

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 _UpperCamelCase							(							a__							,		a__							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=AutoencoderKL
			lowerCamelCase__							='sample'
			lowerCamelCase__							=1e-2
			@property
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				4
				SCREAMING_SNAKE_CASE      :					str    =				3
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				(32, 32)

				SCREAMING_SNAKE_CASE      :					Any    =				floats_tensor((batch_size, num_channels) + sizes		).to(_UpperCAmelCase		)

				return {"sample": image}
			@property
			def 							__UpperCamelCase       (	self							:       int		)							->				List[Any]:

				"""simple docstring"""

				return (3, 32, 32)
			@property
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Any:

				"""simple docstring"""

				return (3, 32, 32)
			def 							__UpperCamelCase       (	self							:       str		)							->				Optional[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			@unittest.skipIf(torch_device == "mps"					,					"Gradient checkpointing skipped on MPS"		)
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.prepare_init_args_and_inputs_for_common()
				SCREAMING_SNAKE_CASE      :					str    =				self.model_class(**_UpperCAmelCase		)
				model.to(_UpperCAmelCase		)

				assert not model.is_gradient_checkpointing and model.training

				SCREAMING_SNAKE_CASE      :					int    =				model(**_UpperCAmelCase		).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()

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.randn_like(_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					str    =				(out - labels).mean()
				loss.backward()

				# re-instantiate the model now enabling gradient checkpointing
				SCREAMING_SNAKE_CASE      :					str    =				self.model_class(**_UpperCAmelCase		)
				# clone model
				model_a.load_state_dict(model.state_dict()		)
				model_a.to(_UpperCAmelCase		)
				model_a.enable_gradient_checkpointing()

				assert model_a.is_gradient_checkpointing and model_a.training

				SCREAMING_SNAKE_CASE      :					List[Any]    =				model_a(**_UpperCAmelCase		).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()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1e-5		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				dict(model.named_parameters()		)
				SCREAMING_SNAKE_CASE      :					Dict    =				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]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"					,					output_loading_info=_UpperCAmelCase		)
				self.assertIsNotNone(_UpperCAmelCase		)
				self.assertEqual(len(loading_info["missing_keys"]		)					,					0		)

				model.to(_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					str    =				model(**self.dummy_input		)

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






			def 							__UpperCamelCase       (	self							:       int		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
				SCREAMING_SNAKE_CASE      :					int    =				model.to(_UpperCAmelCase		)
				model.eval()

				if torch_device == "mps":
					SCREAMING_SNAKE_CASE      :					Dict    =				torch.manual_seed(0		)
				else:
					SCREAMING_SNAKE_CASE      :					int    =				torch.Generator(device=_UpperCAmelCase		).manual_seed(0		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				torch.randn(
				    1					,					model.config.in_channels					,					model.config.sample_size					,					model.config.sample_size					,					generator=torch.manual_seed(0		)					,					)
				SCREAMING_SNAKE_CASE      :					List[str]    =				image.to(_UpperCAmelCase		)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					int    =				model(_UpperCAmelCase					,					sample_posterior=_UpperCAmelCase					,					generator=_UpperCAmelCase		).sample

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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":
					SCREAMING_SNAKE_CASE      :					Tuple    =				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":
					SCREAMING_SNAKE_CASE      :					Dict    =				torch.tensor(
					    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
				else:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.tensor(
					    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]		)

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






@slow
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Optional[int]					,					a							:       Any		)							->				List[Any]:

				"""simple docstring"""

				return F"gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCAmelCase		) for s in shape]		)}.npy"
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__UpperCamelCase       (	self							:       str					,					a							:       int=0					,					a							:       List[str]=(4, 3, 512, 512)					,					a							:       Dict=False		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				torch.floataa if fpaa else torch.floataa
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.from_numpy(load_hf_numpy(self.get_file_format(_UpperCAmelCase					,					_UpperCAmelCase		)		)		).to(_UpperCAmelCase		).to(_UpperCAmelCase		)
				return image
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Union[str, Any]="CompVis/stable-diffusion-v1-4"					,					a							:       Optional[int]=False		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				'fp16' if fpaa else None
				SCREAMING_SNAKE_CASE      :					Any    =				torch.floataa if fpaa else torch.floataa

