

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


a__				=		logging.get_logger(__name__)

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "linear"
							UpperCAmelCase__       :						str	    =    "cosine"
							UpperCAmelCase__       :						List[Any]	    =    "cosine_with_restarts"
							UpperCAmelCase__       :						int	    =    "polynomial"
							UpperCAmelCase__       :						Optional[Any]	    =    "constant"
							UpperCAmelCase__       :						Union[str, Any]	    =    "constant_with_warmup"
							UpperCAmelCase__       :						str	    =    "piecewise_constant"



def    __UpperCAmelCase (	__a      :			Optimizer		,__a      :			int = -1	)			->     Optional[Any]:

					"""simple docstring"""

					return LambdaLR(__a		,lambda __a	: 1		,last_epoch=__a	)



def    __UpperCAmelCase (	__a      :			Optimizer		,__a      :			int		,__a      :			int = -1	)			->     Dict:

					"""simple docstring"""

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

					return LambdaLR(__a		,__a		,last_epoch=__a	)



def    __UpperCAmelCase (	__a      :			Optimizer		,__a      :			str		,__a      :			int = -1	)			->     Tuple:

					"""simple docstring"""

					_a	:    Tuple         =		{}
					_a	:    Optional[Any]         =		step_rules.split(''','''	)
					for rule_str in rule_list[:-1]:
										_a      ,					_a	:    str         =		rule_str.split(''':'''	)
										_a	:    List[Any]         =		int(__a	)
										_a	:    Tuple         =		float(__a	)
										_a	:    str         =		value
					_a	:    str         =		float(rule_list[-1]	)

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

										return rule_func

					_a	:    List[Any]         =		create_rules_function(__a		,__a	)

					return LambdaLR(__a		,__a		,last_epoch=__a	)



def    __UpperCAmelCase (	__a      :			str		,__a      :			Tuple		,__a      :			List[Any]		,__a      :			Union[str, Any]=-1	)			->     Optional[int]:

					"""simple docstring"""

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

					return LambdaLR(__a		,__a		,__a	)



def    __UpperCAmelCase (	__a      :			Optimizer		,__a      :			int		,__a      :			int		,__a      :			float = 0.5		,__a      :			int = -1	)			->     Union[str, Any]:

					"""simple docstring"""

					def lr_lambda(__a      :			Optional[int]	):
										if current_step < num_warmup_steps:
															return float(__a	) / float(max(1		,__a	)	)
										_a	:    Optional[Any]         =		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(__a	) * 2.0 * progress	))	)

					return LambdaLR(__a		,__a		,__a	)



def    __UpperCAmelCase (	__a      :			Optimizer		,__a      :			int		,__a      :			int		,__a      :			int = 1		,__a      :			int = -1	)			->     int:

					"""simple docstring"""

					def lr_lambda(__a      :			List[str]	):
										if current_step < num_warmup_steps:
															return float(__a	) / float(max(1		,__a	)	)
										_a	:    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(__a	) * progress) % 1.0)	))	)

					return LambdaLR(__a		,__a		,__a	)



def    __UpperCAmelCase (	__a      :			str		,__a      :			Any		,__a      :			int		,__a      :			int=1E-7		,__a      :			str=1.0		,__a      :			List[Any]=-1	)			->     Optional[int]:

					"""simple docstring"""

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

					return LambdaLR(__a		,__a		,__a	)


a__				=		{
    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 (	__a      :			Union[str, SchedulerType]		,__a      :			Optimizer		,__a      :			Optional[str] = None		,__a      :			Optional[int] = None		,__a      :			Optional[int] = None		,__a      :			int = 1		,__a      :			float = 1.0		,__a      :			int = -1		,)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Optional[Any]         =		SchedulerType(__a	)
					_a	:    Union[str, Any]         =		TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(__a		,last_epoch=__a	)

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

					# 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(__a		,num_warmup_steps=__a		,last_epoch=__a	)

					# 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(
										    __a		,num_warmup_steps=__a		,num_training_steps=__a		,num_cycles=__a		,last_epoch=__a		,)

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

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






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    (DDPMScheduler,)


							def 				__lowercase				(			self       ,							**_a       )    ->    Any:
												_a	:    List[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       )    ->    Any:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,							[0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=_a       )


							def 				__lowercase				(			self       )    ->    str:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    List[Any]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    Optional[int]         =		len(_a       )

												_a	:    Optional[Any]         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												_a	:    Union[str, Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    str         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    List[Any]         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Optional[Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 258.9606       ) < 1e-2
												assert abs(result_mean.item() - 0.3372       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
												_a	:    Union[str, Any]         =		scheduler_class(**_a       )

												_a	:    Dict         =		len(_a       )

												_a	:    int         =		self.dummy_model()
												_a	:    Tuple         =		self.dummy_sample_deter
												_a	:    List[Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    Dict         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    int         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    str         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 202.0296       ) < 1e-2
												assert abs(result_mean.item() - 0.2631       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Union[str, Any]         =		self.scheduler_classes[0]
												_a	:    Tuple         =		self.get_scheduler_config()
												_a	:    Any         =		scheduler_class(**_a       )

												_a	:    Optional[Any]         =		[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=_a       )

												_a	:    Optional[int]         =		scheduler.timesteps

												for i, timestep in enumerate(_a       ):
																	if i == len(_a       ) - 1:
																						_a	:    Dict         =		-1
																	else:
																						_a	:    Tuple         =		timesteps[i + 1]

																	_a	:    Optional[Any]         =		scheduler.previous_timestep(_a       )
																	_a	:    Optional[Any]         =		prev_t.item()

																	self.assertEqual(_a       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Tuple         =		scheduler_class(**_a       )

												_a	:    str         =		[1_0_0, 8_7, 5_0, 5_1, 0]

												with self.assertRaises(_a       ,							msg='''`custom_timesteps` must be in descending order.'''       ):
																	scheduler.set_timesteps(timesteps=_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												_a	:    Union[str, Any]         =		[1_0_0, 8_7, 5_0, 1, 0]
												_a	:    Optional[Any]         =		len(_a       )

												with self.assertRaises(_a       ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''       ):
																	scheduler.set_timesteps(num_inference_steps=_a       ,							timesteps=_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    Union[str, Any]         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    str         =		[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _a       ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,							):
																	scheduler.set_timesteps(timesteps=_a       )



