





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 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    AutoencoderKL
      A_						:				List[Any]							=    'sample'
      A_						:				List[Any]							=    1e-2


      @property
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   4
         __snake_case							       =   3
         __snake_case							       =   (32, 32)

         __snake_case							       =   floats_tensor((batch_size, num_channels) + sizes      ).to(a__      )

         return {"sample": image}


      @property
      def    a (self       :      Any      ):





         """simple docstring"""



         return (3, 32, 32)


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         return (3, 32, 32)


      def    a (self       :      int      ):





         """simple docstring"""



         __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       :      Tuple      ):





         """simple docstring"""



         pass


      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skipIf(torch_device == '''mps'''				,			'''Gradient checkpointing skipped on MPS'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.prepare_init_args_and_inputs_for_common()
         __snake_case							       =   self.model_class(**a__      )
         model.to(a__      )

         assert not model.is_gradient_checkpointing and model.training

         __snake_case							       =   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()

         __snake_case							       =   torch.randn_like(a__      )
         __snake_case							       =   (out - labels).mean()
         loss.backward()

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

         __snake_case							       =   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()
         __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       :      Tuple      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   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__      )
         __snake_case							       =   model(**self.dummy_input      )

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


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



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

         if torch_device == "mps":
            __snake_case							       =   torch.manual_seed(0      )
         else:
            __snake_case							       =   torch.Generator(device=a__      ).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(a__      )
         with torch.no_grad():
            __snake_case							       =   model(a__				,			sample_posterior=a__				,			generator=a__      ).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(
                [
                    -4.0078E-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							       =   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:
            __snake_case							       =   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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple				,			a__       :      Optional[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



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


      def    a (self       :      Optional[int]				,			a__       :      Optional[Any]=0				,			a__       :      List[Any]=(4, 3, 512, 512)				,			a__       :      Optional[Any]=False      ):





         """simple docstring"""



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


      def    a (self       :      Optional[Any]				,			a__       :      Dict="CompVis/stable-diffusion-v1-4"				,			a__       :      List[Any]=False      ):





         """simple docstring"""



         __snake_case							       =   '''fp16''' if fpaa else None
         __snake_case							       =   torch.floataa if fpaa else torch.floataa

         __snake_case							       =   AutoencoderKL.from_pretrained(
             a__				,			subfolder='''vae'''				,			torch_dtype=a__				,			revision=a__				,			)
         model.to(a__      ).eval()

         return model


      def    a (self       :      Union[str, Any]				,			a__       :      int=0      ):





         """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    a (self       :      Optional[int]				,			a__       :      int				,			a__       :      Union[str, Any]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model()
         __snake_case							       =   self.get_sd_image(a__      )
         __snake_case							       =   self.get_generator(a__      )

         with torch.no_grad():
            __snake_case							       =   model(a__				,			generator=a__				,			sample_posterior=a__      ).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(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    a (self       :      Union[str, Any]				,			a__       :      Union[str, Any]				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model(fpaa=a__      )
         __snake_case							       =   self.get_sd_image(a__				,			fpaa=a__      )
         __snake_case							       =   self.get_generator(a__      )

         with torch.no_grad():
            __snake_case							       =   model(a__				,			generator=a__				,			sample_posterior=a__      ).sample

         assert sample.shape == image.shape

         __snake_case							       =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __snake_case							       =   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    a (self       :      Optional[Any]				,			a__       :      str				,			a__       :      Tuple				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model()
         __snake_case							       =   self.get_sd_image(a__      )

         with torch.no_grad():
            __snake_case							       =   model(a__      ).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(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    a (self       :      str				,			a__       :      Optional[int]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model()
         __snake_case							       =   self.get_sd_image(a__				,			shape=(3, 4, 64, 64)      )

         with torch.no_grad():
            __snake_case							       =   model.decode(a__      ).sample

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

         __snake_case							       =   sample[-1, -2:, :2, -2:].flatten().cpu()
         __snake_case							       =   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    a (self       :      List[Any]				,			a__       :      Any				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model(fpaa=a__      )
         __snake_case							       =   self.get_sd_image(a__				,			shape=(3, 4, 64, 64)				,			fpaa=a__      )

         with torch.no_grad():
            __snake_case							       =   model.decode(a__      ).sample

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

         __snake_case							       =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __snake_case							       =   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    a (self       :      Tuple				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model(fpaa=a__      )
         __snake_case							       =   self.get_sd_image(a__				,			shape=(3, 4, 64, 64)				,			fpaa=a__      )

         with torch.no_grad():
            __snake_case							       =   model.decode(a__      ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            __snake_case							       =   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    a (self       :      int				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model()
         __snake_case							       =   self.get_sd_image(a__				,			shape=(3, 4, 64, 64)      )

         with torch.no_grad():
            __snake_case							       =   model.decode(a__      ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            __snake_case							       =   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    a (self       :      str				,			a__       :      Optional[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   self.get_sd_vae_model()
         __snake_case							       =   self.get_sd_image(a__      )
         __snake_case							       =   self.get_generator(a__      )

         with torch.no_grad():
            __snake_case							       =   model.encode(a__      ).latent_dist
            __snake_case							       =   dist.sample(generator=a__      )

         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(a__      )

         __snake_case							       =   3E-3 if torch_device != '''mps''' else 1E-2
         assert torch_all_close(a__				,			a__				,			atol=a__      )








import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

