



'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			(DPMSolverSDEScheduler,)
 __lowerCamelCase       :			Union[str, Any]		=			10



 def    a__    (self					,  **A			)       ->	List[str]:


      """simple docstring"""



      _a           =    {
          '''num_train_timesteps''': 1_100,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''noise_sampler_seed''': 0,
      }

      config.update(**A			)
      return config



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=A			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,  [0.0002, 0.002, 0.02]			):
           self.check_over_configs(beta_start=A					,  beta_end=A			)



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


      """simple docstring"""



      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.scheduler_classes[0]
      _a           =    self.get_scheduler_config()
      _a           =    scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      _a           =    self.dummy_model()
      _a           =    self.dummy_sample_deter * scheduler.init_noise_sigma
      _a           =    sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
           _a           =    scheduler.scale_model_input(A					,  A			)

           _a           =    model(A					,  A			)

           _a           =    scheduler.step(A					,  A					,  A			)
           _a           =    output.prev_sample

      _a           =    torch.sum(torch.abs(A			)			)
      _a           =    torch.mean(torch.abs(A			)			)

      if torch_device in ["mps"]:
           assert abs(result_sum.item() - 167.47821044921875			) < 1E-2
           assert abs(result_mean.item() - 0.2178705964565277			) < 1E-3
      elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 171.59352111816406			) < 1E-2
           assert abs(result_mean.item() - 0.22342906892299652			) < 1E-3
      else:
           assert abs(result_sum.item() - 162.52383422851562			) < 1E-2
           assert abs(result_mean.item() - 0.211619570851326			) < 1E-3



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.scheduler_classes[0]
      _a           =    self.get_scheduler_config(prediction_type='''v_prediction'''			)
      _a           =    scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      _a           =    self.dummy_model()
      _a           =    self.dummy_sample_deter * scheduler.init_noise_sigma
      _a           =    sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
           _a           =    scheduler.scale_model_input(A					,  A			)

           _a           =    model(A					,  A			)

           _a           =    scheduler.step(A					,  A					,  A			)
           _a           =    output.prev_sample

      _a           =    torch.sum(torch.abs(A			)			)
      _a           =    torch.mean(torch.abs(A			)			)

      if torch_device in ["mps"]:
           assert abs(result_sum.item() - 124.77149200439453			) < 1E-2
           assert abs(result_mean.item() - 0.16226289014816284			) < 1E-3
      elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 128.1663360595703			) < 1E-2
           assert abs(result_mean.item() - 0.16688326001167297			) < 1E-3
      else:
           assert abs(result_sum.item() - 119.8487548828125			) < 1E-2
           assert abs(result_mean.item() - 0.1560530662536621			) < 1E-3



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.scheduler_classes[0]
      _a           =    self.get_scheduler_config()
      _a           =    scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps					,  device=A			)

      _a           =    self.dummy_model()
      _a           =    self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
           _a           =    scheduler.scale_model_input(A					,  A			)

           _a           =    model(A					,  A			)

           _a           =    scheduler.step(A					,  A					,  A			)
           _a           =    output.prev_sample

      _a           =    torch.sum(torch.abs(A			)			)
      _a           =    torch.mean(torch.abs(A			)			)

      if torch_device in ["mps"]:
           assert abs(result_sum.item() - 167.46957397460938			) < 1E-2
           assert abs(result_mean.item() - 0.21805934607982635			) < 1E-3
      elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 171.59353637695312			) < 1E-2
           assert abs(result_mean.item() - 0.22342908382415771			) < 1E-3
      else:
           assert abs(result_sum.item() - 162.52383422851562			) < 1E-2
           assert abs(result_mean.item() - 0.211619570851326			) < 1E-3



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.scheduler_classes[0]
      _a           =    self.get_scheduler_config()
      _a           =    scheduler_class(**A					,  use_karras_sigmas=A			)

      scheduler.set_timesteps(self.num_inference_steps					,  device=A			)

      _a           =    self.dummy_model()
      _a           =    self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      _a           =    sample.to(A			)

      for t in scheduler.timesteps:
           _a           =    scheduler.scale_model_input(A					,  A			)

           _a           =    model(A					,  A			)

           _a           =    scheduler.step(A					,  A					,  A			)
           _a           =    output.prev_sample

      _a           =    torch.sum(torch.abs(A			)			)
      _a           =    torch.mean(torch.abs(A			)			)

      if torch_device in ["mps"]:
           assert abs(result_sum.item() - 176.66974135742188			) < 1E-2
           assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2
      elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 177.63653564453125			) < 1E-2
           assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2
      else:
           assert abs(result_sum.item() - 170.3135223388672			) < 1E-2
           assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2








'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



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


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



