




'''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 					_a (UpperCamelCase__,		UpperCamelCase__,		unittest.TestCase	):





     '''simple docstring'''



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







     @property
     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      List[Any]        =			4
          snake_case			:      Any        =			3
          snake_case			:      Dict        =			(32, 32)

          snake_case			:      Optional[Any]        =			floats_tensor((batch_size, num_channels) + sizes							).to(lowercase_							)

          return {"sample": image}







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







     @property
     def 			snake_case_      (				self							)      ->				Optional[int]:
          return (3, 32, 32)







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      Optional[Any]        =			{
              """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			:      Optional[Any]        =			self.dummy_input
          return init_dict, inputs_dict







     def 			snake_case_      (				self							)      ->				Any:
          pass







     def 			snake_case_      (				self							)      ->				Optional[int]:
          pass







     @unittest.skipIf(torch_device == """mps"""							,"""Gradient checkpointing skipped on MPS"""							)
     def 			snake_case_      (				self							)      ->				Optional[int]:
          # enable deterministic behavior for gradient checkpointing
          snake_case ,		snake_case			:      Tuple        =			self.prepare_init_args_and_inputs_for_common()
          snake_case			:      int        =			self.model_class(**lowercase_							)
          model.to(lowercase_							)

          assert not model.is_gradient_checkpointing and model.training

          snake_case			:      int        =			model(**lowercase_							).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			:      Any        =			torch.randn_like(lowercase_							)
          snake_case			:      Union[str, Any]        =			(out - labels).mean()
          loss.backward()

          # re-instantiate the model now enabling gradient checkpointing
          snake_case			:      Optional[Any]        =			self.model_class(**lowercase_							)
          # clone model
          model_a.load_state_dict(model.state_dict()							)
          model_a.to(lowercase_							)
          model_a.enable_gradient_checkpointing()

          assert model_a.is_gradient_checkpointing and model_a.training

          snake_case			:      int        =			model_a(**lowercase_							).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			:      Union[str, Any]        =			(out_a - labels).mean()
          loss_a.backward()

          # compare the output and parameters gradients
          self.assertTrue((loss - loss_a).abs() < 1E-5							)
          snake_case			:      List[Any]        =			dict(model.named_parameters()							)
          snake_case			:      Union[str, Any]        =			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 			snake_case_      (				self							)      ->				Tuple:
          snake_case ,		snake_case			:      List[Any]        =			AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""							,output_loading_info=lowercase_							)
          self.assertIsNotNone(lowercase_							)
          self.assertEqual(len(loading_info["""missing_keys"""]							)							,0							)

          model.to(lowercase_							)
          snake_case			:      Dict        =			model(**self.dummy_input							)

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







     def 			snake_case_      (				self							)      ->				Optional[int]:
          snake_case			:      Union[str, Any]        =			AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""							)
          snake_case			:      Dict        =			model.to(lowercase_							)
          model.eval()

          if torch_device == "mps":
               snake_case			:      int        =			torch.manual_seed(0							)
          else:
               snake_case			:      Optional[int]        =			torch.Generator(device=lowercase_							).manual_seed(0							)

          snake_case			:      int        =			torch.randn(
              1							,model.config.in_channels							,model.config.sample_size							,model.config.sample_size							,generator=torch.manual_seed(0							)							,)
          snake_case			:      Any        =			image.to(lowercase_							)
          with torch.no_grad():
               snake_case			:      Optional[Any]        =			model(lowercase_							,sample_posterior=lowercase_							,generator=lowercase_							).sample

          snake_case			:      Tuple        =			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			:      int        =			torch.tensor(
                   [
                       -4.0078E-01,
                       -3.8323E-04,
                       -1.2681E-01,
                       -1.1462E-01,
                       2.0095E-01,
                       1.0893E-01,
                       -8.8247E-02,
                       -3.0361E-01,
                       -9.8644E-03,
                   ]							)
          elif torch_device == "cpu":
               snake_case			:      Optional[Any]        =			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:
               snake_case			:      Tuple        =			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(lowercase_							,lowercase_							,rtol=1E-2							)							)







@slow
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          return F'''gaussian_noise_s={seed}_shape={"_".join([str(lowercase_							) for s in shape]							)}.npy'''







