





'''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


__lowerCamelCase						: Optional[Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""linear"""
  lowerCAmelCase_					=				"""cosine"""
  lowerCAmelCase_					=				"""cosine_with_restarts"""
  lowerCAmelCase_					=				"""polynomial"""
  lowerCAmelCase_					=				"""constant"""
  lowerCAmelCase_					=				"""constant_with_warmup"""
  lowerCAmelCase_					=				"""piecewise_constant"""




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = -1   ):


  """simple docstring"""

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


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


  """simple docstring"""

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

  return LambdaLR(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_SNAKE_CASE   )


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


  """simple docstring"""

  _UpperCamelCase       			={}
  _UpperCamelCase       			=step_rules.split(''','''   )
  for rule_str in rule_list[:-1]:
    _UpperCamelCase			,     _UpperCamelCase       			=rule_str.split(''':'''   )
    _UpperCamelCase       			=int(__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=float(__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=value
  _UpperCamelCase       			=float(rule_list[-1]   )

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

    return rule_func

  _UpperCamelCase       			=create_rules_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return LambdaLR(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_SNAKE_CASE   )


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


  """simple docstring"""

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

  return LambdaLR(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 0.5  ,      __SCREAMING_SNAKE_CASE = -1   ):


  """simple docstring"""

  def lr_lambda(__SCREAMING_SNAKE_CASE   ):
    if current_step < num_warmup_steps:
      return float(__SCREAMING_SNAKE_CASE   ) / float(max(1  ,      __SCREAMING_SNAKE_CASE   )   )
    _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(__SCREAMING_SNAKE_CASE   ) * 2.0 * progress   ))   )

  return LambdaLR(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


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


  """simple docstring"""

  def lr_lambda(__SCREAMING_SNAKE_CASE   ):
    if current_step < num_warmup_steps:
      return float(__SCREAMING_SNAKE_CASE   ) / float(max(1  ,      __SCREAMING_SNAKE_CASE   )   )
    _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(__SCREAMING_SNAKE_CASE   ) * progress) % 1.0)   ))   )

  return LambdaLR(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=1E-7  ,      __SCREAMING_SNAKE_CASE=1.0  ,      __SCREAMING_SNAKE_CASE=-1   ):


  """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(__SCREAMING_SNAKE_CASE   ):
    if current_step < num_warmup_steps:
      return float(__SCREAMING_SNAKE_CASE   ) / float(max(1  ,      __SCREAMING_SNAKE_CASE   )   )
    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(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


__lowerCamelCase						: str		=   {
    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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = None  ,      __SCREAMING_SNAKE_CASE = None  ,      __SCREAMING_SNAKE_CASE = None  ,      __SCREAMING_SNAKE_CASE = 1  ,      __SCREAMING_SNAKE_CASE = 1.0  ,      __SCREAMING_SNAKE_CASE = -1  ,      ):


  """simple docstring"""

  _UpperCamelCase       			=SchedulerType(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(__SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_SNAKE_CASE   )

  if name == SchedulerType.PIECEWISE_CONSTANT:
    return schedule_func(__SCREAMING_SNAKE_CASE  ,      step_rules=__SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_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(__SCREAMING_SNAKE_CASE  ,      num_warmup_steps=__SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_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(
        __SCREAMING_SNAKE_CASE  ,      num_warmup_steps=__SCREAMING_SNAKE_CASE  ,      num_training_steps=__SCREAMING_SNAKE_CASE  ,      num_cycles=__SCREAMING_SNAKE_CASE  ,      last_epoch=__SCREAMING_SNAKE_CASE  ,      )

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

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













'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__lowerCamelCase						: Optional[int]		=   TypeVar('T')







class 		UpperCAmelCase   (				Generic[T]):


  """simple docstring"""







  lowerCAmelCase_					=				42  # Cache store of keys
  lowerCAmelCase_					=				42  # References of the keys in cache
  lowerCAmelCase_					=				10  # Maximum capacity of cache


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					int  )     ->						None:
    _UpperCamelCase       			=deque()
    _UpperCamelCase       			=set()
    if not n:
      _UpperCamelCase       			=sys.maxsize
    elif n < 0:
      raise ValueError('''n should be an integer greater than 0.'''  )
    else:
      _UpperCamelCase       			=n


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					T  )     ->						None:
    if x not in self.key_reference:
      if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
        _UpperCamelCase       			=self.dq_store.pop()
        self.key_reference.remove(UpperCamelCase__  )
    else:
      self.dq_store.remove(UpperCamelCase__  )

    self.dq_store.appendleft(UpperCamelCase__  )
    self.key_reference.add(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						None:
    for k in self.dq_store:
      print(UpperCamelCase__  )




  def __repr__(				self    :					str  )     ->						str:
    return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase						: LRUCache[str | int]		=   LRUCache(4)
      lru_cache.refer('A')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('A')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






