
'''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


snake_case									=    logging.get_logger(__name__)




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				str	=			"""linear"""
 A_				:				Dict	=			"""cosine"""
 A_				:				Union[str, Any]	=			"""cosine_with_restarts"""
 A_				:				Optional[Any]	=			"""polynomial"""
 A_				:				int	=			"""constant"""
 A_				:				Optional[int]	=			"""constant_with_warmup"""
 A_				:				Optional[int]	=			"""piecewise_constant"""



def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_ = -1			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = -1			):







 """simple docstring"""


 def lr_lambda(lowerCamelCase_			):
  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_      ,			lowerCamelCase_      ,			lowerCamelCase_ = -1			):







 """simple docstring"""


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

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

 return LambdaLR(lowerCamelCase_      ,			lowerCamelCase_      ,			last_epoch=lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=-1			):







 """simple docstring"""


 def lr_lambda(lowerCamelCase_			):
  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_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = 0.5      ,			lowerCamelCase_ = -1			):







 """simple docstring"""


 def lr_lambda(lowerCamelCase_			):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase_			) / float(max(1      ,			lowerCamelCase_			)			)
  lowerCAmelCase__     :   List[str]     		=	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_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = 1      ,			lowerCamelCase_ = -1			):







 """simple docstring"""


 def lr_lambda(lowerCamelCase_			):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase_			) / float(max(1      ,			lowerCamelCase_			)			)
  lowerCAmelCase__     :   Any     		=	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_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=1e-7      ,			lowerCamelCase_=1.0      ,			lowerCamelCase_=-1			):







 """simple docstring"""


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

 return LambdaLR(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)


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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = None      ,			lowerCamelCase_ = None      ,			lowerCamelCase_ = None      ,			lowerCamelCase_ = 1      ,			lowerCamelCase_ = 1.0      ,			lowerCamelCase_ = -1      ,			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	SchedulerType(lowerCamelCase_			)
 lowerCAmelCase__     :   Tuple     		=	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'''

import warnings
from functools import wraps
from typing import Callable


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 @wraps(lowerCamelCase_			)
 def _inner_fn(*lowerCamelCase_      ,			**lowerCamelCase_			):
  warnings.warn(
      (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,			lowerCamelCase_      ,			)
  return fn(*lowerCamelCase_      ,			**lowerCamelCase_			)

 return _inner_fn





