

"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 			_UpperCAmelCase	(						snake_case__):
			__a       :				int     =							(KDPMaDiscreteScheduler,)
			__a       :				Any     =							1_0
			def 							__snake_case       (						self					,						**_A   )			->			Dict:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_A   )
				return config
			def 							__snake_case       (						self   )			->			List[str]:






				'''simple docstring'''







				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_A   )
			def 							__snake_case       (						self   )			->			Tuple:






				'''simple docstring'''







				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,						[0.0002, 0.002, 0.02]   ):
					self.check_over_configs(beta_start=_A					,						beta_end=_A   )
			def 							__snake_case       (						self   )			->			Optional[int]:






				'''simple docstring'''







				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_A   )
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_A   )
			def 							__snake_case       (						self   )			->			Any:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    self.scheduler_classes[0]
				_UpperCAmelCase			:							Tuple	  =    self.get_scheduler_config(prediction_type="""v_prediction"""   )
				_UpperCAmelCase			:							Dict	  =    scheduler_class(**_A   )

				scheduler.set_timesteps(self.num_inference_steps   )

				_UpperCAmelCase			:							Optional[Any]	  =    self.dummy_model()
				_UpperCAmelCase			:							Union[str, Any]	  =    self.dummy_sample_deter * scheduler.init_noise_sigma
				_UpperCAmelCase			:							Optional[Any]	  =    sample.to(_A   )

				for i, t in enumerate(scheduler.timesteps   ):
					_UpperCAmelCase			:							Tuple	  =    scheduler.scale_model_input(_A					,						_A   )

					_UpperCAmelCase			:							List[str]	  =    model(_A					,						_A   )

					_UpperCAmelCase			:							Any	  =    scheduler.step(_A					,						_A					,						_A   )
					_UpperCAmelCase			:							Dict	  =    output.prev_sample

				_UpperCAmelCase			:							Union[str, Any]	  =    torch.sum(torch.abs(_A   )   )
				_UpperCAmelCase			:							int	  =    torch.mean(torch.abs(_A   )   )

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.69_34e-07   ) < 1e-2
					assert abs(result_mean.item() - 6.11_12e-10   ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07   ) < 1e-2
					assert abs(result_mean.item() - 0.0002   ) < 1e-3
			def 							__snake_case       (						self   )			->			Optional[int]:






				'''simple docstring'''







				if torch_device == "mps":
					return
				_UpperCAmelCase			:							Any	  =    self.scheduler_classes[0]
				_UpperCAmelCase			:							str	  =    self.get_scheduler_config()
				_UpperCAmelCase			:							Tuple	  =    scheduler_class(**_A   )

				scheduler.set_timesteps(self.num_inference_steps   )

				_UpperCAmelCase			:							List[Any]	  =    self.dummy_model()
				_UpperCAmelCase			:							Dict	  =    self.dummy_sample_deter * scheduler.init_noise_sigma
				_UpperCAmelCase			:							Dict	  =    sample.to(_A   )

				for i, t in enumerate(scheduler.timesteps   ):
					_UpperCAmelCase			:							Tuple	  =    scheduler.scale_model_input(_A					,						_A   )

					_UpperCAmelCase			:							List[str]	  =    model(_A					,						_A   )

					_UpperCAmelCase			:							Optional[int]	  =    scheduler.step(_A					,						_A					,						_A   )
					_UpperCAmelCase			:							Any	  =    output.prev_sample

				_UpperCAmelCase			:							Optional[Any]	  =    torch.sum(torch.abs(_A   )   )
				_UpperCAmelCase			:							List[str]	  =    torch.mean(torch.abs(_A   )   )

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 20.4125   ) < 1e-2
					assert abs(result_mean.item() - 0.0266   ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.4125   ) < 1e-2
					assert abs(result_mean.item() - 0.0266   ) < 1e-3
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







				if torch_device == "mps":
					return
				_UpperCAmelCase			:							List[str]	  =    self.scheduler_classes[0]
				_UpperCAmelCase			:							List[str]	  =    self.get_scheduler_config()
				_UpperCAmelCase			:							str	  =    scheduler_class(**_A   )

				scheduler.set_timesteps(self.num_inference_steps					,						device=_A   )

				_UpperCAmelCase			:							Dict	  =    self.dummy_model()
				_UpperCAmelCase			:							List[str]	  =    self.dummy_sample_deter.to(_A   ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					_UpperCAmelCase			:							Optional[int]	  =    scheduler.scale_model_input(_A					,						_A   )

					_UpperCAmelCase			:							Dict	  =    model(_A					,						_A   )

					_UpperCAmelCase			:							int	  =    scheduler.step(_A					,						_A					,						_A   )
					_UpperCAmelCase			:							Tuple	  =    output.prev_sample

				_UpperCAmelCase			:							Optional[Any]	  =    torch.sum(torch.abs(_A   )   )
				_UpperCAmelCase			:							Any	  =    torch.mean(torch.abs(_A   )   )

				if str(_A   ).startswith("""cpu"""   ):
					# The following sum varies between 148 and 156 on mps. Why?
					assert abs(result_sum.item() - 20.4125   ) < 1e-2
					assert abs(result_mean.item() - 0.0266   ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.4125   ) < 1e-2
					assert abs(result_mean.item() - 0.0266   ) < 1e-3






'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

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

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

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

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

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

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


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

  testmod()

