




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_lowerCamelCase      :  Any      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     "linear"
				lowerCAmelCase__				=     "cosine"
				lowerCAmelCase__				=     "cosine_with_restarts"
				lowerCAmelCase__				=     "polynomial"
				lowerCAmelCase__				=     "constant"
				lowerCAmelCase__				=     "constant_with_warmup"
				lowerCAmelCase__				=     "piecewise_constant"






def 							_UpperCAmelCase  (UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : int = -1       ):
				'''simple docstring'''
				return LambdaLR(UpperCamelCase_      ,    lambda UpperCamelCase_       : 1      ,    last_epoch=UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : int = -1       ):
				'''simple docstring'''

				def lr_lambda(UpperCamelCase_ : int       ):
								if current_step < num_warmup_steps:
												return float(UpperCamelCase_       ) / float(max(1.0      ,    UpperCamelCase_       )       )
								return 1.0

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : int = -1       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      {}
				_lowerCAmelCase    :    str										=      step_rules.split(""","""       )
				for rule_str in rule_list[:-1]:
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      rule_str.split(""":"""       )
								_lowerCAmelCase    :    Dict										=      int(UpperCamelCase_       )
								_lowerCAmelCase    :    int										=      float(UpperCamelCase_       )
								_lowerCAmelCase    :    Optional[Any]										=      value
				_lowerCAmelCase    :    List[Any]										=      float(rule_list[-1]       )

				def create_rules_function(UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : int       ):
								def rule_func(UpperCamelCase_ : int       ) -> float:
												_lowerCAmelCase    :    Any										=      sorted(rules_dict.keys()       )
												for i, sorted_step in enumerate(UpperCamelCase_       ):
																if steps < sorted_step:
																				return rules_dict[sorted_steps[i]]
												return last_lr_multiple

								return rule_func

				_lowerCAmelCase    :    int										=      create_rules_function(UpperCamelCase_      ,    UpperCamelCase_       )

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Tuple=-1       ):
				'''simple docstring'''

				def lr_lambda(UpperCamelCase_ : int       ):
								if current_step < num_warmup_steps:
												return float(UpperCamelCase_       ) / float(max(1      ,    UpperCamelCase_       )       )
								return max(
								    0.0      ,    float(num_training_steps - current_step       ) / float(max(1      ,    num_training_steps - num_warmup_steps       )       )       )

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : float = 0.5      ,    UpperCamelCase_ : int = -1       ):
				'''simple docstring'''

				def lr_lambda(UpperCamelCase_ : List[Any]       ):
								if current_step < num_warmup_steps:
												return float(UpperCamelCase_       ) / float(max(1      ,    UpperCamelCase_       )       )
								_lowerCAmelCase    :    Optional[int]										=      float(current_step - num_warmup_steps       ) / float(max(1      ,    num_training_steps - num_warmup_steps       )       )
								return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * float(UpperCamelCase_       ) * 2.0 * progress       ))       )

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : int = 1      ,    UpperCamelCase_ : int = -1       ):
				'''simple docstring'''

				def lr_lambda(UpperCamelCase_ : Dict       ):
								if current_step < num_warmup_steps:
												return float(UpperCamelCase_       ) / float(max(1      ,    UpperCamelCase_       )       )
								_lowerCAmelCase    :    Optional[Any]										=      float(current_step - num_warmup_steps       ) / float(max(1      ,    num_training_steps - num_warmup_steps       )       )
								if progress >= 1.0:
												return 0.0
								return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * ((float(UpperCamelCase_       ) * progress) % 1.0)       ))       )

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Tuple=1E-7      ,    UpperCamelCase_ : int=1.0      ,    UpperCamelCase_ : List[str]=-1       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      optimizer.defaults["""lr"""]
				if not (lr_init > lr_end):
								raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"       )

				def lr_lambda(UpperCamelCase_ : int       ):
								if current_step < num_warmup_steps:
												return float(UpperCamelCase_       ) / float(max(1      ,    UpperCamelCase_       )       )
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												_lowerCAmelCase    :    Dict										=      lr_init - lr_end
												_lowerCAmelCase    :    Union[str, Any]										=      num_training_steps - num_warmup_steps
												_lowerCAmelCase    :    Union[str, Any]										=      1 - (current_step - num_warmup_steps) / decay_steps
												_lowerCAmelCase    :    Union[str, Any]										=      lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )


_lowerCamelCase      :  Dict      						=						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, SchedulerType]      ,    UpperCamelCase_ : Optimizer      ,    UpperCamelCase_ : Optional[str] = None      ,    UpperCamelCase_ : Optional[int] = None      ,    UpperCamelCase_ : Optional[int] = None      ,    UpperCamelCase_ : int = 1      ,    UpperCamelCase_ : float = 1.0      ,    UpperCamelCase_ : int = -1      ,    ):
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      SchedulerType(UpperCamelCase_       )
				_lowerCAmelCase    :    Any										=      TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )

				if name == SchedulerType.PIECEWISE_CONSTANT:
								return schedule_func(UpperCamelCase_      ,    step_rules=UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )

				# All other schedulers require `num_warmup_steps`
				if num_warmup_steps is None:
								raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."       )

				if name == SchedulerType.CONSTANT_WITH_WARMUP:
								return schedule_func(UpperCamelCase_      ,    num_warmup_steps=UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )

				# All other schedulers require `num_training_steps`
				if num_training_steps is None:
								raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."       )

				if name == SchedulerType.COSINE_WITH_RESTARTS:
								return schedule_func(
								    UpperCamelCase_      ,    num_warmup_steps=UpperCamelCase_      ,    num_training_steps=UpperCamelCase_      ,    num_cycles=UpperCamelCase_      ,    last_epoch=UpperCamelCase_      ,    )

				if name == SchedulerType.POLYNOMIAL:
								return schedule_func(
								    UpperCamelCase_      ,    num_warmup_steps=UpperCamelCase_      ,    num_training_steps=UpperCamelCase_      ,    power=UpperCamelCase_      ,    last_epoch=UpperCamelCase_      ,    )

				return schedule_func(
				    UpperCamelCase_      ,    num_warmup_steps=UpperCamelCase_      ,    num_training_steps=UpperCamelCase_      ,    last_epoch=UpperCamelCase_       )







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																new_scheduler.set_timesteps(_UpperCAmelCase      )
																# copy over dummy past residuals
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

												# copy over dummy past residuals (must be after setting timesteps)
												_lowerCAmelCase    :    str										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      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)
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      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"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												_lowerCAmelCase    :    List[Any]										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												_lowerCAmelCase    :    List[str]										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




								for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=_UpperCAmelCase      )



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





								'''simple docstring'''




								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								scheduler.set_timesteps(10      )
								assert torch.equal(
								    scheduler.timesteps		,	torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCAmelCase      )



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								for t in [1, 5, 10]:
												self.check_over_forward(time_step=_UpperCAmelCase      )



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





								'''simple docstring'''




								for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]      ):
												self.check_over_forward(num_inference_steps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.step_plms(self.dummy_sample		,	1		,	self.dummy_sample      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

