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      A							(				_lowerCamelCase     ):
   __UpperCAmelCase : List[str]      =			'linear'
   __UpperCAmelCase : List[str]      =			'cosine'
   __UpperCAmelCase : Optional[Any]      =			'cosine_with_restarts'
   __UpperCAmelCase : Tuple      =			'polynomial'
   __UpperCAmelCase : Union[str, Any]      =			'constant'
   __UpperCAmelCase : List[str]      =			'constant_with_warmup'
   __UpperCAmelCase : int      =			'piecewise_constant'




def    lowerCAmelCase_ (     __A,  __A = -1							)     ->				List[Any]:







      '''simple docstring'''


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




def    lowerCAmelCase_ (     __A,  __A,  __A = -1							)     ->				Tuple:







      '''simple docstring'''


      def lr_lambda(__A							):
            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    lowerCAmelCase_ (     __A,  __A,  __A = -1							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  step_rules.split(","							)
      for rule_str in rule_list[:-1]:
            UpperCAmelCase__		,      UpperCAmelCase__								=  rule_str.split(":"							)
            UpperCAmelCase__								=  int(__A							)
            UpperCAmelCase__								=  float(__A							)
            UpperCAmelCase__								=  value
      UpperCAmelCase__								=  float(rule_list[-1]							)

      def create_rules_function(__A,  __A							):
            def rule_func(__A							) -> float:
                  UpperCAmelCase__								=  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

      UpperCAmelCase__								=  create_rules_function(__A,  __A							)

      return LambdaLR(__A,  __A,  last_epoch=__A							)




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A=-1							)     ->				Dict:







      '''simple docstring'''


      def lr_lambda(__A							):
            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    lowerCAmelCase_ (     __A,  __A,  __A,  __A = 0.5,  __A = -1							)     ->				Optional[int]:







      '''simple docstring'''


      def lr_lambda(__A							):
            if current_step < num_warmup_steps:
                  return float(__A							) / float(max(1,  __A							)							)
            UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A,  __A,  __A = 1,  __A = -1							)     ->				str:







      '''simple docstring'''


      def lr_lambda(__A							):
            if current_step < num_warmup_steps:
                  return float(__A							) / float(max(1,  __A							)							)
            UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A,  __A,  __A=1e-7,  __A=1.0,  __A=-1							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  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(__A							):
            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:
                  UpperCAmelCase__								=  lr_init - lr_end
                  UpperCAmelCase__								=  num_training_steps - num_warmup_steps
                  UpperCAmelCase__								=  1 - (current_step - num_warmup_steps) / decay_steps
                  UpperCAmelCase__								=  lr_range * pct_remaining**power + lr_end
                  return decay / lr_init  # as LambdaLR multiplies by lr_init

      return LambdaLR(__A,  __A,  __A							)


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    lowerCAmelCase_ (     __A,  __A,  __A = None,  __A = None,  __A = None,  __A = 1,  __A = 1.0,  __A = -1,  )     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  SchedulerType(__A							)
      UpperCAmelCase__								=  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							)










from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





