




"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				lowerCamelCase		(			a__					):
   lowercase							:	List[Any]  =					(DDIMParallelScheduler,)
   lowercase							:	Optional[Any]  =					(('eta', 0.0), ('num_inference_steps', 5_0))







   def    a_				(				self     ,   **SCREAMING_SNAKE_CASE_			):
       UpperCamelCase					: Optional[Any] 							=				{
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """clip_sample""": True,
       }

       config.update(**_snake_case			)
       return config







   def    a_				(				self     ,   **SCREAMING_SNAKE_CASE_			):
       UpperCamelCase					: List[str] 							=				self.scheduler_classes[0]
       UpperCamelCase					: Any 							=				self.get_scheduler_config(**_snake_case			)
       UpperCamelCase					: Any 							=				scheduler_class(**_snake_case			)

       UpperCamelCase							,				UpperCamelCase					: Dict 							=				10, 0.0

       UpperCamelCase					: Optional[Any] 							=				self.dummy_model()
       UpperCamelCase					: List[str] 							=				self.dummy_sample_deter

       scheduler.set_timesteps(_snake_case			)

       for t in scheduler.timesteps:
           UpperCamelCase					: Union[str, Any] 							=				model(_snake_case     ,   _snake_case			)
           UpperCamelCase					: str 							=				scheduler.step(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case			).prev_sample

       return sample







   def    a_				(				self			):






       for timesteps in [100, 500, 1000]:
           self.check_over_configs(num_train_timesteps=_snake_case			)







   def    a_				(				self			):






       for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=_snake_case			)

       UpperCamelCase					: str 							=				self.scheduler_classes[0]
       UpperCamelCase					: List[str] 							=				self.get_scheduler_config(steps_offset=1			)
       UpperCamelCase					: Tuple 							=				scheduler_class(**_snake_case			)
       scheduler.set_timesteps(5			)
       assert torch.equal(scheduler.timesteps     ,   torch.LongTensor([801, 601, 401, 201, 1]			)			)







   def    a_				(				self			):






       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,   [0.002, 0.02, 0.2, 2]			):
           self.check_over_configs(beta_start=_snake_case     ,   beta_end=_snake_case			)







   def    a_				(				self			):






       for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_snake_case			)







   def    a_				(				self			):






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







   def    a_				(				self			):






       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_snake_case			)







   def    a_				(				self			):






       for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=_snake_case			)







   def    a_				(				self			):






       for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=_snake_case			)







   def    a_				(				self			):
       self.check_over_configs(thresholding=_snake_case			)
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=_snake_case     ,   prediction_type=_snake_case     ,   sample_max_value=_snake_case     ,   )







   def    a_				(				self			):






       for t in [1, 10, 49]:
           self.check_over_forward(time_step=_snake_case			)







   def    a_				(				self			):






       for t, num_inference_steps in zip([1, 10, 50]     ,   [10, 50, 500]			):
           self.check_over_forward(time_step=_snake_case     ,   num_inference_steps=_snake_case			)







   def    a_				(				self			):






       for t, eta in zip([1, 10, 49]     ,   [0.0, 0.5, 1.0]			):
           self.check_over_forward(time_step=_snake_case     ,   eta=_snake_case			)







   def    a_				(				self			):
       UpperCamelCase					: int 							=				self.scheduler_classes[0]
       UpperCamelCase					: str 							=				self.get_scheduler_config()
       UpperCamelCase					: Optional[int] 							=				scheduler_class(**_snake_case			)

       assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0			) - 0.0			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420     ,   400			) - 0.14771			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980     ,   960			) - 0.32460			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0			) - 0.0			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487     ,   486			) - 0.00979			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999     ,   998			) - 0.02			)			) < 1e-5







   def    a_				(				self			):
       UpperCamelCase					: str 							=				self.scheduler_classes[0]
       UpperCamelCase					: Union[str, Any] 							=				self.get_scheduler_config()
       UpperCamelCase					: Any 							=				scheduler_class(**_snake_case			)

       UpperCamelCase							,				UpperCamelCase					: List[str] 							=				10, 0.0
       scheduler.set_timesteps(_snake_case			)

       UpperCamelCase					: Union[str, Any] 							=				self.dummy_model()
       UpperCamelCase					: Any 							=				self.dummy_sample_deter
       UpperCamelCase					: Union[str, Any] 							=				self.dummy_sample_deter + 0.1
       UpperCamelCase					: List[str] 							=				self.dummy_sample_deter - 0.1

       UpperCamelCase					: Any 							=				samplea.shape[0]
       UpperCamelCase					: Optional[int] 							=				torch.stack([samplea, samplea, samplea]     ,   dim=0			)
       UpperCamelCase					: Union[str, Any] 							=				torch.arange(_snake_case			)[0:3, None].repeat(1     ,   _snake_case			)

       UpperCamelCase					: Optional[Any] 							=				model(samples.flatten(0     ,   1			)     ,   timesteps.flatten(0     ,   1			)			)
       UpperCamelCase					: Union[str, Any] 							=				scheduler.batch_step_no_noise(_snake_case     ,   timesteps.flatten(0     ,   1			)     ,   samples.flatten(0     ,   1			)     ,   _snake_case			)

       UpperCamelCase					: int 							=				torch.sum(torch.abs(_snake_case			)			)
       UpperCamelCase					: Dict 							=				torch.mean(torch.abs(_snake_case			)			)

       assert abs(result_sum.item() - 1147.7904			) < 1e-2
       assert abs(result_mean.item() - 0.4982			) < 1e-3







   def    a_				(				self			):
       UpperCamelCase					: Any 							=				self.full_loop()

       UpperCamelCase					: Dict 							=				torch.sum(torch.abs(_snake_case			)			)
       UpperCamelCase					: List[str] 							=				torch.mean(torch.abs(_snake_case			)			)

       assert abs(result_sum.item() - 172.0067			) < 1e-2
       assert abs(result_mean.item() - 0.223967			) < 1e-3







   def    a_				(				self			):
       UpperCamelCase					: List[Any] 							=				self.full_loop(prediction_type="""v_prediction"""			)

       UpperCamelCase					: Optional[Any] 							=				torch.sum(torch.abs(_snake_case			)			)
       UpperCamelCase					: Optional[Any] 							=				torch.mean(torch.abs(_snake_case			)			)

       assert abs(result_sum.item() - 52.5302			) < 1e-2
       assert abs(result_mean.item() - 0.0684			) < 1e-3







   def    a_				(				self			):
       UpperCamelCase					: str 							=				self.full_loop(set_alpha_to_one=_snake_case     ,   beta_start=0.01			)
       UpperCamelCase					: List[Any] 							=				torch.sum(torch.abs(_snake_case			)			)
       UpperCamelCase					: List[Any] 							=				torch.mean(torch.abs(_snake_case			)			)

       assert abs(result_sum.item() - 149.8295			) < 1e-2
       assert abs(result_mean.item() - 0.1951			) < 1e-3







   def    a_				(				self			):
       UpperCamelCase					: Tuple 							=				self.full_loop(set_alpha_to_one=_snake_case     ,   beta_start=0.01			)
       UpperCamelCase					: List[str] 							=				torch.sum(torch.abs(_snake_case			)			)
       UpperCamelCase					: Dict 							=				torch.mean(torch.abs(_snake_case			)			)

       assert abs(result_sum.item() - 149.0784			) < 1e-2
       assert abs(result_mean.item() - 0.1941			) < 1e-3











"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





