






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    (DPMSolverSinglestepScheduler,)
      A          =    (('''num_inference_steps''', 25),)
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lambda_min_clipped": -float("inf"		),
              "variance_type": None,
          }

          config.update(**_lowerCAmelCase		)
          return config
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :Optional[Any]      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				self.dummy_sample
          lowerCAmelCase__   :Tuple      							=				0.1 * sample
          lowerCAmelCase__   :Optional[Any]      							=				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :Any      							=				self.get_scheduler_config(**_lowerCAmelCase		)
              lowerCAmelCase__   :List[Any]      							=				scheduler_class(**_lowerCAmelCase		)
              scheduler.set_timesteps(_lowerCAmelCase		)
              # copy over dummy past residuals
              lowerCAmelCase__   :Tuple      							=				dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase		)
                  lowerCAmelCase__   :Union[str, Any]      							=				scheduler_class.from_pretrained(_lowerCAmelCase		)
                  new_scheduler.set_timesteps(_lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowerCAmelCase__   :List[str]      							=				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__   :Optional[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 			snake_case_      (   self		):



          '''simple docstring'''
          pass
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :Tuple      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				self.dummy_sample
          lowerCAmelCase__   :List[Any]      							=				0.1 * sample
          lowerCAmelCase__   :int      							=				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :str      							=				self.get_scheduler_config()
              lowerCAmelCase__   :str      							=				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__   :Union[str, Any]      							=				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__   :Optional[Any]      							=				dummy_past_residuals[: new_scheduler.config.solver_order]

              lowerCAmelCase__   :int      							=				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 			snake_case_      (   self    ,	_lowerCAmelCase=None    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          if scheduler is None:
              lowerCAmelCase__   :Any      							=				self.scheduler_classes[0]
              lowerCAmelCase__   :Union[str, Any]      							=				self.get_scheduler_config(**_lowerCAmelCase		)
              lowerCAmelCase__   :Optional[int]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :List[Any]      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :Dict      							=				self.get_scheduler_config(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				10
          lowerCAmelCase__   :Optional[Any]      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
              lowerCAmelCase__   :Tuple      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          return sample
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          lowerCAmelCase__   :Tuple      							=				50
          lowerCAmelCase__   :str      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          # make sure that the first t is uneven
          for i, t in enumerate(scheduler.timesteps[3:]		):
              lowerCAmelCase__   :Optional[Any]      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Optional[Any]      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          lowerCAmelCase__   :Optional[Any]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2574		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for timesteps in [25, 50, 100, 999, 1_000]:
              self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          lowerCAmelCase__   :Tuple      							=				self.full_loop(scheduler=_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3

          lowerCAmelCase__   :Optional[int]      							=				DEISMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Dict      							=				DPMSolverMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Optional[int]      							=				UniPCMultistepScheduler.from_config(scheduler.config		)
          lowerCAmelCase__   :Tuple      							=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)

          lowerCAmelCase__   :Tuple      							=				self.full_loop(scheduler=_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3
      def 			snake_case_      (   self		):



          '''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 			snake_case_      (   self		):



          '''simple docstring'''
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for algorithm_type in ["dpmsolver", "dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                  for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=_lowerCAmelCase    ,	solver_type=_lowerCAmelCase    ,	prediction_type=_lowerCAmelCase    ,	algorithm_type=_lowerCAmelCase    ,	)
                          lowerCAmelCase__   :Union[str, Any]      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
          self.check_over_configs(lambda_min_clipped=-5.1		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.check_over_configs(variance_type=_lowerCAmelCase		)
          self.check_over_configs(variance_type="learned_range"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
              self.check_over_forward(num_inference_steps=_lowerCAmelCase    ,	time_step=0		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.full_loop()
          lowerCAmelCase__   :int      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2248		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				self.full_loop(prediction_type="v_prediction"		)
          lowerCAmelCase__   :Any      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.1453		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.full_loop(prediction_type="v_prediction"    ,	use_karras_sigmas=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.0649		) < 1e-3



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :Union[str, Any]      							=				self.get_scheduler_config(thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0		)
          lowerCAmelCase__   :Optional[int]      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				10
          lowerCAmelCase__   :List[Any]      							=				self.dummy_model()
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
              lowerCAmelCase__   :Dict      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          assert sample.dtype == torch.floataa














import re


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]"    ,			str_       )]


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :List[Any]      							=				split_input(str_       )
    return "".join(
        ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool    ,			UpperCAmelCase							:  str       ):
    try:
        lowerCAmelCase__   :Dict      							=				split_input(UpperCAmelCase       )
        if upper:
            lowerCAmelCase__   :Dict      							=				"".join(
                [
                    separator.join([char.upper() for char in sub_str]       )
                    for sub_str in string_split
                ]       )
        else:
            lowerCAmelCase__   :Any      							=				"".join(
                [
                    separator.join([char.lower() for char in sub_str]       )
                    for sub_str in string_split
                ]       )
        return res_str
    except IndexError:
        return "not valid string"


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return to_simple_case(UpperCAmelCase       )


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    try:
        lowerCAmelCase__   :Any      							=				to_simple_case(UpperCAmelCase       )
        return res_str[0].lower() + res_str[1:]
    except IndexError:
        return "not valid string"


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool       ):
    return to_complex_case(UpperCAmelCase    ,			UpperCAmelCase    ,			"_"       )


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool       ):
    return to_complex_case(UpperCAmelCase    ,			UpperCAmelCase    ,			"-"       )


if __name__ == "__main__":
 __import__("""doctest""").testmod()