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







     def 			snake_case_      (				self							,__a=0							,__a=(4, 3, 512, 512)							,__a=False							)      ->				Tuple:
          snake_case			:      Union[str, Any]        =			torch.floataa if fpaa else torch.floataa
          snake_case			:      Dict        =			torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_							,lowercase_							)							)							).to(lowercase_							).to(lowercase_							)
          return image







     def 			snake_case_      (				self							,__a="CompVis/stable-diffusion-v1-4"							,__a=False							)      ->				Tuple:
          snake_case			:      List[Any]        =			"""fp16""" if fpaa else None
          snake_case			:      int        =			torch.floataa if fpaa else torch.floataa

          snake_case			:      Any        =			AutoencoderKL.from_pretrained(
              lowercase_							,subfolder="""vae"""							,torch_dtype=lowercase_							,revision=lowercase_							,)
          model.to(lowercase_							).eval()

          return model







     def 			snake_case_      (				self							,__a=0							)      ->				Union[str, Any]:
          if torch_device == "mps":
               return torch.manual_seed(lowercase_							)
          return torch.Generator(device=lowercase_							).manual_seed(lowercase_							)







     @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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Any:
          snake_case			:      Tuple        =			self.get_sd_vae_model()
          snake_case			:      Tuple        =			self.get_sd_image(lowercase_							)
          snake_case			:      str        =			self.get_generator(lowercase_							)

          with torch.no_grad():
               snake_case			:      List[Any]        =			model(lowercase_							,generator=lowercase_							,sample_posterior=lowercase_							).sample

          assert sample.shape == image.shape

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

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							,__a							)      ->				List[str]:
          snake_case			:      Optional[Any]        =			self.get_sd_vae_model(fpaa=lowercase_							)
          snake_case			:      Dict        =			self.get_sd_image(lowercase_							,fpaa=lowercase_							)
          snake_case			:      Optional[Any]        =			self.get_generator(lowercase_							)

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

          assert sample.shape == image.shape

          snake_case			:      Optional[Any]        =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
          snake_case			:      int        =			torch.tensor(lowercase_							)

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				int:
          snake_case			:      List[str]        =			self.get_sd_vae_model()
          snake_case			:      Union[str, Any]        =			self.get_sd_image(lowercase_							)

          with torch.no_grad():
               snake_case			:      str        =			model(lowercase_							).sample

          assert sample.shape == image.shape

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

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							,__a							)      ->				Dict:
          snake_case			:      Dict        =			self.get_sd_vae_model()
          snake_case			:      List[str]        =			self.get_sd_image(lowercase_							,shape=(3, 4, 64, 64)							)

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

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

          snake_case			:      Optional[int]        =			sample[-1, -2:, :2, -2:].flatten().cpu()
          snake_case			:      List[Any]        =			torch.tensor(lowercase_							)

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							,__a							)      ->				Dict:
          snake_case			:      str        =			self.get_sd_vae_model(fpaa=lowercase_							)
          snake_case			:      Union[str, Any]        =			self.get_sd_image(lowercase_							,shape=(3, 4, 64, 64)							,fpaa=lowercase_							)

          with torch.no_grad():
               snake_case			:      Optional[Any]        =			model.decode(lowercase_							).sample

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

          snake_case			:      str        =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
          snake_case			:      Optional[int]        =			torch.tensor(lowercase_							)

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							)      ->				List[Any]:
          snake_case			:      str        =			self.get_sd_vae_model(fpaa=lowercase_							)
          snake_case			:      Union[str, Any]        =			self.get_sd_image(lowercase_							,shape=(3, 4, 64, 64)							,fpaa=lowercase_							)

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

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
               snake_case			:      Union[str, Any]        =			model.decode(lowercase_							).sample

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

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							)      ->				Tuple:
          snake_case			:      Optional[Any]        =			self.get_sd_vae_model()
          snake_case			:      Tuple        =			self.get_sd_image(lowercase_							,shape=(3, 4, 64, 64)							)

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

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

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

          assert torch_all_close(lowercase_							,lowercase_							,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 			snake_case_      (				self							,__a							,__a							)      ->				Any:
          snake_case			:      List[str]        =			self.get_sd_vae_model()
          snake_case			:      List[str]        =			self.get_sd_image(lowercase_							)
          snake_case			:      List[Any]        =			self.get_generator(lowercase_							)

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

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

          snake_case			:      Dict        =			sample[0, -1, -3:, -3:].flatten().cpu()
          snake_case			:      Dict        =			torch.tensor(lowercase_							)

          snake_case			:      Any        =			3E-3 if torch_device != """mps""" else 1E-2
          assert torch_all_close(lowercase_							,lowercase_							,atol=lowercase_							)










'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase				:      Optional[int]						      ={
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =["VivitImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "VivitModel",
      "VivitPreTrainedModel",
      "VivitForVideoClassification",
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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



