


'''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


lowercase__				     =		logging.get_logger(__name__)





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
					UpperCAmelCase 					=				'''linear'''
					UpperCAmelCase 					=				'''cosine'''
					UpperCAmelCase 					=				'''cosine_with_restarts'''
					UpperCAmelCase 					=				'''polynomial'''
					UpperCAmelCase 					=				'''constant'''
					UpperCAmelCase 					=				'''constant_with_warmup'''
					UpperCAmelCase 					=				'''piecewise_constant'''







def __UpperCamelCase   (			__lowerCamelCase		: Optimizer      ,  __lowerCamelCase		: int = -1				)      ->     List[str]:







					'''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Optimizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int = -1				)      ->     str:







					'''simple docstring'''






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

					return LambdaLR(__lowerCamelCase      ,  __lowerCamelCase      ,  last_epoch=__lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Optimizer      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: int = -1				)      ->     str:







					'''simple docstring'''






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

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

										return rule_func

					_a						=		create_rules_function(__lowerCamelCase      ,  __lowerCamelCase				)

					return LambdaLR(__lowerCamelCase      ,  __lowerCamelCase      ,  last_epoch=__lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Dict      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str=-1				)      ->     Optional[int]:







					'''simple docstring'''






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

					return LambdaLR(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Optimizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: float = 0.5      ,  __lowerCamelCase		: int = -1				)      ->     List[Any]:







					'''simple docstring'''






					def lr_lambda(__lowerCamelCase		: Dict				):
										if current_step < num_warmup_steps:
															return float(__lowerCamelCase				) / float(max(1      ,  __lowerCamelCase				)				)
										_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(__lowerCamelCase				) * 2.0 * progress				))				)

					return LambdaLR(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Optimizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int = 1      ,  __lowerCamelCase		: int = -1				)      ->     Any:







					'''simple docstring'''






					def lr_lambda(__lowerCamelCase		: Optional[Any]				):
										if current_step < num_warmup_steps:
															return float(__lowerCamelCase				) / float(max(1      ,  __lowerCamelCase				)				)
										_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(__lowerCamelCase				) * progress) % 1.0)				))				)

					return LambdaLR(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[int]=1E-7      ,  __lowerCamelCase		: Dict=1.0      ,  __lowerCamelCase		: Optional[Any]=-1				)      ->     Union[str, 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(__lowerCamelCase		: int				):
										if current_step < num_warmup_steps:
															return float(__lowerCamelCase				) / float(max(1      ,  __lowerCamelCase				)				)
										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(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)


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







					'''simple docstring'''






					_a						=		SchedulerType(__lowerCamelCase				)
					_a						=		TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(__lowerCamelCase      ,  last_epoch=__lowerCamelCase				)

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

					# 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(__lowerCamelCase      ,  num_warmup_steps=__lowerCamelCase      ,  last_epoch=__lowerCamelCase				)

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

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

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





'''simple docstring'''

def __UpperCamelCase   (			__lowerCamelCase		: int = 400_0000				)      ->     int:







     '''simple docstring'''






     _a						=		[]
     _a			,			_a						=		0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(__lowerCamelCase				)
          _a			,			_a						=		b, a + b
     return sum(__lowerCamelCase				)


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

