
"""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


lowercase_       :						Optional[int]     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""linear"""
     A__      =			"""cosine"""
     A__      =			"""cosine_with_restarts"""
     A__      =			"""polynomial"""
     A__      =			"""constant"""
     A__      =			"""constant_with_warmup"""
     A__      =			"""piecewise_constant"""





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							int = -1				)  ->    str:
  return LambdaLR(lowerCamelCase__,						lambda lowerCamelCase__				: 1,						last_epoch=lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int = -1				)  ->    str:

  def lr_lambda(lowerCamelCase__							:							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    _lowerCAmelCase    (     lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							str,						lowerCamelCase__							:							int = -1				)  ->    Optional[Any]:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = {}
  _SCREAMING_SNAKE_CASE   :				int							    = step_rules.split(","				)
  for rule_str in rule_list[:-1]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = rule_str.split(":"				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = int(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Dict							    = float(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = value
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = float(rule_list[-1]				)

  def create_rules_function(lowerCamelCase__							:							int,						lowerCamelCase__							:							Dict				):
    def rule_func(lowerCamelCase__							:							int				) -> float:
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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

  _SCREAMING_SNAKE_CASE   :				str							    = create_rules_function(lowerCamelCase__,						lowerCamelCase__				)

  return LambdaLR(lowerCamelCase__,						lowerCamelCase__,						last_epoch=lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							str,						lowerCamelCase__							:							Optional[int]=-1				)  ->    str:

  def lr_lambda(lowerCamelCase__							:							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    _lowerCAmelCase    (     lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int,						lowerCamelCase__							:							float = 0.5,						lowerCamelCase__							:							int = -1				)  ->    int:

  def lr_lambda(lowerCamelCase__							:							Any				):
    if current_step < num_warmup_steps:
      return float(lowerCamelCase__				) / float(max(1,						lowerCamelCase__				)				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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    _lowerCAmelCase    (     lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int = 1,						lowerCamelCase__							:							int = -1				)  ->    Optional[Any]:

  def lr_lambda(lowerCamelCase__							:							Union[str, Any]				):
    if current_step < num_warmup_steps:
      return float(lowerCamelCase__				) / float(max(1,						lowerCamelCase__				)				)
    _SCREAMING_SNAKE_CASE   :				Tuple							    = 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    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[Any]=1E-7,						lowerCamelCase__							:							Any=1.0,						lowerCamelCase__							:							Tuple=-1				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[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(lowerCamelCase__							:							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:
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = lr_init - lr_end
      _SCREAMING_SNAKE_CASE   :				List[str]							    = num_training_steps - num_warmup_steps
      _SCREAMING_SNAKE_CASE   :				Tuple							    = 1 - (current_step - num_warmup_steps) / decay_steps
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)


lowercase_       :						List[Any]     					=	{
    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    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, SchedulerType],						lowerCamelCase__							:							Optimizer,						lowerCamelCase__							:							Optional[str] = None,						lowerCamelCase__							:							Optional[int] = None,						lowerCamelCase__							:							Optional[int] = None,						lowerCamelCase__							:							int = 1,						lowerCamelCase__							:							float = 1.0,						lowerCamelCase__							:							int = -1,						)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = SchedulerType(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				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"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



