import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 						__snake_case						(			A_		):





      '''simple docstring'''






      _snake_case     		=							(DPMSolverMultistepScheduler,)
      _snake_case     		=							(('num_inference_steps', 25),)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	**_UpperCamelCase  :					Tuple)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    {
              'num_train_timesteps': 1000,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'solver_order': 2,
              'prediction_type': 'epsilon',
              'thresholding': False,
              'sample_max_value': 1.0,
              'algorithm_type': 'dpmsolver++',
              'solver_type': 'midpoint',
              'lower_order_final': False,
              'lambda_min_clipped': -float("""inf"""),
              'variance_type': None,
          }

          config.update(**__lowerCamelCase)
          return config



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					List[Any]=0     ,	**_UpperCamelCase  :					Optional[Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    dict(self.forward_default_kwargs)
          _lowerCamelCase  :		List[Any]			    =    kwargs.pop("""num_inference_steps"""     ,	__lowerCamelCase)
          _lowerCamelCase  :		Dict			    =    self.dummy_sample
          _lowerCamelCase  :		Dict			    =    0.1 * sample
          _lowerCamelCase  :		int			    =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
              _lowerCamelCase  :		Union[str, Any]			    =    self.get_scheduler_config(**__lowerCamelCase)
              _lowerCamelCase  :		Optional[Any]			    =    scheduler_class(**__lowerCamelCase)
              scheduler.set_timesteps(__lowerCamelCase)
              # copy over dummy past residuals
              _lowerCamelCase  :		Dict			    =    dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase)
                  _lowerCamelCase  :		str			    =    scheduler_class.from_pretrained(__lowerCamelCase)
                  new_scheduler.set_timesteps(__lowerCamelCase)
                  # copy over dummy past residuals
                  _lowerCamelCase  :		Any			    =    dummy_past_residuals[: new_scheduler.config.solver_order]

              _lowerCamelCase  :		Any			    =    sample, sample
              for t in range(__lowerCamelCase     ,	time_step + scheduler.config.solver_order + 1):
                  _lowerCamelCase  :		Any			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample
                  _lowerCamelCase  :		List[Any]			    =    new_scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample

                  assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



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


          """simple docstring"""


          pass



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					List[str]=0     ,	**_UpperCamelCase  :					Any)					->str:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    dict(self.forward_default_kwargs)
          _lowerCamelCase  :		int			    =    kwargs.pop("""num_inference_steps"""     ,	__lowerCamelCase)
          _lowerCamelCase  :		Any			    =    self.dummy_sample
          _lowerCamelCase  :		Dict			    =    0.1 * sample
          _lowerCamelCase  :		Union[str, Any]			    =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
              _lowerCamelCase  :		List[Any]			    =    self.get_scheduler_config()
              _lowerCamelCase  :		Any			    =    scheduler_class(**__lowerCamelCase)
              scheduler.set_timesteps(__lowerCamelCase)

              # copy over dummy past residuals (must be after setting timesteps)
              _lowerCamelCase  :		Dict			    =    dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase)
                  _lowerCamelCase  :		Tuple			    =    scheduler_class.from_pretrained(__lowerCamelCase)
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(__lowerCamelCase)

                  # copy over dummy past residual (must be after setting timesteps)
                  _lowerCamelCase  :		int			    =    dummy_past_residuals[: new_scheduler.config.solver_order]

              _lowerCamelCase  :		Tuple			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample
              _lowerCamelCase  :		str			    =    new_scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					List[str]=None     ,	**_UpperCamelCase  :					Tuple)					->Dict:


          """simple docstring"""


          if scheduler is None:
              _lowerCamelCase  :		List[Any]			    =    self.scheduler_classes[0]
              _lowerCamelCase  :		Any			    =    self.get_scheduler_config(**__lowerCamelCase)
              _lowerCamelCase  :		Any			    =    scheduler_class(**__lowerCamelCase)

          _lowerCamelCase  :		List[str]			    =    10
          _lowerCamelCase  :		Any			    =    self.dummy_model()
          _lowerCamelCase  :		int			    =    self.dummy_sample_deter
          scheduler.set_timesteps(__lowerCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		Union[str, Any]			    =    model(__lowerCamelCase     ,	__lowerCamelCase)
              _lowerCamelCase  :		str			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase).prev_sample

          return sample



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    dict(self.forward_default_kwargs)

          _lowerCamelCase  :		Dict			    =    kwargs.pop("""num_inference_steps"""     ,	__lowerCamelCase)

          for scheduler_class in self.scheduler_classes:
              _lowerCamelCase  :		str			    =    self.get_scheduler_config()
              _lowerCamelCase  :		List[str]			    =    scheduler_class(**__lowerCamelCase)

              _lowerCamelCase  :		Tuple			    =    self.dummy_sample
              _lowerCamelCase  :		str			    =    0.1 * sample

              if num_inference_steps is not None and hasattr(__lowerCamelCase     ,	"""set_timesteps"""):
                  scheduler.set_timesteps(__lowerCamelCase)
              elif num_inference_steps is not None and not hasattr(__lowerCamelCase     ,	"""set_timesteps"""):
                  _lowerCamelCase  :		List[Any]			    =    num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              _lowerCamelCase  :		Any			    =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
              _lowerCamelCase  :		Dict			    =    dummy_past_residuals[: scheduler.config.solver_order]

