


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Optional[int]								=			(LMSDiscreteScheduler,)
					SCREAMING_SNAKE_CASE_		:       Any								=			10
					def 	a			(						self       :						Optional[int]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      {
											    'num_train_timesteps': 1_1_0_0,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_lowerCAmelCase  )
											return config
					def 	a			(						self       :						Union[str, Any]  )							->					Tuple:



											'''simple docstring'''





											for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_lowerCAmelCase  )
					def 	a			(						self       :						Union[str, Any]  )							->					List[Any]:



											'''simple docstring'''





											for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,      [0.0002, 0.002, 0.02]  ):
																	self.check_over_configs(beta_start=_lowerCAmelCase	,      beta_end=_lowerCAmelCase  )
					def 	a			(						self       :						str  )							->					Any:



											'''simple docstring'''





											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=_lowerCAmelCase  )
					def 	a			(						self       :						str  )							->					Tuple:



											'''simple docstring'''





											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_lowerCAmelCase  )
					def 	a			(						self       :						int  )							->					Optional[int]:



											'''simple docstring'''





											for t in [0, 5_0_0, 8_0_0]:
																	self.check_over_forward(time_step=_lowerCAmelCase  )
					def 	a			(						self       :						Union[str, Any]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

											scheduler.set_timesteps(self.num_inference_steps  )

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(scheduler.timesteps  ):
																	__magic_name__					=      scheduler.scale_model_input(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      model(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      scheduler.step(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_lowerCAmelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_lowerCAmelCase  )  )

											assert abs(result_sum.item() - 1006.388  ) < 1E-2
											assert abs(result_mean.item() - 1.31  ) < 1E-3
					def 	a			(						self       :						Any  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config(prediction_type='v_prediction'  )
											__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

											scheduler.set_timesteps(self.num_inference_steps  )

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(scheduler.timesteps  ):
																	__magic_name__					=      scheduler.scale_model_input(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      model(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      scheduler.step(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_lowerCAmelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_lowerCAmelCase  )  )

											assert abs(result_sum.item() - 0.0017  ) < 1E-2
											assert abs(result_mean.item() - 2.2_6_7_6E-0_6  ) < 1E-3
					def 	a			(						self       :						Dict  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_lowerCAmelCase  )

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

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											__magic_name__					=      sample.to(_lowerCAmelCase  )

											for i, t in enumerate(scheduler.timesteps  ):
																	__magic_name__					=      scheduler.scale_model_input(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      model(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      scheduler.step(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_lowerCAmelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_lowerCAmelCase  )  )

											assert abs(result_sum.item() - 1006.388  ) < 1E-2
											assert abs(result_mean.item() - 1.31  ) < 1E-3







					def 	a			(						self       :						Any  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_lowerCAmelCase	,      use_karras_sigmas=_lowerCAmelCase  )

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

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter.to(_lowerCAmelCase  ) * scheduler.init_noise_sigma
											__magic_name__					=      sample.to(_lowerCAmelCase  )

											for t in scheduler.timesteps:
																	__magic_name__					=      scheduler.scale_model_input(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      model(_lowerCAmelCase	,      _lowerCAmelCase  )

																	__magic_name__					=      scheduler.step(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_lowerCAmelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_lowerCAmelCase  )  )

											assert abs(result_sum.item() - 3812.9927  ) < 2E-2
											assert abs(result_mean.item() - 4.9648  ) < 1E-3





from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 				UpperCamelCase_	(					__snake_case      :    Dict  )->   str:




						'''simple docstring'''
						if is_torch_available():
												import torch

												if isinstance(__snake_case						,      torch.Tensor  ):
																		return True

						return isinstance(__snake_case						,      np.ndarray  )




class      __snake_case				(   _UpperCAmelCase):
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      fields(self  )

											# Safety and consistency checks
											if not len(_lowerCAmelCase  ):
																	raise ValueError(F'''{self.__class__.__name__} has no fields.'''  )

											__magic_name__					=      getattr(self	,      class_fields[0].name  )
											__magic_name__					=      all(getattr(self	,      field.name  ) is None for field in class_fields[1:]  )

											if other_fields_are_none and isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	for key, value in first_field.items():
																							__magic_name__					=      value
											else:
																	for field in class_fields:
																							__magic_name__					=      getattr(self	,      field.name  )
																							if v is not None:
																													__magic_name__					=      v
					def __delitem__(						self       :						Dict	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						List[str]  )							->					List[str]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Optional[int]	,      *_lowerCAmelCase       :						Optional[Any]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Union[str, Any]	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						Any  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Any	,      *_lowerCAmelCase       :						str	,      **_lowerCAmelCase       :						Optional[int]  )							->					List[Any]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''  )
					def __getitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Any  )							->					Tuple:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      dict(self.items()  )
																	return inner_dict[k]
											else:
																	return self.to_tuple()[k]
					def __setattr__(						self       :						str	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]  )							->					int:



											'''simple docstring'''





											if name in self.keys() and value is not None:
																	# Don't call self.__setitem__ to avoid recursion errors
																	super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )
					def __setitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						List[str]  )							->					Optional[int]:



											'''simple docstring'''





											super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											# Don't call self.__setattr__ to avoid recursion errors
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )







					def 	a			(						self       :						str  )							->					Tuple[Any]:



											'''simple docstring'''





											return tuple(self[k] for k in self.keys()  )

