
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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'linear'
	a			:Union[str, Any]         =	'cosine'
	a			:List[str]         =	'cosine_with_restarts'
	a			:Dict         =	'polynomial'
	a			:Tuple         =	'constant'
	a			:int         =	'constant_with_warmup'
	a			:Union[str, Any]         =	'piecewise_constant'




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int = -1					):


				'''simple docstring'''
				return LambdaLR(snake_case__						,   lambda snake_case__					: 1						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int = -1					):


				'''simple docstring'''

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

				return LambdaLR(snake_case__						,   snake_case__						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					str						,   snake_case__:					int = -1					):


				'''simple docstring'''
				lowercase_       =					{}
				lowercase_       =					step_rules.split(''','''					)
				for rule_str in rule_list[:-1]:
								lowercase_  ,	lowercase_       =					rule_str.split(''':'''					)
								lowercase_       =					int(snake_case__					)
								lowercase_       =					float(snake_case__					)
								lowercase_       =					value
				lowercase_       =					float(rule_list[-1]					)

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

								return rule_func

				lowercase_       =					create_rules_function(snake_case__						,   snake_case__					)

				return LambdaLR(snake_case__						,   snake_case__						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					List[str]						,   snake_case__:					List[Any]						,   snake_case__:					Dict						,   snake_case__:					int=-1					):


				'''simple docstring'''

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

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					float = 0.5						,   snake_case__:					int = -1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					List[Any]					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								lowercase_       =					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(snake_case__					) * 2.0 * progress					))					)

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int = 1						,   snake_case__:					int = -1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					Any					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								lowercase_       =					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(snake_case__					) * progress) % 1.0)					))					)

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[str]						,   snake_case__:					Union[str, Any]=1e-7						,   snake_case__:					Tuple=1.0						,   snake_case__:					Optional[Any]=-1					):


				'''simple docstring'''
				lowercase_       =					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(snake_case__:					int					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												lowercase_       =					lr_init - lr_end
												lowercase_       =					num_training_steps - num_warmup_steps
												lowercase_       =					1 - (current_step - num_warmup_steps) / decay_steps
												lowercase_       =					lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)


__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 				a						(							snake_case__:					Union[str, SchedulerType]						,   snake_case__:					Optimizer						,   snake_case__:					Optional[str] = None						,   snake_case__:					Optional[int] = None						,   snake_case__:					Optional[int] = None						,   snake_case__:					int = 1						,   snake_case__:					float = 1.0						,   snake_case__:					int = -1						,   ):


				'''simple docstring'''
				lowercase_       =					SchedulerType(snake_case__					)
				lowercase_       =					TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(snake_case__						,   last_epoch=snake_case__					)

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

				# 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(snake_case__						,   num_warmup_steps=snake_case__						,   last_epoch=snake_case__					)

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

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

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






def 				a						(							snake_case__:					int = 100					):


				'''simple docstring'''
				lowercase_       =					(n * (n + 1) // 2) ** 2
				lowercase_       =					n * (n + 1) * (2 * n + 1) // 6
				return sum_cubes - sum_squares


if __name__ == "__main__":
						print(f"{solution() = }")




