


'''simple docstring'''


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						lowerCAmelCase__      (      _snake_case						):
			lowerCAmelCase			:						Union[str, Any]							 =  (LMSDiscreteScheduler,)
			lowerCAmelCase			:						Tuple							 =  10




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	**lowerCamelCase__	:  List[Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   {
					    "num_train_timesteps": 11_00,
					    "beta_start": 0.0_0_0_1,
					    "beta_end": 0.0_2,
					    "beta_schedule": "linear",
					}

					config.update(**lowercase__			)
					return config




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Tuple:



					'''simple docstring'''







					for timesteps in [10, 50, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=lowercase__			)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[Any]:



					'''simple docstring'''







					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,	[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
							self.check_over_configs(beta_start=lowercase__						,	beta_end=lowercase__			)




			def 				lowerCAmelCase__     (		self	:  int			) ->int:



					'''simple docstring'''







					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowercase__			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					for t in [0, 5_00, 8_00]:
							self.check_over_forward(time_step=lowercase__			)




			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   self.scheduler_classes[0]
					_UpperCAmelCase		:							List[Any]             =   self.get_scheduler_config()
					_UpperCAmelCase		:							Dict             =   scheduler_class(**lowercase__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					_UpperCAmelCase		:							Any             =   self.dummy_model()
					_UpperCAmelCase		:							str             =   self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps			):
							_UpperCAmelCase		:							List[str]             =   scheduler.scale_model_input(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Tuple             =   model(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							List[str]             =   scheduler.step(lowercase__						,	lowercase__						,	lowercase__			)
							_UpperCAmelCase		:							str             =   output.prev_sample

					_UpperCAmelCase		:							Optional[int]             =   torch.sum(torch.abs(lowercase__			)			)
					_UpperCAmelCase		:							int             =   torch.mean(torch.abs(lowercase__			)			)

					assert abs(result_sum.item() - 1_0_0_6.3_8_8			) < 1E-2
					assert abs(result_mean.item() - 1.3_1			) < 1E-3




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   self.scheduler_classes[0]
					_UpperCAmelCase		:							Optional[int]             =   self.get_scheduler_config(prediction_type="v_prediction"			)
					_UpperCAmelCase		:							int             =   scheduler_class(**lowercase__			)

					scheduler.set_timesteps(self.num_inference_steps			)

					_UpperCAmelCase		:							Dict             =   self.dummy_model()
					_UpperCAmelCase		:							Optional[Any]             =   self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps			):
							_UpperCAmelCase		:							List[str]             =   scheduler.scale_model_input(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							int             =   model(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Optional[Any]             =   scheduler.step(lowercase__						,	lowercase__						,	lowercase__			)
							_UpperCAmelCase		:							List[Any]             =   output.prev_sample

					_UpperCAmelCase		:							Optional[int]             =   torch.sum(torch.abs(lowercase__			)			)
					_UpperCAmelCase		:							int             =   torch.mean(torch.abs(lowercase__			)			)

					assert abs(result_sum.item() - 0.0_0_1_7			) < 1E-2
					assert abs(result_mean.item() - 2.2676E-06			) < 1E-3




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   self.scheduler_classes[0]
					_UpperCAmelCase		:							List[str]             =   self.get_scheduler_config()
					_UpperCAmelCase		:							Optional[int]             =   scheduler_class(**lowercase__			)

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

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

					for i, t in enumerate(scheduler.timesteps			):
							_UpperCAmelCase		:							Optional[Any]             =   scheduler.scale_model_input(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Union[str, Any]             =   model(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Union[str, Any]             =   scheduler.step(lowercase__						,	lowercase__						,	lowercase__			)
							_UpperCAmelCase		:							Optional[Any]             =   output.prev_sample

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

					assert abs(result_sum.item() - 1_0_0_6.3_8_8			) < 1E-2
					assert abs(result_mean.item() - 1.3_1			) < 1E-3






			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   self.scheduler_classes[0]
					_UpperCAmelCase		:							Any             =   self.get_scheduler_config()
					_UpperCAmelCase		:							Tuple             =   scheduler_class(**lowercase__						,	use_karras_sigmas=lowercase__			)

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

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

					for t in scheduler.timesteps:
							_UpperCAmelCase		:							str             =   scheduler.scale_model_input(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Dict             =   model(lowercase__						,	lowercase__			)

							_UpperCAmelCase		:							Tuple             =   scheduler.step(lowercase__						,	lowercase__						,	lowercase__			)
							_UpperCAmelCase		:							List[Any]             =   output.prev_sample

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

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7			) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8			) < 1E-3






'''simple docstring'''







from collections import namedtuple

_lowerCamelCase    =  namedtuple('from_to', 'from_ to')

_lowerCamelCase    =  {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

_lowerCamelCase    =  {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     from_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	_lowercase							=     to_type.lower().rstrip("""s"""	)
	_lowercase							=     TYPE_CONVERSION.get(snake_case_							,       snake_case_	)
	if new_from not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'from_type' value: {from_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	if new_to not in METRIC_CONVERSION:
		_lowercase							=     (
		    F"""Invalid 'to_type' value: {to_type!r}.\n"""
		    F"""Conversion abbreviations are: {", ".join(snake_case_	)}"""
		)
		raise ValueError(snake_case_	)
	return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()




