample

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

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




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
				        [47, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
				        # fmt: on
				    ]			)
				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       self.get_sd_vae_model()
											snake_case_     							=       self.get_sd_image(snake_case			)
											snake_case_     							=       self.get_generator(snake_case			)

											with torch.no_grad():
																		snake_case_     							=       model.encode(snake_case			).latent_dist
																		snake_case_     							=       dist.sample(generator=snake_case			)

											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(snake_case			)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




