


'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				(DPMSolverSinglestepScheduler,)
							lowercase__      :       str     						=				(("num_inference_steps", 2_5),)







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							List[str]:
														lowerCAmelCase__	       =     {
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_001,
														    '''beta_end''': 0.02,
														    '''beta_schedule''': '''linear''',
														    '''solver_order''': 2,
														    '''prediction_type''': '''epsilon''',
														    '''thresholding''': False,
														    '''sample_max_value''': 1.0,
														    '''algorithm_type''': '''dpmsolver++''',
														    '''solver_type''': '''midpoint''',
														    '''lambda_min_clipped''': -float('''inf'''       ),
														    '''variance_type''': None,
														}

														config.update(**lowerCamelCase_       )
														return config







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=0 ,   **lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     dict(self.forward_default_kwargs       )
														lowerCAmelCase__	       =     kwargs.pop('''num_inference_steps''' ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     self.dummy_sample
														lowerCAmelCase__	       =     0.1 * sample
														lowerCAmelCase__	       =     [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					lowerCAmelCase__	       =     self.get_scheduler_config(**lowerCamelCase_       )
																					lowerCAmelCase__	       =     scheduler_class(**lowerCamelCase_       )
																					scheduler.set_timesteps(lowerCamelCase_       )
																					# copy over dummy past residuals
																					lowerCAmelCase__	       =     dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase_       )
																												lowerCAmelCase__	       =     scheduler_class.from_pretrained(lowerCamelCase_       )
																												new_scheduler.set_timesteps(lowerCamelCase_       )
																												# copy over dummy past residuals
																												lowerCAmelCase__	       =     dummy_past_residuals[: new_scheduler.config.solver_order]

																					lowerCAmelCase__       , lowerCAmelCase__	       =     sample, sample
																					for t in range(lowerCamelCase_ ,   time_step + scheduler.config.solver_order + 1       ):
																												lowerCAmelCase__	       =     scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       ).prev_sample
																												lowerCAmelCase__	       =     new_scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       ).prev_sample

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=0 ,   **lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     dict(self.forward_default_kwargs       )
														lowerCAmelCase__	       =     kwargs.pop('''num_inference_steps''' ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     self.dummy_sample
														lowerCAmelCase__	       =     0.1 * sample
														lowerCAmelCase__	       =     [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					lowerCAmelCase__	       =     self.get_scheduler_config()
																					lowerCAmelCase__	       =     scheduler_class(**lowerCamelCase_       )
																					scheduler.set_timesteps(lowerCamelCase_       )

																					# copy over dummy past residuals (must be after setting timesteps)
																					lowerCAmelCase__	       =     dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase_       )
																												lowerCAmelCase__	       =     scheduler_class.from_pretrained(lowerCamelCase_       )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(lowerCamelCase_       )

																												# copy over dummy past residual (must be after setting timesteps)
																												lowerCAmelCase__	       =     dummy_past_residuals[: new_scheduler.config.solver_order]

																					lowerCAmelCase__	       =     scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       ).prev_sample
																					lowerCAmelCase__	       =     new_scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       ).prev_sample

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=None ,   **lowerCamelCase_       )	->							str:
														if scheduler is None:
																					lowerCAmelCase__	       =     self.scheduler_classes[0]
																					lowerCAmelCase__	       =     self.get_scheduler_config(**lowerCamelCase_       )
																					lowerCAmelCase__	       =     scheduler_class(**lowerCamelCase_       )

														lowerCAmelCase__	       =     self.scheduler_classes[0]
														lowerCAmelCase__	       =     self.get_scheduler_config(**lowerCamelCase_       )
														lowerCAmelCase__	       =     scheduler_class(**lowerCamelCase_       )

														lowerCAmelCase__	       =     10
														lowerCAmelCase__	       =     self.dummy_model()
														lowerCAmelCase__	       =     self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase_       )

														for i, t in enumerate(scheduler.timesteps       ):
																					lowerCAmelCase__	       =     model(lowerCamelCase_ ,   lowerCamelCase_       )
																					lowerCAmelCase__	       =     scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       ).prev_sample

														return sample







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
														lowerCAmelCase__	       =     50
														lowerCAmelCase__	       =     self.dummy_model()
														lowerCAmelCase__	       =     self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase_       )

														# make sure that the first t is uneven
														for i, t in enumerate(scheduler.timesteps[3:]       ):
																					lowerCAmelCase__	       =     model(lowerCamelCase_ ,   lowerCamelCase_       )
																					lowerCAmelCase__	       =     scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       ).prev_sample

