




from typing import Any
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,)     ->					List[Any]:
							_validation(
							    UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,)
							# Creates data structures and fill initial step
							lowercase__    :			dict     =				{}
							lowercase__    :			dict     =				{}
							for state in states_space:
														lowercase__    :			Optional[int]     =				observations_space[0]
														lowercase__    :			Any     =				(
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														lowercase__    :			Union[str, Any]     =				None

							# Fills the data structure with the probabilities of
							# different transitions and pointers to previous states
							for o in range(1							,len(UpperCamelCase__							)							):
														lowercase__    :			str     =				observations_space[o]
														lowercase__    :			Union[str, Any]     =				observations_space[o - 1]
														for state in states_space:
																					# Calculates the argmax for probability function
																					lowercase__    :			int     =				''''''
																					lowercase__    :			List[str]     =				-1
																					for k_state in states_space:
																												lowercase__    :			Union[str, Any]     =				(
																												    probabilities[(k_state, prior_observation)]
																												    * transition_probabilities[k_state][state]
																												    * emission_probabilities[state][observation]
																												)
																												if probability > max_probability:
																																			lowercase__    :			Tuple     =				probability
																																			lowercase__    :			Union[str, Any]     =				k_state

            # Update probabilities and pointers dicts
																					lowercase__    :			Tuple     =				(
																					    probabilities[(arg_max, prior_observation)]
																					    * transition_probabilities[arg_max][state]
																					    * emission_probabilities[state][observation]
																					)

																					lowercase__    :			Optional[Any]     =				arg_max

    # The final observation
							lowercase__    :			List[str]     =				observations_space[len(UpperCamelCase__							) - 1]

							# argmax for given final observation
							lowercase__    :			Dict     =				''''''
							lowercase__    :			Tuple     =				-1
							for k_state in states_space:
														lowercase__    :			Any     =				probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					lowercase__    :			List[str]     =				probability
																					lowercase__    :			Tuple     =				k_state
							lowercase__    :			Any     =				arg_max

							# Process pointers backwards
							lowercase__    :			List[Any]     =				last_state
							lowercase__    :			int     =				[]
							for o in range(len(UpperCamelCase__							) - 1							,-1							,-1							):
														result.append(UpperCamelCase__							)
														lowercase__    :			int     =				pointers[previous, observations_space[o]]
							result.reverse()

							return result
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,)     ->					Any:
							_validate_not_empty(
							    UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,)
							_validate_lists(UpperCamelCase__							,UpperCamelCase__							)
							_validate_dicts(
							    UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,)     ->					Dict:
							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ]							):
														raise ValueError("There\'s an empty parameter"							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					str:
							_validate_list(UpperCamelCase__							,"observations_space"							)
							_validate_list(UpperCamelCase__							,"states_space"							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					str:
							if not isinstance(_object							,UpperCamelCase__							):
														lowercase__    :			List[Any]     =				F"""{var_name} must be a list"""
														raise ValueError(UpperCamelCase__							)
							else:
														for x in _object:
																					if not isinstance(UpperCamelCase__							,UpperCamelCase__							):
																												lowercase__    :			List[Any]     =				F"""{var_name} must be a list of strings"""
																												raise ValueError(UpperCamelCase__							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,)     ->					Dict:
							_validate_dict(UpperCamelCase__							,"initial_probabilities"							,UpperCamelCase__							)
							_validate_nested_dict(UpperCamelCase__							,"transition_probabilities"							)
							_validate_nested_dict(UpperCamelCase__							,"emission_probabilities"							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					Tuple:
							_validate_dict(_object							,UpperCamelCase__							,UpperCamelCase__							)
							for x in _object.values():
														_validate_dict(UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_ = False							)     ->					str:
							if not isinstance(_object							,UpperCamelCase__							):
														lowercase__    :			List[str]     =				F"""{var_name} must be a dict"""
														raise ValueError(UpperCamelCase__							)
							if not all(isinstance(UpperCamelCase__							,UpperCamelCase__							) for x in _object							):
														lowercase__    :			Dict     =				F"""{var_name} all keys must be strings"""
														raise ValueError(UpperCamelCase__							)
							if not all(isinstance(UpperCamelCase__							,UpperCamelCase__							) for x in _object.values()							):
														lowercase__    :			Optional[Any]     =				'''nested dictionary ''' if nested else ''''''
														lowercase__    :			Optional[Any]     =				F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
														raise ValueError(UpperCamelCase__							)


if __name__ == "__main__":
						from doctest import testmod

						testmod()

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE_		 =	(('''num_inference_steps''', 25),)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**__SCREAMING_SNAKE_CASE	)
           return config






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Dict											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.dummy_sample
           UpperCamelCase__     : Optional[int]											=					0.1 * sample
           UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 UpperCamelCase__					,UpperCamelCase__     : Any											=					sample, sample
                 for t in range(__SCREAMING_SNAKE_CASE    ,    time_step + scheduler.config.solver_order + 1	):
                       UpperCamelCase__     : Union[str, Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                       UpperCamelCase__     : int											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Tuple											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					self.dummy_sample
           UpperCamelCase__     : List[Any]											=					0.1 * sample
           UpperCamelCase__     : Dict											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Union[str, Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : str											=					scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE	)
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Dict											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if scheduler is None:
                 UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
                 UpperCamelCase__     : str											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Dict											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Tuple											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Tuple											=					self.dummy_model()
           UpperCamelCase__     : Dict											=					self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

           return sample






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					dict(self.forward_default_kwargs	)

           UpperCamelCase__     : List[Any]											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

                 UpperCamelCase__     : int											=					self.dummy_sample
                 UpperCamelCase__     : int											=					0.1 * sample

                 if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE    ,    '''set_timesteps'''	):
                       scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
                 elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE    ,    '''set_timesteps'''	):
                       UpperCamelCase__     : str											=					num_inference_steps

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

                 UpperCamelCase__     : int											=					scheduler.timesteps[5]
                 UpperCamelCase__     : Optional[int]											=					scheduler.timesteps[6]

                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler(**self.get_scheduler_config()	)
           UpperCamelCase__     : Tuple											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3

           UpperCamelCase__     : List[Any]											=					DPMSolverSinglestepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : Union[str, Any]											=					DPMSolverMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : int											=					UniPCMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler.from_config(scheduler.config	)

           UpperCamelCase__     : Dict											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    sample_max_value=__SCREAMING_SNAKE_CASE    ,    algorithm_type='''deis'''    ,    solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    )






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




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   UpperCamelCase__     : Any											=					self.full_loop(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   assert not torch.isnan(__SCREAMING_SNAKE_CASE	).any(), "Samples have nan numbers"






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,    time_step=0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.full_loop()
           UpperCamelCase__     : Optional[int]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.full_loop(prediction_type='''v_prediction'''	)
           UpperCamelCase__     : List[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.091	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Optional[Any]											=					self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,    dynamic_thresholding_ratio=0	)
           UpperCamelCase__     : Tuple											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Dict											=					self.dummy_model()
           UpperCamelCase__     : Tuple											=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Tuple											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           assert sample.dtype == torch.floataa


