




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


lowerCamelCase       :	Dict				  =     logging.get_logger(__name__)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''linear'''
 UpperCamelCase		     =		'''cosine'''
 UpperCamelCase		     =		'''cosine_with_restarts'''
 UpperCamelCase		     =		'''polynomial'''
 UpperCamelCase		     =		'''constant'''
 UpperCamelCase		     =		'''constant_with_warmup'''
 UpperCamelCase		     =		'''piecewise_constant'''

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optimizer			,							lowercase			: int = -1  ):


      '''simple docstring'''



      return LambdaLR(_lowerCAmelCase			,							lambda lowercase  : 1			,							last_epoch=_lowerCAmelCase  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optimizer			,							lowercase			: int			,							lowercase			: int = -1  ):


      '''simple docstring'''



      def lr_lambda(lowercase			: 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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optimizer			,							lowercase			: str			,							lowercase			: int = -1  ):


      '''simple docstring'''



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

      def create_rules_function(lowercase			: str			,							lowercase			: Optional[Any]  ):
            def rule_func(lowercase			: int  ) -> float:
                  lowerCamelCase_	  =					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

      lowerCamelCase_	  =					create_rules_function(_lowerCAmelCase			,							_lowerCAmelCase  )

      return LambdaLR(_lowerCAmelCase			,							_lowerCAmelCase			,							last_epoch=_lowerCAmelCase  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: List[Any]			,							lowercase			: List[str]			,							lowercase			: Dict=-1  ):


      '''simple docstring'''



      def lr_lambda(lowercase			: 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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optimizer			,							lowercase			: int			,							lowercase			: int			,							lowercase			: float = 0.5			,							lowercase			: int = -1  ):


      '''simple docstring'''



      def lr_lambda(lowercase			: Union[str, Any]  ):
            if current_step < num_warmup_steps:
                  return float(_lowerCAmelCase  ) / float(max(1			,							_lowerCAmelCase  )  )
            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(_lowerCAmelCase  ) * 2.0 * progress  ))  )

      return LambdaLR(_lowerCAmelCase			,							_lowerCAmelCase			,							_lowerCAmelCase  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optimizer			,							lowercase			: int			,							lowercase			: int			,							lowercase			: int = 1			,							lowercase			: int = -1  ):


      '''simple docstring'''



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

      return LambdaLR(_lowerCAmelCase			,							_lowerCAmelCase			,							_lowerCAmelCase  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any			,							lowercase			: str			,							lowercase			: Union[str, Any]			,							lowercase			: Union[str, Any]=1e-7			,							lowercase			: Tuple=1.0			,							lowercase			: int=-1  ):


      '''simple docstring'''



      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(lowercase			: 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:
                  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(_lowerCAmelCase			,							_lowerCAmelCase			,							_lowerCAmelCase  )


lowerCamelCase       :	Optional[int]				  =     {
    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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, SchedulerType]			,							lowercase			: Optimizer			,							lowercase			: Optional[str] = None			,							lowercase			: Optional[int] = None			,							lowercase			: Optional[int] = None			,							lowercase			: int = 1			,							lowercase			: float = 1.0			,							lowercase			: int = -1			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					SchedulerType(_lowerCAmelCase  )
      lowerCamelCase_	  =					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  )









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



