






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 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







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





       @property
       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							4
         UpperCamelCase_:   Any        =							3
         UpperCamelCase_:   str        =							(3_2, 3_2)

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

         return {"sample": image}





       @property
       def      _a       (   self				):
         return (3, 3_2, 3_2)





       @property
       def      _a       (   self				):
         return (3, 3_2, 3_2)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							{
             'block_out_channels': [3_2, 6_4],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 4,
         }
         UpperCamelCase_:   Optional[Any]        =							self.dummy_input
         return init_dict, inputs_dict





       def      _a       (   self				):
         pass





       def      _a       (   self				):
         pass





       @unittest.skipIf(torch_device == 'mps'    ,     'Gradient checkpointing skipped on MPS'				)
       def      _a       (   self				):
         # enable deterministic behavior for gradient checkpointing
         UpperCamelCase_ ,UpperCamelCase_:   Any        =							self.prepare_init_args_and_inputs_for_common()
         UpperCamelCase_:   int        =							self.model_class(**_lowerCamelCase				)
         model.to(_lowerCamelCase				)

         assert not model.is_gradient_checkpointing and model.training

         UpperCamelCase_:   List[str]        =							model(**_lowerCamelCase				).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()

         UpperCamelCase_:   Dict        =							torch.randn_like(_lowerCamelCase				)
         UpperCamelCase_:   str        =							(out - labels).mean()
         loss.backward()

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

         assert model_a.is_gradient_checkpointing and model_a.training

         UpperCamelCase_:   int        =							model_a(**_lowerCamelCase				).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()
         UpperCamelCase_:   Optional[int]        =							(out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1e-5				)
         UpperCamelCase_:   Optional[Any]        =							dict(model.named_parameters()				)
         UpperCamelCase_:   List[str]        =							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				):
         UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'    ,     output_loading_info=_lowerCamelCase				)
         self.assertIsNotNone(_lowerCamelCase				)
         self.assertEqual(len(loading_info['missing_keys']				)    ,     0				)

         model.to(_lowerCamelCase				)
         UpperCamelCase_:   Any        =							model(**self.dummy_input				)

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





       def      _a       (   self				):
         UpperCamelCase_:   str        =							AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'				)
         UpperCamelCase_:   Any        =							model.to(_lowerCamelCase				)
         model.eval()

         if torch_device == "mps":
           UpperCamelCase_:   Optional[Any]        =							torch.manual_seed(0				)
         else:
           UpperCamelCase_:   List[Any]        =							torch.Generator(device=_lowerCamelCase				).manual_seed(0				)

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

         UpperCamelCase_:   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":
           UpperCamelCase_:   List[str]        =							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":
           UpperCamelCase_:   List[str]        =							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:
           UpperCamelCase_:   int        =							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(_lowerCamelCase    ,     _lowerCamelCase    ,     rtol=1e-2				)				)







@slow
class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCamelCase				) for s in shape]				)}.npy'''





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





       def      _a       (   self    ,     _lowerCamelCase=0    ,     _lowerCamelCase=(4, 3, 5_1_2, 5_1_2)    ,     _lowerCamelCase=False				):
         UpperCamelCase_:   Optional[Any]        =							torch.floataa if fpaa else torch.floataa
         UpperCamelCase_:   Any        =							torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCamelCase    ,     _lowerCamelCase				)				)				).to(_lowerCamelCase				).to(_lowerCamelCase				)
         return image





       def      _a       (   self    ,     _lowerCamelCase="CompVis/stable-diffusion-v1-4"    ,     _lowerCamelCase=False				):
         UpperCamelCase_:   List[str]        =							'fp16' if fpaa else None
         UpperCamelCase_:   int        =							torch.floataa if fpaa else torch.floataa

         UpperCamelCase_:   Optional[Any]        =							AutoencoderKL.from_pretrained(
             _lowerCamelCase    ,     subfolder='vae'    ,     torch_dtype=_lowerCamelCase    ,     revision=_lowerCamelCase    ,     )
         model.to(_lowerCamelCase				).eval()

         return model





       def      _a       (   self    ,     _lowerCamelCase=0				):
         if torch_device == "mps":
           return torch.manual_seed(_lowerCamelCase				)
         return torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)





       @parameterized.expand(
           [
               # fmt: off
               [3_3, [-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]],
               [4_7, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Any        =							self.get_sd_vae_model()
         UpperCamelCase_:   str        =							self.get_sd_image(_lowerCamelCase				)
         UpperCamelCase_:   int        =							self.get_generator(_lowerCamelCase				)

         with torch.no_grad():
           UpperCamelCase_:   List[str]        =							model(_lowerCamelCase    ,     generator=_lowerCamelCase    ,     sample_posterior=_lowerCamelCase				).sample

         assert sample.shape == image.shape

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

         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=3e-3				)





