

'''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        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'linear'
   _SCREAMING_SNAKE_CASE   :					int     	=							'cosine'
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							'cosine_with_restarts'
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							'polynomial'
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							'constant'
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							'constant_with_warmup'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'piecewise_constant'







def  _A		(     snake_case		,							snake_case = -1					)      ->     List[str]:
   return LambdaLR(snake_case		,							lambda snake_case					: 1		,							last_epoch=snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case = -1					)      ->     List[Any]:

   def lr_lambda(snake_case					):
      if current_step < num_warmup_steps:
         return float(snake_case					) / float(max(1.0		,							snake_case					)					)
      return 1.0

   return LambdaLR(snake_case		,							snake_case		,							last_epoch=snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case = -1					)      ->     Union[str, Any]:
   _lowercase      :				Any      					=       {}
   _lowercase      :				List[str]      					=       step_rules.split(","					)
   for rule_str in rule_list[:-1]:
      _lowercase					,				_lowercase      :				Dict      					=       rule_str.split(":"					)
      _lowercase      :				List[str]      					=       int(snake_case					)
      _lowercase      :				int      					=       float(snake_case					)
      _lowercase      :				List[Any]      					=       value
   _lowercase      :				List[str]      					=       float(rule_list[-1]					)

   def create_rules_function(snake_case		,							snake_case					):
      def rule_func(snake_case					) -> float:
         _lowercase      :				Optional[int]      					=       sorted(rules_dict.keys()					)
         for i, sorted_step in enumerate(snake_case					):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   _lowercase      :				Any      					=       create_rules_function(snake_case		,							snake_case					)

   return LambdaLR(snake_case		,							snake_case		,							last_epoch=snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=-1					)      ->     List[str]:

   def lr_lambda(snake_case					):
      if current_step < num_warmup_steps:
         return float(snake_case					) / float(max(1		,							snake_case					)					)
      return max(
          0.0		,							float(num_training_steps - current_step					) / float(max(1		,							num_training_steps - num_warmup_steps					)					)					)

   return LambdaLR(snake_case		,							snake_case		,							snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case = 0.5		,							snake_case = -1					)      ->     int:

   def lr_lambda(snake_case					):
      if current_step < num_warmup_steps:
         return float(snake_case					) / float(max(1		,							snake_case					)					)
      _lowercase      :				Optional[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(snake_case					) * 2.0 * progress					))					)

   return LambdaLR(snake_case		,							snake_case		,							snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case = 1		,							snake_case = -1					)      ->     Union[str, Any]:

   def lr_lambda(snake_case					):
      if current_step < num_warmup_steps:
         return float(snake_case					) / float(max(1		,							snake_case					)					)
      _lowercase      :				Optional[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(snake_case					) * progress) % 1.0)					))					)

   return LambdaLR(snake_case		,							snake_case		,							snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=1E-7		,							snake_case=1.0		,							snake_case=-1					)      ->     str:
   _lowercase      :				List[str]      					=       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(snake_case					):
      if current_step < num_warmup_steps:
         return float(snake_case					) / float(max(1		,							snake_case					)					)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         _lowercase      :				Optional[int]      					=       lr_init - lr_end
         _lowercase      :				Dict      					=       num_training_steps - num_warmup_steps
         _lowercase      :				Dict      					=       1 - (current_step - num_warmup_steps) / decay_steps
         _lowercase      :				Any      					=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(snake_case		,							snake_case		,							snake_case					)


_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  _A		(     snake_case		,							snake_case		,							snake_case = None		,							snake_case = None		,							snake_case = None		,							snake_case = 1		,							snake_case = 1.0		,							snake_case = -1		,							)      ->     Tuple:
   _lowercase      :				Optional[int]      					=       SchedulerType(snake_case					)
   _lowercase      :				Tuple      					=       TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(snake_case		,							last_epoch=snake_case					)

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(snake_case		,							step_rules=snake_case		,							last_epoch=snake_case					)

   # 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(snake_case		,							num_warmup_steps=snake_case		,							last_epoch=snake_case					)

   # 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(
          snake_case		,							num_warmup_steps=snake_case		,							num_training_steps=snake_case		,							num_cycles=snake_case		,							last_epoch=snake_case		,							)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          snake_case		,							num_warmup_steps=snake_case		,							num_training_steps=snake_case		,							power=snake_case		,							last_epoch=snake_case		,							)

   return schedule_func(
       snake_case		,							num_warmup_steps=snake_case		,							num_training_steps=snake_case		,							last_epoch=snake_case					)









'''simple docstring'''
def  _A		(     snake_case		,							snake_case					)      ->     int:
   return int((input_a, input_a).count(0					) != 0					)







def  _A		(     )      ->     None:
   assert nand_gate(0		,							0					) == 1
   assert nand_gate(0		,							1					) == 1
   assert nand_gate(1		,							0					) == 1
   assert nand_gate(1		,							1					) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