				SCREAMING_SNAKE_CASE      :					List[str]    =				AutoencoderKL.from_pretrained(
				    _UpperCAmelCase					,					subfolder="vae"					,					torch_dtype=_UpperCAmelCase					,					revision=_UpperCAmelCase					,					)
				model.to(_UpperCAmelCase		).eval()

				return model
			def 							__UpperCamelCase       (	self							:       str					,					a							:       Any=0		)							->				List[Any]:

				"""simple docstring"""

				if torch_device == "mps":
					return torch.manual_seed(_UpperCAmelCase		)
				return torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]		)
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Dict					,					a							:       Optional[Any]					,					a							:       Any		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_sd_image(_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_generator(_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(_UpperCAmelCase					,					generator=_UpperCAmelCase					,					sample_posterior=_UpperCAmelCase		).sample

				assert sample.shape == image.shape

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					atol=3e-3		)
			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
			        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
			        # fmt: on
			    ]		)
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       Any					,					a							:       str		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_sd_vae_model(fpaa=_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_sd_image(_UpperCAmelCase					,					fpaa=_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_generator(_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model(_UpperCAmelCase					,					generator=_UpperCAmelCase					,					sample_posterior=_UpperCAmelCase		).sample

				assert sample.shape == image.shape

				SCREAMING_SNAKE_CASE      :					List[str]    =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.tensor(_UpperCAmelCase		)

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					atol=1e-2		)
			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       str					,					a							:       Any					,					a							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.get_sd_image(_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Dict    =				model(_UpperCAmelCase		).sample

				assert sample.shape == image.shape

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

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					atol=3e-3		)
			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
			        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
			        # fmt: on
			    ]		)
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       Optional[int]					,					a							:       List[Any]					,					a							:       List[Any]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.get_sd_image(_UpperCAmelCase					,					shape=(3, 4, 64, 64)		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					List[str]    =				model.decode(_UpperCAmelCase		).sample

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

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				sample[-1, -2:, :2, -2:].flatten().cpu()
				SCREAMING_SNAKE_CASE      :					Any    =				torch.tensor(_UpperCAmelCase		)

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					atol=1e-3		)
			@parameterized.expand(
			    [
			        # fmt: off
			        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
			        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
			        # fmt: on
			    ]		)
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       str					,					a							:       Union[str, Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.get_sd_vae_model(fpaa=_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_sd_image(_UpperCAmelCase					,					shape=(3, 4, 64, 64)					,					fpaa=_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					List[Any]    =				model.decode(_UpperCAmelCase		).sample

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

				SCREAMING_SNAKE_CASE      :					Dict    =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.tensor(_UpperCAmelCase		)

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					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							:       int					,					a							:       Union[str, Any]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.get_sd_vae_model(fpaa=_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.get_sd_image(_UpperCAmelCase					,					shape=(3, 4, 64, 64)					,					fpaa=_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model.decode(_UpperCAmelCase		).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Tuple    =				model.decode(_UpperCAmelCase		).sample

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

				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					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							:       Any					,					a							:       Dict		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_sd_image(_UpperCAmelCase					,					shape=(3, 4, 64, 64)		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				model.decode(_UpperCAmelCase		).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model.decode(_UpperCAmelCase		).sample

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

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






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
			        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
			        # fmt: on
			    ]		)
			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       List[Any]					,					a							:       Optional[int]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_sd_vae_model()
				SCREAMING_SNAKE_CASE      :					int    =				self.get_sd_image(_UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.get_generator(_UpperCAmelCase		)

				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Any    =				model.encode(_UpperCAmelCase		).latent_dist
					SCREAMING_SNAKE_CASE      :					List[Any]    =				dist.sample(generator=_UpperCAmelCase		)

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

				SCREAMING_SNAKE_CASE      :					int    =				sample[0, -1, -3:, -3:].flatten().cpu()
				SCREAMING_SNAKE_CASE      :					Any    =				torch.tensor(_UpperCAmelCase		)

				SCREAMING_SNAKE_CASE      :					Any    =				3e-3 if torch_device != 'mps' else 1e-2
				assert torch_all_close(_UpperCAmelCase					,					_UpperCAmelCase					,					atol=_UpperCAmelCase		)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__    :      List[str]						=    {
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    [
      '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GitForCausalLM''',
      '''GitModel''',
      '''GitPreTrainedModel''',
      '''GitVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  lowercase__    :      List[str]						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)