
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					):
   A__   :						Dict			=	AutoencoderKL
   A__   :						Dict			=	"sample"
   A__   :						Any			=	1e-2



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 4
          snake_case			:  int     							= 3
          snake_case			:  List[Any]     							= (32, 32)

          snake_case			:  Any     							= floats_tensor((batch_size, num_channels) + sizes					).to(snake_case__					)

          return {"sample": image}



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      List[str]:


          '''simple docstring'''


          return (3, 32, 32)



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


          '''simple docstring'''


          return (3, 32, 32)



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


          '''simple docstring'''


          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			:  str     							= self.dummy_input
          return init_dict, inputs_dict



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          snake_case							,   snake_case			:  Dict     							= self.prepare_init_args_and_inputs_for_common()
          snake_case			:  List[str]     							= self.model_class(**snake_case__					)
          model.to(snake_case__					)

          assert not model.is_gradient_checkpointing and model.training

          snake_case			:  Union[str, Any]     							= 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			:  Any     							= torch.randn_like(snake_case__					)
          snake_case			:  Optional[Any]     							= (out - labels).mean()
          loss.backward()

          # re-instantiate the model now enabling gradient checkpointing
          snake_case			:  List[Any]     							= 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			:  Union[str, Any]     							= 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			:  List[str]     							= (out_a - labels).mean()
          loss_a.backward()

          # compare the output and parameters gradients
          self.assertTrue((loss - loss_a).abs() < 1e-5					)
          snake_case			:  Union[str, Any]     							= dict(model.named_parameters()					)
          snake_case			:  Optional[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 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[int]     							= 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			:  Optional[int]     							= model(**self.dummy_input					)

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[str]     							= AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"					)
          snake_case			:  str     							= model.to(snake_case__					)
          model.eval()

          if torch_device == "mps":
                 snake_case			:  Any     							= torch.manual_seed(0					)
          else:
                 snake_case			:  int     							= torch.Generator(device=snake_case__					).manual_seed(0					)

          snake_case			:  Tuple     							= 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(snake_case__					)
          with torch.no_grad():
                 snake_case			:  Any     							= model(snake_case__       ,		sample_posterior=snake_case__       ,		generator=snake_case__					).sample

          snake_case			:  str     							= 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			:  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":
                 snake_case			:  Optional[int]     							= torch.tensor(
                     [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]					)
          else:
                 snake_case			:  str     							= 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(snake_case__       ,		snake_case__       ,		rtol=1e-2					)					)






@slow
class     UpperCAmelCase						(		unittest.TestCase					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   Any					)						->      Any:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Union[str, Any]:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str=0       ,		snake_case__						:   Any=(4, 3, 5_12, 5_12)       ,		snake_case__						:   Optional[int]=False					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= torch.floataa if fpaa else torch.floataa
          snake_case			:  Optional[int]     							= torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case__       ,		snake_case__					)					)					).to(snake_case__					).to(snake_case__					)
          return image



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   Union[str, Any]="CompVis/stable-diffusion-v1-4"       ,		snake_case__						:   Optional[int]=False					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= "fp16" if fpaa else None
          snake_case			:  Any     							= torch.floataa if fpaa else torch.floataa

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

          return model



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   Tuple=0					)						->      Optional[int]:


          '''simple docstring'''


          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, [-0.1603, 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 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Tuple       ,		snake_case__						:   Union[str, Any]					)						->      str:


          '''simple docstring'''


          snake_case			:  int     							= self.get_sd_vae_model()
          snake_case			:  List[str]     							= self.get_sd_image(snake_case__					)
          snake_case			:  Dict     							= self.get_generator(snake_case__					)

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

          assert sample.shape == image.shape

          snake_case			:  Tuple     							= 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(snake_case__       ,		snake_case__       ,		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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Dict       ,		snake_case__						:   List[str]					)						->      List[Any]:


          '''simple docstring'''


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

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

          assert sample.shape == image.shape

          snake_case			:  Dict     							= sample[-1, -2:, :2, -2:].flatten().float().cpu()
          snake_case			:  Union[str, Any]     							= torch.tensor(snake_case__					)

          assert torch_all_close(snake_case__       ,		snake_case__       ,		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 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= self.get_sd_vae_model()
          snake_case			:  Any     							= self.get_sd_image(snake_case__					)

          with torch.no_grad():
                 snake_case			:  Dict     							= model(snake_case__					).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(snake_case__       ,		snake_case__       ,		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 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.get_sd_vae_model()
          snake_case			:  int     							= self.get_sd_image(snake_case__       ,		shape=(3, 4, 64, 64)					)

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

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

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

          assert torch_all_close(snake_case__       ,		snake_case__       ,		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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Tuple       ,		snake_case__						:   Optional[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Dict     							= self.get_sd_vae_model(fpaa=snake_case__					)
          snake_case			:  int     							= self.get_sd_image(snake_case__       ,		shape=(3, 4, 64, 64)       ,		fpaa=snake_case__					)

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

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

          snake_case			:  Any     							= sample[-1, -2:, :2, -2:].flatten().float().cpu()
          snake_case			:  int     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   Tuple					)						->      str:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.get_sd_vae_model(fpaa=snake_case__					)
          snake_case			:  Optional[int]     							= self.get_sd_image(snake_case__       ,		shape=(3, 4, 64, 64)       ,		fpaa=snake_case__					)

          with torch.no_grad():
                 snake_case			:  List[Any]     							= model.decode(snake_case__					).sample

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
                 snake_case			:  Optional[int]     							= model.decode(snake_case__					).sample

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

          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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Union[str, Any]					)						->      Tuple:


          '''simple docstring'''


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

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

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
                 snake_case			:  List[str]     							= model.decode(snake_case__					).sample

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

          assert torch_all_close(snake_case__       ,		snake_case__       ,		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 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   Any       ,		snake_case__						:   int					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Any     							= self.get_sd_vae_model()
          snake_case			:  str     							= self.get_sd_image(snake_case__					)
          snake_case			:  Union[str, Any]     							= self.get_generator(snake_case__					)

          with torch.no_grad():
                 snake_case			:  List[str]     							= model.encode(snake_case__					).latent_dist
                 snake_case			:  Optional[int]     							= dist.sample(generator=snake_case__					)

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

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

          snake_case			:  Optional[int]     							= 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, is_vision_available


__lowerCamelCase				=			{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase				=			["""ChineseCLIPFeatureExtractor"""]
   __lowerCamelCase				=			["""ChineseCLIPImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase				=			[
       """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ChineseCLIPModel""",
       """ChineseCLIPPreTrainedModel""",
       """ChineseCLIPTextModel""",
       """ChineseCLIPVisionModel""",
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

   __lowerCamelCase				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





