




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class   __a(     UpperCAmelCase_   ):






    """simple docstring"""





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





    def 							a__      (      self   ,**_SCREAMING_SNAKE_CASE  ) ->		Optional[int]:
         UpperCAmelCase_			:							Optional[int]   =			{
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.00_01,
             '''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''',
             '''lower_order_final''': False,
             '''lambda_min_clipped''': -float('''inf'''  ),
             '''variance_type''': None,
         }

         config.update(**__UpperCamelCase  )
         return config





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE=0   ,**_SCREAMING_SNAKE_CASE  ) ->		Optional[int]:
         UpperCAmelCase_			:							Union[str, Any]   =			dict(self.forward_default_kwargs  )
         UpperCAmelCase_			:							List[Any]   =			kwargs.pop('''num_inference_steps'''   ,__UpperCamelCase  )
         UpperCAmelCase_			:							str   =			self.dummy_sample
         UpperCAmelCase_			:							Dict   =			0.1 * sample
         UpperCAmelCase_			:							int   =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

              UpperCAmelCase_,    UpperCAmelCase_			:							str   =			sample, sample
              for t in range(__UpperCamelCase   ,time_step + scheduler.config.solver_order + 1  ):
                   UpperCAmelCase_			:							Optional[int]   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample
                   UpperCAmelCase_			:							int   =			new_scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample

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





    def 							a__      (      self  ) ->		Tuple:
         pass





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE=0   ,**_SCREAMING_SNAKE_CASE  ) ->		Optional[Any]:
         UpperCAmelCase_			:							List[Any]   =			dict(self.forward_default_kwargs  )
         UpperCAmelCase_			:							Any   =			kwargs.pop('''num_inference_steps'''   ,__UpperCamelCase  )
         UpperCAmelCase_			:							str   =			self.dummy_sample
         UpperCAmelCase_			:							Union[str, Any]   =			0.1 * sample
         UpperCAmelCase_			:							str   =			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_			:							Union[str, Any]   =			self.get_scheduler_config()
              UpperCAmelCase_			:							List[str]   =			scheduler_class(**__UpperCamelCase  )
              scheduler.set_timesteps(__UpperCamelCase  )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__UpperCamelCase  )
                   UpperCAmelCase_			:							Optional[int]   =			scheduler_class.from_pretrained(__UpperCamelCase  )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__UpperCamelCase  )

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

              UpperCAmelCase_			:							Dict   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample
              UpperCAmelCase_			:							Optional[int]   =			new_scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample

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





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE=None   ,**_SCREAMING_SNAKE_CASE  ) ->		Dict:
         if scheduler is None:
              UpperCAmelCase_			:							List[Any]   =			self.scheduler_classes[0]
              UpperCAmelCase_			:							List[Any]   =			self.get_scheduler_config(**__UpperCamelCase  )
              UpperCAmelCase_			:							List[str]   =			scheduler_class(**__UpperCamelCase  )

         UpperCAmelCase_			:							Tuple   =			10
         UpperCAmelCase_			:							Dict   =			self.dummy_model()
         UpperCAmelCase_			:							str   =			self.dummy_sample_deter
         scheduler.set_timesteps(__UpperCamelCase  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_			:							Optional[int]   =			model(__UpperCamelCase   ,__UpperCamelCase  )
              UpperCAmelCase_			:							Optional[int]   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase  ).prev_sample

         return sample





    def 							a__      (      self  ) ->		Dict:
         UpperCAmelCase_			:							int   =			dict(self.forward_default_kwargs  )

         UpperCAmelCase_			:							Tuple   =			kwargs.pop('''num_inference_steps'''   ,__UpperCamelCase  )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_			:							Union[str, Any]   =			self.get_scheduler_config()
              UpperCAmelCase_			:							List[str]   =			scheduler_class(**__UpperCamelCase  )

              UpperCAmelCase_			:							Any   =			self.dummy_sample
              UpperCAmelCase_			:							Union[str, Any]   =			0.1 * sample

              if num_inference_steps is not None and hasattr(__UpperCamelCase   ,'''set_timesteps'''  ):
                   scheduler.set_timesteps(__UpperCamelCase  )
              elif num_inference_steps is not None and not hasattr(__UpperCamelCase   ,'''set_timesteps'''  ):
                   UpperCAmelCase_			:							Optional[Any]   =			num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase_			:							Union[str, Any]   =			[residual + 0.2, residual + 0.15, residual + 0.10]
              UpperCAmelCase_			:							str   =			dummy_past_residuals[: scheduler.config.solver_order]

              UpperCAmelCase_			:							Dict   =			scheduler.timesteps[5]
              UpperCAmelCase_			:							List[str]   =			scheduler.timesteps[6]

              UpperCAmelCase_			:							Union[str, Any]   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample
              UpperCAmelCase_			:							Dict   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,**__UpperCamelCase  ).prev_sample

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





    def 							a__      (      self  ) ->		Tuple:
         for timesteps in [25, 50, 100, 999, 1_000]:
              self.check_over_configs(num_train_timesteps=__UpperCamelCase  )





    def 							a__      (      self  ) ->		Optional[Any]:
         self.check_over_configs(thresholding=__UpperCamelCase  )
         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=__UpperCamelCase   ,prediction_type=__UpperCamelCase   ,sample_max_value=__UpperCamelCase   ,algorithm_type='''dpmsolver++'''   ,solver_order=__UpperCamelCase   ,solver_type=__UpperCamelCase   ,)





    def 							a__      (      self  ) ->		Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__UpperCamelCase  )





