



from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase_									=							TypeVar("T")
UpperCAmelCase_									=							TypeVar("U")





class 		UpperCAmelCase						(						Generic[T, U]				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  key
         _lowerCAmelCase 			=  val
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None





   def __repr__(	self			):
         return (
             F'''Node: key: {self.key}, val: {self.val}, '''
             F'''has next: {bool(self.next			)}, has prev: {bool(self.prev			)}'''
         )






class 		UpperCAmelCase						(						Generic[T, U]				):





   def __init__(	self			):
         _lowerCAmelCase 			=  DoubleLinkedListNode(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  DoubleLinkedListNode(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.rear, self.head





   def __repr__(	self			):
         _lowerCAmelCase 			=  ['''DoubleLinkedList''']
         _lowerCAmelCase 			=  self.head
         while node.next is not None:
               rep.append(str(_lowerCAmelCase			)			)
               _lowerCAmelCase 			=  node.next
         rep.append(str(self.rear			)			)
         return ",\n    ".join(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         _lowerCAmelCase 			=  node
         _lowerCAmelCase 			=  previous
         _lowerCAmelCase 			=  node
         _lowerCAmelCase 			=  self.rear





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):

         if node.prev is None or node.next is None:
               return None

         _lowerCAmelCase 			=  node.next
         _lowerCAmelCase 			=  node.prev
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None
         return node






class 		UpperCAmelCase						(						Generic[T, U]				):
   SCREAMING_SNAKE_CASE__							=     {}





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  DoubleLinkedList()
         _lowerCAmelCase 			=  capacity
         _lowerCAmelCase 			=  0
         _lowerCAmelCase 			=  0
         _lowerCAmelCase 			=  0
         _lowerCAmelCase 			=  {}





   def __repr__(	self			):
         return (
             F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
             F'''capacity={self.capacity}, current size={self.num_keys})'''
         )





   def __contains__(	self       ,     _lowerCAmelCase			):
         return key in self.cache





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
               self.hits += 1
               _lowerCAmelCase 			=  self.cache[key]
               _lowerCAmelCase 			=  self.list.remove(self.cache[key]			)
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_lowerCAmelCase			)
               return node.val
         self.miss += 1
         return None





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):

         if key not in self.cache:
               if self.num_keys >= self.capacity:
                     # delete first node (oldest) when over capacity
                     _lowerCAmelCase 			=  self.list.head.next

                     # guaranteed to have a non-None first node when num_keys > 0
                     # explain to type checker via assertions
                     assert first_node is not None
                     assert first_node.key is not None
                     assert (
                         self.list.remove(_lowerCAmelCase			) is not None
                     )  # node guaranteed to be in list assert node.key is not None

                     del self.cache[first_node.key]
                     self.num_keys -= 1
               _lowerCAmelCase 			=  DoubleLinkedListNode(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.list.add(self.cache[key]			)
               self.num_keys += 1

         else:
               # bump node to the end of the list, update value
               _lowerCAmelCase 			=  self.list.remove(self.cache[key]			)
               assert node is not None  # node guaranteed to be in list
               _lowerCAmelCase 			=  value
               self.list.add(_lowerCAmelCase			)





   @classmethod
   def 	__lowerCAmelCase							(	cls       ,     _lowerCAmelCase = 128			):

         def cache_decorator_inner(_lowerCAmelCase			) -> Callable[..., U]:
               def cache_decorator_wrapper(*_lowerCAmelCase			) -> U:
                     if func not in cls.decorator_function_to_instance_map:
                           _lowerCAmelCase 			=  LRUCache(_lowerCAmelCase			)

                     _lowerCAmelCase 			=  cls.decorator_function_to_instance_map[func].get(args[0]			)
                     if result is None:
                           _lowerCAmelCase 			=  func(*_lowerCAmelCase			)
                           cls.decorator_function_to_instance_map[func].put(args[0]       ,     _lowerCAmelCase			)
                     return result

               def cache_info() -> LRUCache[T, U]:
                     return cls.decorator_function_to_instance_map[func]

               setattr(_lowerCAmelCase       ,     '''cache_info'''       ,     _lowerCAmelCase			)  # noqa: B010

               return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase__		(				*_SCREAMING_SNAKE_CASE   :	Tuple				)->List[Any]:
      if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  list(_SCREAMING_SNAKE_CASE				)
      for i in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Exception				)->bool:
      _lowerCAmelCase 			=  [
          '''CUDA out of memory.''',  # CUDA OOM
          '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
          '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
      ]
      if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	callable = None     ,				_SCREAMING_SNAKE_CASE   :	int = 1_2_8				)->Optional[int]:
      if function is None:
            return functools.partial(_SCREAMING_SNAKE_CASE     ,				starting_batch_size=_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  starting_batch_size

      def decorator(*_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            _lowerCAmelCase 			=  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
            # Guard against user error
            if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
                  _lowerCAmelCase 			=  ''', '''.join([f'''{arg}={value}''' for arg, value in zip(params[1:]     ,				args[1:]				)]				)
                  raise TypeError(
                      f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                      f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError('''No executable batch size found, reached zero.'''				)
                  try:
                        return function(_SCREAMING_SNAKE_CASE     ,				*_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
                  except Exception as e:
                        if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator