






'''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


UpperCamelCase__			=      logging.get_logger(__name__)


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: List[Any]									=     'linear'
    __lowerCamelCase: Any									=     'cosine'
    __lowerCamelCase: int									=     'cosine_with_restarts'
    __lowerCamelCase: Any									=     'polynomial'
    __lowerCamelCase: Union[str, Any]									=     'constant'
    __lowerCamelCase: List[Any]									=     'constant_with_warmup'
    __lowerCamelCase: Any									=     'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = -1				):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = -1				):


   """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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = -1				):


   """simple docstring"""




   lowercase_						:     str							     =  {}
   lowercase_						:     List[str]							     =  step_rules.split(","				)
   for rule_str in rule_list[:-1]:
      lowercase_     ,		lowercase_						:     List[Any]							     =  rule_str.split(":"				)
      lowercase_						:     Union[str, Any]							     =  int(_UpperCamelCase				)
      lowercase_						:     str							     =  float(_UpperCamelCase				)
      lowercase_						:     List[Any]							     =  value
   lowercase_						:     Optional[int]							     =  float(rule_list[-1]				)

   def create_rules_function(_UpperCamelCase    ,		_UpperCamelCase				):
      def rule_func(_UpperCamelCase				) -> float:
         lowercase_						:     Union[str, Any]							     =  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

   lowercase_						:     Any							     =  create_rules_function(_UpperCamelCase    ,		_UpperCamelCase				)

   return LambdaLR(_UpperCamelCase    ,		_UpperCamelCase    ,		last_epoch=_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=-1				):


   """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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = 0.5    ,		_UpperCamelCase = -1				):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase				):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase				) / float(max(1    ,		_UpperCamelCase				)				)
      lowercase_						:     Tuple							     =  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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = -1				):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase				):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase				) / float(max(1    ,		_UpperCamelCase				)				)
      lowercase_						:     Dict							     =  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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=1e-7    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase=-1				):


   """simple docstring"""




   lowercase_						:     int							     =  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:
         lowercase_						:     Dict							     =  lr_init - lr_end
         lowercase_						:     str							     =  num_training_steps - num_warmup_steps
         lowercase_						:     Any							     =  1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_						:     List[Any]							     =  lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)


UpperCamelCase__			=      {
    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       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = -1    ,		):


   """simple docstring"""




   lowercase_						:     List[str]							     =  SchedulerType(_UpperCamelCase				)
   lowercase_						:     str							     =  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				)












'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  2
   lowercase_						:     Tuple							     =  []
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.append(_UpperCamelCase				)
   if n > 1:
      factors.append(_UpperCamelCase				)
   return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()




