



def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       int:





						"""simple docstring"""





						return number | (1 << position)
def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       int:





						"""simple docstring"""





						return number & ~(1 << position)
def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       int:





						"""simple docstring"""





						return number ^ (1 << position)
def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       bool:





						"""simple docstring"""





						return ((number >> position) & 1) == 1
def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       int:





						"""simple docstring"""





						return int((number & (1 << position)) != 0    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()










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_							:      Optional[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""linear"""
	lowerCAmelCase	:   int				=	"""cosine"""
	lowerCAmelCase	:   Dict				=	"""cosine_with_restarts"""
	lowerCAmelCase	:   Optional[Any]				=	"""polynomial"""
	lowerCAmelCase	:   Dict				=	"""constant"""
	lowerCAmelCase	:   Any				=	"""constant_with_warmup"""
	lowerCAmelCase	:   Union[str, Any]				=	"""piecewise_constant"""





def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						return LambdaLR(_A			,							lambda _A    : 1			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int = -1    )->       Optional[Any]:





						"""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    )->       Dict:





						"""simple docstring"""





						A__							=       {}
						A__							=       step_rules.split(","    )
						for rule_str in rule_list[:-1]:
												A__						,       A__							=       rule_str.split(":"    )
												A__							=       int(_A    )
												A__							=       float(_A    )
												A__							=       value
						A__							=       float(rule_list[-1]    )

						def create_rules_function(_A			:				Any			,							_A			:				Optional[int]    ):
												def rule_func(_A			:				int    ) -> float:
																		A__							=       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__							=       create_rules_function(_A			,							_A    )

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str			,							_A			:				str=-1    )->       Tuple:





						"""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    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       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    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       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			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Tuple=1E-7			,							_A			:				Dict=1.0			,							_A			:				Union[str, Any]=-1    )->       Any:





						"""simple docstring"""





						A__							=       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__							=       lr_init - lr_end
																		A__							=       num_training_steps - num_warmup_steps
																		A__							=       1 - (current_step - num_warmup_steps) / decay_steps
																		A__							=       lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A			,							_A			,							_A    )


UpperCAmelCase_							:      Any      =  {
    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			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       SchedulerType(_A    )
						A__							=       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    )