    def 							a__      (      self  ) ->		Optional[int]:
         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=__UpperCamelCase   ,solver_type=__UpperCamelCase   ,prediction_type=__UpperCamelCase   ,algorithm_type=__UpperCamelCase   ,)
                             UpperCAmelCase_			:							Any   =			self.full_loop(
                                 solver_order=__UpperCamelCase   ,solver_type=__UpperCamelCase   ,prediction_type=__UpperCamelCase   ,algorithm_type=__UpperCamelCase   ,)
                             assert not torch.isnan(__UpperCamelCase  ).any(), "Samples have nan numbers"





    def 							a__      (      self  ) ->		Union[str, Any]:
         self.check_over_configs(lower_order_final=__UpperCamelCase  )
         self.check_over_configs(lower_order_final=__UpperCamelCase  )





    def 							a__      (      self  ) ->		int:
         self.check_over_configs(lambda_min_clipped=-float('''inf'''  )  )
         self.check_over_configs(lambda_min_clipped=-5.1  )





    def 							a__      (      self  ) ->		Union[str, Any]:
         self.check_over_configs(variance_type=__UpperCamelCase  )
         self.check_over_configs(variance_type='''learned_range'''  )





    def 							a__      (      self  ) ->		Optional[int]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
              self.check_over_forward(num_inference_steps=__UpperCamelCase   ,time_step=0  )





    def 							a__      (      self  ) ->		List[Any]:
         UpperCAmelCase_			:							List[str]   =			self.full_loop()
         UpperCAmelCase_			:							str   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 0.33_01  ) < 1e-3





    def 							a__      (      self  ) ->		Union[str, Any]:
         UpperCAmelCase_			:							Dict   =			self.full_loop(thresholding=__UpperCamelCase   ,dynamic_thresholding_ratio=0.87   ,sample_max_value=0.5  )
         UpperCAmelCase_			:							Union[str, Any]   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 1.13_64  ) < 1e-3





    def 							a__      (      self  ) ->		Optional[int]:
         UpperCAmelCase_			:							Optional[Any]   =			self.full_loop(prediction_type='''v_prediction'''  )
         UpperCAmelCase_			:							str   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 0.22_51  ) < 1e-3





    def 							a__      (      self  ) ->		Tuple:
         UpperCAmelCase_			:							Tuple   =			self.full_loop(prediction_type='''v_prediction'''   ,use_karras_sigmas=__UpperCamelCase  )
         UpperCAmelCase_			:							Optional[Any]   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 0.20_96  ) < 1e-3





    def 							a__      (      self  ) ->		List[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         UpperCAmelCase_			:							List[Any]   =			DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
         UpperCAmelCase_			:							Tuple   =			self.full_loop(scheduler=__UpperCamelCase  )
         UpperCAmelCase_			:							List[str]   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 0.33_01  ) < 1e-3

         UpperCAmelCase_			:							Tuple   =			DPMSolverSinglestepScheduler.from_config(scheduler.config  )
         UpperCAmelCase_			:							List[str]   =			UniPCMultistepScheduler.from_config(scheduler.config  )
         UpperCAmelCase_			:							Optional[int]   =			DEISMultistepScheduler.from_config(scheduler.config  )
         UpperCAmelCase_			:							List[str]   =			DPMSolverMultistepScheduler.from_config(scheduler.config  )

         UpperCAmelCase_			:							Any   =			self.full_loop(scheduler=__UpperCamelCase  )
         UpperCAmelCase_			:							List[str]   =			torch.mean(torch.abs(__UpperCamelCase  )  )

         assert abs(result_mean.item() - 0.33_01  ) < 1e-3





    def 							a__      (      self  ) ->		List[Any]:
         UpperCAmelCase_			:							List[Any]   =			self.scheduler_classes[0]
         UpperCAmelCase_			:							str   =			self.get_scheduler_config(thresholding=__UpperCamelCase   ,dynamic_thresholding_ratio=0  )
         UpperCAmelCase_			:							int   =			scheduler_class(**__UpperCamelCase  )

         UpperCAmelCase_			:							Dict   =			10
         UpperCAmelCase_			:							int   =			self.dummy_model()
         UpperCAmelCase_			:							Optional[Any]   =			self.dummy_sample_deter.half()
         scheduler.set_timesteps(__UpperCamelCase  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_			:							List[Any]   =			model(__UpperCamelCase   ,__UpperCamelCase  )
              UpperCAmelCase_			:							Optional[Any]   =			scheduler.step(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase  ).prev_sample

         assert sample.dtype == torch.floataa





    def 							a__      (      self   ,**_SCREAMING_SNAKE_CASE  ) ->		int:
         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_			:							Optional[int]   =			self.get_scheduler_config(**__UpperCamelCase  )
              UpperCAmelCase_			:							str   =			scheduler_class(**__UpperCamelCase  )

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





'''simple docstring'''







from functools import lru_cache






@lru_cache
def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if not isinstance(lowercase_						,							lowercase_  ):
				raise TypeError("k must be an integer."  )
		if k < 0:
				raise ValueError("k integer must be greater or equal to zero."  )
		if k == 0:
				return 0
		elif k == 1:
				return 1
		else:
				return fibonacci(k - 1  ) + fibonacci(k - 2  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       len(lowercase_  )
		# Find m such that F_m >= n where F_i is the i_th fibonacci number.
		_snake_case	       =       0
		while True:
				if fibonacci(lowercase_  ) >= len_list:
						_snake_case	       =       i
						break
				i += 1
		_snake_case	       =       0
		while fibb_k > 0:
				_snake_case	       =       min(
				    offset + fibonacci(fibb_k - 1  )						,							len_list - 1  )  # Prevent out of range
				_snake_case	       =       arr[index_k]
				if item_k_1 == val:
						return index_k
				elif val < item_k_1:
						fibb_k -= 1
				elif val > item_k_1:
						offset += fibonacci(fibb_k - 1  )
						fibb_k -= 2
		else:
				return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()





