






'''simple docstring'''







from __future__ import annotations

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

__A      :       List[Any]      =	TypeVar('T')
__A      :       Tuple      =	TypeVar('U')






class 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     def __init__(self    :	int				,				lowercase__    :	T | None				,				lowercase__    :	U | None					)  ->       str:
           __A		   =      key
           __A		   =      val
           __A		   =      0
           __A		   =      None
           __A		   =      None






     def __repr__(self    :	Optional[Any]					)  ->       str:
           return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
               self.key				,				self.val				,				self.freq				,				self.next is not None				,				self.prev is not None					)







class 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     def __init__(self    :	Union[str, Any]					)  ->       None:
           __A		   =      DoubleLinkedListNode(lowercase__				,				lowercase__					)
           __A		   =      DoubleLinkedListNode(lowercase__				,				lowercase__					)
           __A   ,				__A		   =      self.rear, self.head






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






     def     _UpperCamelCase   (self    :	List[str]				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       None:
           __A		   =      self.rear.prev

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

           __A		   =      node
           __A		   =      previous
           __A		   =      node
           __A		   =      self.rear
           node.freq += 1
           self._position_node(lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       None:

           while node.prev is not None and node.prev.freq > node.freq:
                 # swap node with previous node
                 __A		   =      node.prev

                 __A		   =      previous_node.prev
                 __A		   =      node.prev
                 __A		   =      previous_node
                 __A		   =      node






     def     _UpperCamelCase   (self    :	Dict				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       DoubleLinkedListNode[T, U] | None:

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

           __A		   =      node.next
           __A		   =      node.prev
           __A		   =      None
           __A		   =      None
           return node







class 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     _snake_case             =  {}






     def __init__(self    :	Optional[int]				,				lowercase__    :	int					)  ->       Optional[Any]:
           __A		   =      DoubleLinkedList()
           __A		   =      capacity
           __A		   =      0
           __A		   =      0
           __A		   =      0
           __A		   =      {}






     def __repr__(self    :	int					)  ->       str:
           return (
               f"CacheInfo(hits={self.hits}, misses={self.miss}, "
               f"capacity={self.capacity}, current_size={self.num_keys})"
           )






     def __contains__(self    :	List[Any]				,				lowercase__    :	T					)  ->       bool:
           return key in self.cache






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	T					)  ->       U | None:

           if key in self.cache:
                 self.hits += 1
                 __A		   =      self.cache[key]
                 __A		   =      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(lowercase__					)
                 return node.val
           self.miss += 1
           return None






     def     _UpperCamelCase   (self    :	int				,				lowercase__    :	T				,				lowercase__    :	U					)  ->       None:

           if key not in self.cache:
                 if self.num_keys >= self.capacity:
                       # delete first node when over capacity
                       __A		   =      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(lowercase__					) is not None
                       # first_node guaranteed to be in list

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

           else:
                 __A		   =      self.list.remove(self.cache[key]					)
                 assert node is not None  # node guaranteed to be in list
                 __A		   =      value
                 self.list.add(lowercase__					)






     @classmethod
     def     _UpperCamelCase   (cls    :	type[LFUCache[T, U]]				,				lowercase__    :	int = 128					)  ->       Callable[[Callable[[T], U]], Callable[..., U]]:

           def cache_decorator_inner(lowercase__    :	Callable[[T], U]					) -> Callable[..., U]:
                 def cache_decorator_wrapper(*lowercase__    :	T					) -> U:
                       if func not in cls.decorator_function_to_instance_map:
                             __A		   =      LFUCache(lowercase__					)

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

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

                 setattr(lowercase__				,				"cache_info"				,				lowercase__					)  # noqa: B010

                 return cache_decorator_wrapper

           return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()










'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __A		   =      ArgumentParser(
          description=(
              "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
          )							)

      # Optional arguments for the launch helper
      parser.add_argument("--num_cores"							,			type=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

      # positional
      parser.add_argument(
          "training_script"							,			type=snake_case__							,			help=(
              "The full path to the single TPU training "
              "program/script to be launched in parallel, "
              "followed by all the arguments for the "
              "training script"
          )							,			)

      # rest from the training program
      parser.add_argument("training_script_args"							,			nargs=snake_case__							)

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __A		   =      parse_args()

      # Import training_script as a module.
      __A		   =      Path(args.training_script							)
      sys.path.append(str(script_fpath.parent.resolve()							)							)
      __A		   =      script_fpath.stem
      __A		   =      importlib.import_module(snake_case__							)

      # Patch sys.argv
      __A		   =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores							)]

      xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores							)


if __name__ == "__main__":
       main()


