


'''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


SCREAMING_SNAKE_CASE_   =      logging.get_logger(__name__)


class 			lowerCAmelCase_    (							snake_case__   ):





  """simple docstring"""



  a_							:Tuple				       ='linear'
  a_							:Optional[Any]				       ='cosine'
  a_							:Optional[int]				       ='cosine_with_restarts'
  a_							:Optional[Any]				       ='polynomial'
  a_							:Optional[int]				       ='constant'
  a_							:Dict				       ='constant_with_warmup'
  a_							:Optional[int]				       ='piecewise_constant'




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = -1		)					->       List[str]:
     """simple docstring"""




     return LambdaLR(__A					,	lambda __SCREAMING_SNAKE_CASE		: 1					,	last_epoch=__A		)




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = -1		)					->       List[Any]:
     """simple docstring"""




     def lr_lambda(__SCREAMING_SNAKE_CASE		):
          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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = -1		)					->       int:
     """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(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		):
          def rule_func(__SCREAMING_SNAKE_CASE		) -> 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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE=-1		)					->       str:
     """simple docstring"""




     def lr_lambda(__SCREAMING_SNAKE_CASE		):
          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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = 0.5					,	__SCREAMING_SNAKE_CASE = -1		)					->       Union[str, Any]:
     """simple docstring"""




     def lr_lambda(__SCREAMING_SNAKE_CASE		):
          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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = 1					,	__SCREAMING_SNAKE_CASE = -1		)					->       Any:
     """simple docstring"""




     def lr_lambda(__SCREAMING_SNAKE_CASE		):
          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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE=1E-7					,	__SCREAMING_SNAKE_CASE=1.0					,	__SCREAMING_SNAKE_CASE=-1		)					->       Optional[int]:
     """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(__SCREAMING_SNAKE_CASE		):
          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		)


SCREAMING_SNAKE_CASE_   =      {
    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 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE = None					,	__SCREAMING_SNAKE_CASE = None					,	__SCREAMING_SNAKE_CASE = None					,	__SCREAMING_SNAKE_CASE = 1					,	__SCREAMING_SNAKE_CASE = 1.0					,	__SCREAMING_SNAKE_CASE = -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		)











'''simple docstring'''





class __A     :



 '''simple docstring'''







 def __init__(self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    len(A			)
      _a           =    [0] * len_array

      if len_array > 0:
           _a           =    array[0]

      for i in range(1					,  A			):
           _a           =    self.prefix_sum[i - 1] + array[i]



 def    a__    (self					,  A					,  A			)       ->	int:


      """simple docstring"""



      if start == 0:
           return self.prefix_sum[end]

      return self.prefix_sum[end] - self.prefix_sum[start - 1]



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



      _a           =    {0}
      for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
                return True

           sums.add(A			)

      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()



