





'''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 				__lowercase							(   a__			,   a__			,   unittest.TestCase    ):







	'''simple docstring'''

	a     :					Union[str, Any]           =							AutoencoderKL
	a     :					Tuple           =							"""sample"""
	a     :					int           =							1e-2


	@property
	def     _UpperCAmelCase  (self			)				->				List[str]:







				'''simple docstring'''





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

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

				return {"sample": image}


	@property
	def     _UpperCAmelCase  (self			)				->				Optional[Any]:







				'''simple docstring'''





				return (3, 32, 32)


	@property
	def     _UpperCAmelCase  (self			)				->				str:







				'''simple docstring'''





				return (3, 32, 32)


	def     _UpperCAmelCase  (self			)				->				int:







				'''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			)				->				Any:







				'''simple docstring'''





				pass


	def     _UpperCAmelCase  (self			)				->				List[str]:







				'''simple docstring'''





				pass


	@unittest.skipIf(torch_device == '''mps'''  ,'''Gradient checkpointing skipped on MPS'''			)
	def     _UpperCAmelCase  (self			)				->				str:







				'''simple docstring'''





				__lowercase					    =				self.prepare_init_args_and_inputs_for_common()
				__lowercase					    =				self.model_class(**__a			)
				model.to(__a			)

				assert not model.is_gradient_checkpointing and model.training

				__lowercase					    =				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()

				__lowercase					    =				torch.randn_like(__a			)
				__lowercase					    =				(out - labels).mean()
				loss.backward()

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

				__lowercase					    =				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()
				__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			)				->				Dict:







				'''simple docstring'''





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

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


	def     _UpperCAmelCase  (self			)				->				List[str]:







				'''simple docstring'''





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

				if torch_device == "mps":
							__lowercase					    =				torch.manual_seed(0			)
				else:
							__lowercase					    =				torch.Generator(device=__a			).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(__a			)
				with torch.no_grad():
							__lowercase					    =				model(__a  ,sample_posterior=__a  ,generator=__a			).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(__a  ,__a  ,rtol=1E-2			)			)






@slow
class 				__lowercase							(   unittest.TestCase    ):







	'''simple docstring'''

	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase			)				->				List[Any]:







				'''simple docstring'''





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


	def     _UpperCAmelCase  (self			)				->				str:







				'''simple docstring'''





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


	def     _UpperCAmelCase  (self  ,_lowerCamelCase=0  ,_lowerCamelCase=(4, 3, 512, 512)  ,_lowerCamelCase=False			)				->				Dict:







				'''simple docstring'''





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


	def     _UpperCAmelCase  (self  ,_lowerCamelCase="CompVis/stable-diffusion-v1-4"  ,_lowerCamelCase=False			)				->				List[Any]:







				'''simple docstring'''





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

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

				return model


	def     _UpperCAmelCase  (self  ,_lowerCamelCase=0			)				->				Dict:







				'''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  ,_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase			)				->				Any:







				'''simple docstring'''





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

				with torch.no_grad():
							__lowercase					    =				model(__a  ,generator=__a  ,sample_posterior=__a			).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(__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  ,_lowerCamelCase  ,_lowerCamelCase			)				->				int:







				'''simple docstring'''





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

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

				assert sample.shape == image.shape

				__lowercase					    =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				__lowercase					    =				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  ,_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase			)				->				Optional[int]:







				'''simple docstring'''





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

				with torch.no_grad():
							__lowercase					    =				model(__a			).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(__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  ,_lowerCamelCase  ,_lowerCamelCase			)				->				Union[str, Any]:







				'''simple docstring'''





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

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

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

				__lowercase					    =				sample[-1, -2:, :2, -2:].flatten().cpu()
				__lowercase					    =				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  ,_lowerCamelCase  ,_lowerCamelCase			)				->				List[str]:







				'''simple docstring'''





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

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

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

				__lowercase					    =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				__lowercase					    =				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  ,_lowerCamelCase			)				->				Union[str, Any]:







				'''simple docstring'''





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

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

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							__lowercase					    =				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  ,_lowerCamelCase			)				->				List[Any]:







				'''simple docstring'''





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

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

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							__lowercase					    =				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  ,_lowerCamelCase  ,_lowerCamelCase			)				->				List[Any]:







				'''simple docstring'''





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

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

				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(__a			)

				__lowercase					    =				3E-3 if torch_device != """mps""" else 1E-2
				assert torch_all_close(__a  ,__a  ,atol=__a			)











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Union[str, Any]      =       {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   Union[str, Any]      =       [
          """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InstructBlipQFormerModel""",
          """InstructBlipPreTrainedModel""",
          """InstructBlipForConditionalGeneration""",
          """InstructBlipVisionModel""",
      ]

if TYPE_CHECKING:
      from .configuration_instructblip import (
          INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          InstructBlipConfig,
          InstructBlipQFormerConfig,
          InstructBlipVisionConfig,
      )
      from .processing_instructblip import InstructBlipProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_instructblip import (
                INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                InstructBlipForConditionalGeneration,
                InstructBlipPreTrainedModel,
                InstructBlipQFormerModel,
                InstructBlipVisionModel,
            )

else:
      import sys

      lowercase      :   List[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



