



'''simple docstring'''





def   a_   (		_UpperCAmelCase    :      int				)    ->  bool:

      if number < 0:
            raise ValueError('number must not be negative'				)
      return number & (number - 1) == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()









'''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


a    :							Any										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"linear"
				snake_case_           =						"cosine"
				snake_case_           =						"cosine_with_restarts"
				snake_case_           =						"polynomial"
				snake_case_           =						"constant"
				snake_case_           =						"constant_with_warmup"
				snake_case_           =						"piecewise_constant"







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							{}
					snake_case_		     =							step_rules.split(''','''      )
					for rule_str in rule_list[:-1]:
										snake_case_     ,snake_case_		     =							rule_str.split(''':'''      )
										snake_case_		     =							int(__UpperCAmelCase      )
										snake_case_		     =							float(__UpperCAmelCase      )
										snake_case_		     =							value
					snake_case_		     =							float(rule_list[-1]      )

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

										return rule_func

					snake_case_		     =							create_rules_function(__UpperCAmelCase, __UpperCAmelCase      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=-1      )						->   List[Any]:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.5, __UpperCAmelCase = -1      )						->   Optional[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										snake_case_		     =							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(__UpperCAmelCase      ) * 2.0 * progress      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 1, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-7, __UpperCAmelCase=1.0, __UpperCAmelCase=-1      )						->   str:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


a    :							Tuple										=    {
    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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 1, __UpperCAmelCase = 1.0, __UpperCAmelCase = -1, )						->   int:

					'''simple docstring'''

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

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

					# 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(__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

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

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

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