														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.2_574       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														# make sure that iterating over schedulers with same config names gives same results
														# for defaults
														lowerCAmelCase__	       =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
														lowerCAmelCase__	       =     self.full_loop(scheduler=lowerCamelCase_       )
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.2_791       ) < 1e-3

														lowerCAmelCase__	       =     DEISMultistepScheduler.from_config(scheduler.config       )
														lowerCAmelCase__	       =     DPMSolverMultistepScheduler.from_config(scheduler.config       )
														lowerCAmelCase__	       =     UniPCMultistepScheduler.from_config(scheduler.config       )
														lowerCAmelCase__	       =     DPMSolverSinglestepScheduler.from_config(scheduler.config       )

														lowerCAmelCase__	       =     self.full_loop(scheduler=lowerCamelCase_       )
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.2_791       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														self.check_over_configs(thresholding=lowerCamelCase_       )
														for order in [1, 2, 3]:
																					for solver_type in ["midpoint", "heun"]:
																												for threshold in [0.5, 1.0, 2.0]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    thresholding=lowerCamelCase_ ,   prediction_type=lowerCamelCase_ ,   sample_max_value=lowerCamelCase_ ,   algorithm_type='''dpmsolver++''' ,   solver_order=lowerCamelCase_ ,   solver_type=lowerCamelCase_ ,   )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																					for solver_type in ["midpoint", "heun"]:
																												for order in [1, 2, 3]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    solver_order=lowerCamelCase_ ,   solver_type=lowerCamelCase_ ,   prediction_type=lowerCamelCase_ ,   algorithm_type=lowerCamelCase_ ,   )
																																										lowerCAmelCase__	       =     self.full_loop(
																																										    solver_order=lowerCamelCase_ ,   solver_type=lowerCamelCase_ ,   prediction_type=lowerCamelCase_ ,   algorithm_type=lowerCamelCase_ ,   )
																																										assert not torch.isnan(lowerCamelCase_       ).any(), "Samples have nan numbers"







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														self.check_over_configs(lower_order_final=lowerCamelCase_       )
														self.check_over_configs(lower_order_final=lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														self.check_over_configs(lambda_min_clipped=-float('''inf'''       )       )
														self.check_over_configs(lambda_min_clipped=-5.1       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														self.check_over_configs(variance_type=lowerCamelCase_       )
														self.check_over_configs(variance_type='''learned_range'''       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																					self.check_over_forward(num_inference_steps=lowerCamelCase_ ,   time_step=0       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     self.full_loop()
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.2_791       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     self.full_loop(use_karras_sigmas=lowerCamelCase_       )
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.2_248       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     self.full_loop(prediction_type='''v_prediction'''       )
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.1_453       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     self.full_loop(prediction_type='''v_prediction''' ,   use_karras_sigmas=lowerCamelCase_       )
														lowerCAmelCase__	       =     torch.mean(torch.abs(lowerCamelCase_       )       )

														assert abs(result_mean.item() - 0.0_649       ) < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.scheduler_classes[0]
														lowerCAmelCase__	       =     self.get_scheduler_config(thresholding=lowerCamelCase_ ,   dynamic_thresholding_ratio=0       )
														lowerCAmelCase__	       =     scheduler_class(**lowerCamelCase_       )

														lowerCAmelCase__	       =     10
														lowerCAmelCase__	       =     self.dummy_model()
														lowerCAmelCase__	       =     self.dummy_sample_deter.half()
														scheduler.set_timesteps(lowerCamelCase_       )

														for i, t in enumerate(scheduler.timesteps       ):
																					lowerCAmelCase__	       =     model(lowerCamelCase_ ,   lowerCamelCase_       )
																					lowerCAmelCase__	       =     scheduler.step(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       ).prev_sample

														assert sample.dtype == torch.floataa



'''simple docstring'''






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


__UpperCAmelCase       =							logging.get_logger(__name__)




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				"linear"
							lowercase__      :       Any     						=				"cosine"
							lowercase__      :       Optional[int]     						=				"cosine_with_restarts"
							lowercase__      :       Optional[Any]     						=				"polynomial"
							lowercase__      :       Tuple     						=				"constant"
							lowercase__      :       Optional[int]     						=				"constant_with_warmup"
							lowercase__      :       Optional[int]     						=				"piecewise_constant"


def  _snake_case       (					A			,   A = -1	)						->   Any:
							return LambdaLR(A			,   lambda A	: 1			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Optional[Any]:

							def lr_lambda(A	):
														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  _snake_case       (					A			,   A			,   A = -1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     {}
							lowerCAmelCase__	       =     step_rules.split(''','''	)
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     rule_str.split(''':'''	)
														lowerCAmelCase__	       =     int(A	)
														lowerCAmelCase__	       =     float(A	)
														lowerCAmelCase__	       =     value
							lowerCAmelCase__	       =     float(rule_list[-1]	)

							def create_rules_function(A			,   A	):
														def rule_func(A	) -> float:
																					lowerCAmelCase__	       =     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

							lowerCAmelCase__	       =     create_rules_function(A			,   A	)

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A			,   A=-1	)						->   Optional[int]:

							def lr_lambda(A	):
														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  _snake_case       (					A			,   A			,   A			,   A = 0.5			,   A = -1	)						->   List[str]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     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  _snake_case       (					A			,   A			,   A			,   A = 1			,   A = -1	)						->   Union[str, Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     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  _snake_case       (					A			,   A			,   A			,   A=1E-7			,   A=1.0			,   A=-1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     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	):
														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:
																					lowerCAmelCase__	       =     lr_init - lr_end
																					lowerCAmelCase__	       =     num_training_steps - num_warmup_steps
																					lowerCAmelCase__	       =     1 - (current_step - num_warmup_steps) / decay_steps
																					lowerCAmelCase__	       =     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A			,   A			,   A	)


__UpperCAmelCase       =							{
    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  _snake_case       (					A			,   A			,   A = None			,   A = None			,   A = None			,   A = 1			,   A = 1.0			,   A = -1			,   )						->   int:
							lowerCAmelCase__	       =     SchedulerType(A	)
							lowerCAmelCase__	       =     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	)