              _lowerCamelCase  :		Optional[Any]			    =    scheduler.timesteps[5]
              _lowerCamelCase  :		int			    =    scheduler.timesteps[6]

              _lowerCamelCase  :		Union[str, Any]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample
              _lowerCamelCase  :		List[str]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	**__lowerCamelCase).prev_sample

              self.assertEqual(output_a.shape     ,	sample.shape)
              self.assertEqual(output_a.shape     ,	output_a.shape)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->str:


          """simple docstring"""


          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCamelCase)



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


          """simple docstring"""


          self.check_over_configs(thresholding=__lowerCamelCase)
          for order in [1, 2, 3]:
              for solver_type in ["midpoint", "heun"]:
                  for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=__lowerCamelCase     ,	prediction_type=__lowerCamelCase     ,	sample_max_value=__lowerCamelCase     ,	algorithm_type="""dpmsolver++"""     ,	solver_order=__lowerCamelCase     ,	solver_type=__lowerCamelCase     ,	)



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


          """simple docstring"""


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



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


          """simple docstring"""


          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                          if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                              if order == 3:
                                  continue
                          else:
                              self.check_over_configs(
                                  solver_order=__lowerCamelCase     ,	solver_type=__lowerCamelCase     ,	prediction_type=__lowerCamelCase     ,	algorithm_type=__lowerCamelCase     ,	)
                          _lowerCamelCase  :		Dict			    =    self.full_loop(
                              solver_order=__lowerCamelCase     ,	solver_type=__lowerCamelCase     ,	prediction_type=__lowerCamelCase     ,	algorithm_type=__lowerCamelCase     ,	)
                          assert not torch.isnan(__lowerCamelCase).any(), "Samples have nan numbers"



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


          """simple docstring"""


          self.check_over_configs(lower_order_final=__lowerCamelCase)
          self.check_over_configs(lower_order_final=__lowerCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->str:


          """simple docstring"""


          self.check_over_configs(lambda_min_clipped=-float("""inf"""))
          self.check_over_configs(lambda_min_clipped=-5.1)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Tuple:


          """simple docstring"""


          self.check_over_configs(variance_type=__lowerCamelCase)
          self.check_over_configs(variance_type="""learned_range""")



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


          """simple docstring"""


          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=__lowerCamelCase     ,	time_step=0)



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    self.full_loop()
          _lowerCamelCase  :		List[Any]			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 0.3_3_0_1) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase  :		int			    =    self.full_loop(thresholding=__lowerCamelCase     ,	dynamic_thresholding_ratio=0.8_7     ,	sample_max_value=0.5)
          _lowerCamelCase  :		str			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 1.1_3_6_4) < 1E-3



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.full_loop(prediction_type="""v_prediction""")
          _lowerCamelCase  :		Tuple			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 0.2_2_5_1) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.full_loop(prediction_type="""v_prediction"""     ,	use_karras_sigmas=__lowerCamelCase)
          _lowerCamelCase  :		int			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 0.2_0_9_6) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    DPMSolverMultistepScheduler(**self.get_scheduler_config())
          _lowerCamelCase  :		Any			    =    self.full_loop(scheduler=__lowerCamelCase)
          _lowerCamelCase  :		Any			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 0.3_3_0_1) < 1E-3

          _lowerCamelCase  :		Any			    =    DPMSolverSinglestepScheduler.from_config(scheduler.config)
          _lowerCamelCase  :		List[str]			    =    UniPCMultistepScheduler.from_config(scheduler.config)
          _lowerCamelCase  :		List[str]			    =    DEISMultistepScheduler.from_config(scheduler.config)
          _lowerCamelCase  :		Any			    =    DPMSolverMultistepScheduler.from_config(scheduler.config)

          _lowerCamelCase  :		str			    =    self.full_loop(scheduler=__lowerCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    torch.mean(torch.abs(__lowerCamelCase))

          assert abs(result_mean.item() - 0.3_3_0_1) < 1E-3



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


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    self.scheduler_classes[0]
          _lowerCamelCase  :		str			    =    self.get_scheduler_config(thresholding=__lowerCamelCase     ,	dynamic_thresholding_ratio=0)
          _lowerCamelCase  :		Union[str, Any]			    =    scheduler_class(**__lowerCamelCase)

          _lowerCamelCase  :		Any			    =    10
          _lowerCamelCase  :		List[str]			    =    self.dummy_model()
          _lowerCamelCase  :		Union[str, Any]			    =    self.dummy_sample_deter.half()
          scheduler.set_timesteps(__lowerCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase  :		Optional[int]			    =    model(__lowerCamelCase     ,	__lowerCamelCase)
              _lowerCamelCase  :		List[str]			    =    scheduler.step(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase).prev_sample

          assert sample.dtype == torch.floataa




      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	**_UpperCamelCase  :					Optional[Any])					->Dict:


          """simple docstring"""


          for scheduler_class in self.scheduler_classes:
              _lowerCamelCase  :		str			    =    self.get_scheduler_config(**__lowerCamelCase)
              _lowerCamelCase  :		Union[str, Any]			    =    scheduler_class(**__lowerCamelCase)

              scheduler.set_timesteps(scheduler.config.num_train_timesteps)
              assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps





"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

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

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



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