       @parameterized.expand(
           [
               # fmt: off
               [3_3, [-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]],
               [4_7, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[int]        =							self.get_sd_vae_model(fpaa=_lowerCamelCase				)
         UpperCamelCase_:   str        =							self.get_sd_image(_lowerCamelCase    ,     fpaa=_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							self.get_generator(_lowerCamelCase				)

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

         assert sample.shape == image.shape

         UpperCamelCase_:   str        =							sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCamelCase_:   Any        =							torch.tensor(_lowerCamelCase				)

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





       @parameterized.expand(
           [
               # fmt: off
               [3_3, [-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]],
               [4_7, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Dict        =							self.get_sd_vae_model()
         UpperCamelCase_:   Union[str, Any]        =							self.get_sd_image(_lowerCamelCase				)

         with torch.no_grad():
           UpperCamelCase_:   Optional[Any]        =							model(_lowerCamelCase				).sample

         assert sample.shape == image.shape

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

         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=3e-3				)





       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-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]],
               [3_7, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.get_sd_vae_model()
         UpperCamelCase_:   Dict        =							self.get_sd_image(_lowerCamelCase    ,     shape=(3, 4, 6_4, 6_4)				)

         with torch.no_grad():
           UpperCamelCase_:   List[str]        =							model.decode(_lowerCamelCase				).sample

         assert list(sample.shape				) == [3, 3, 5_1_2, 5_1_2]

         UpperCamelCase_:   Any        =							sample[-1, -2:, :2, -2:].flatten().cpu()
         UpperCamelCase_:   Optional[Any]        =							torch.tensor(_lowerCamelCase				)

         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1e-3				)





       @parameterized.expand(
           [
               # fmt: off
               [2_7, [-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]],
               [1_6, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.get_sd_vae_model(fpaa=_lowerCamelCase				)
         UpperCamelCase_:   int        =							self.get_sd_image(_lowerCamelCase    ,     shape=(3, 4, 6_4, 6_4)    ,     fpaa=_lowerCamelCase				)

         with torch.no_grad():
           UpperCamelCase_:   Tuple        =							model.decode(_lowerCamelCase				).sample

         assert list(sample.shape				) == [3, 3, 5_1_2, 5_1_2]

         UpperCamelCase_:   Any        =							sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCamelCase_:   Tuple        =							torch.tensor(_lowerCamelCase				)

         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=5e-3				)





       @parameterized.expand([(1_3,), (1_6,), (2_7,)]				)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()    ,     reason='xformers is not required when using PyTorch 2.0.'				)
       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.get_sd_vae_model(fpaa=_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							self.get_sd_image(_lowerCamelCase    ,     shape=(3, 4, 6_4, 6_4)    ,     fpaa=_lowerCamelCase				)

         with torch.no_grad():
           UpperCamelCase_:   Optional[int]        =							model.decode(_lowerCamelCase				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
           UpperCamelCase_:   Tuple        =							model.decode(_lowerCamelCase				).sample

         assert list(sample.shape				) == [3, 3, 5_1_2, 5_1_2]

         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1e-1				)





       @parameterized.expand([(1_3,), (1_6,), (3_7,)]				)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()    ,     reason='xformers is not required when using PyTorch 2.0.'				)
       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.get_sd_vae_model()
         UpperCamelCase_:   Dict        =							self.get_sd_image(_lowerCamelCase    ,     shape=(3, 4, 6_4, 6_4)				)

         with torch.no_grad():
           UpperCamelCase_:   Optional[int]        =							model.decode(_lowerCamelCase				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
           UpperCamelCase_:   int        =							model.decode(_lowerCamelCase				).sample

         assert list(sample.shape				) == [3, 3, 5_1_2, 5_1_2]

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





       @parameterized.expand(
           [
               # fmt: off
               [3_3, [-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]],
               [4_7, [-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    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.get_sd_vae_model()
         UpperCamelCase_:   int        =							self.get_sd_image(_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							self.get_generator(_lowerCamelCase				)

         with torch.no_grad():
           UpperCamelCase_:   str        =							model.encode(_lowerCamelCase				).latent_dist
           UpperCamelCase_:   Union[str, Any]        =							dist.sample(generator=_lowerCamelCase				)

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

         UpperCamelCase_:   Any        =							sample[0, -1, -3:, -3:].flatten().cpu()
         UpperCamelCase_:   Optional[int]        =							torch.tensor(_lowerCamelCase				)

         UpperCamelCase_:   int        =							3e-3 if torch_device != 'mps' else 1e-2
         assert torch_all_close(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=_lowerCamelCase				)
"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



