
from __future__ import annotations

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

__magic_name__  :  Union[str, Any]     			=   TypeVar('T')
__magic_name__  :  Tuple     			=   TypeVar('U')


class 							lowerCamelCase       (		Generic[T, U]					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					key
      A_								=					val
      A_								=					0
      A_								=					None
      A_								=					None

  def __repr__(					self					):
      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 							lowerCamelCase       (		Generic[T, U]					):



  """simple docstring"""





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

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

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      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(__UpperCAmelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):



      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 					lowercase_						(					self						,    __UpperCamelCase					):



      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 							lowerCamelCase       (		Generic[T, U]					):



  """simple docstring"""





  lowerCAmelCase_								=		{}

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

  def __repr__(					self					):
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )

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

  def 					lowercase_						(					self						,    __UpperCamelCase					):



      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(__UpperCAmelCase					)
          return node.val
      self.miss += 1
      return None

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):



      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(__UpperCAmelCase					) is not None
              # first_node guaranteed to be in list

              del self.cache[first_node.key]
              self.num_keys -= 1
          A_								=					DoubleLinkedListNode(__UpperCAmelCase						,    __UpperCAmelCase					)
          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(__UpperCAmelCase					)

  @classmethod
  def 					lowercase_						(					cls						,    __UpperCamelCase = 128					):



      def cache_decorator_inner(__UpperCamelCase					) -> Callable[..., U]:
          def cache_decorator_wrapper(*__UpperCamelCase					) -> U:
              if func not in cls.decorator_function_to_instance_map:
                  A_								=					LFUCache(__UpperCAmelCase					)

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

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

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

          return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()












from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			if not inplace:
						UpperCamelCase_     				= ma + ma
			else:
						ma += ma

			return ma




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= -1 * len(__snake_case					)
			UpperCamelCase_     				= list(range(len(tensor.shape[:zero_index]					)					)					)
			return tensor.permute(first_inds + [zero_index + i for i in inds]					)




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			return t.reshape(t.shape[:-no_dims] + (-1,)					)




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case = 1E-4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= mask.expand(*value.shape					)
			return torch.sum(mask * value     ,		dim=__snake_case					) / (eps + torch.sum(__snake_case     ,		dim=__snake_case					))




def  a_		(							__snake_case     ,		__snake_case = 2.31_25     ,		__snake_case = 21.68_75     ,		__snake_case = 6_4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= torch.linspace(__snake_case     ,		__snake_case     ,		no_bins - 1     ,		device=pts.device					)
			UpperCamelCase_     				= torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2     ,		dim=-1					)					)
			return torch.bucketize(__snake_case     ,		__snake_case					)




def  a_		(							__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			UpperCamelCase_     				= dicts[0]
			UpperCamelCase_     				= {}
			for k, v in first.items():
						UpperCamelCase_     				= [d[k] for d in dicts]
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_multimap(__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= fn(__snake_case					)

			return new_dict




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= v_bins.view(((1,) * len(x.shape					)) + (len(__snake_case					),)					)
			UpperCamelCase_     				= x[..., None] - reshaped_bins
			UpperCamelCase_     				= torch.argmin(torch.abs(__snake_case					)     ,		dim=-1					)
			return nn.functional.one_hot(__snake_case     ,		num_classes=len(__snake_case					)					).float()




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case = 0     ,		__snake_case = 0					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= []
			for i, s in enumerate(data.shape[:no_batch_dims]					):
						UpperCamelCase_     				= torch.arange(__snake_case					)
						UpperCamelCase_     				= r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
						ranges.append(__snake_case					)

			UpperCamelCase_     				= [slice(__snake_case					) for _ in range(len(data.shape					) - no_batch_dims					)]
			UpperCamelCase_     				= inds
			ranges.extend(__snake_case					)
			# Matt note: Editing this to get around the behaviour of using a list as an array index changing
			# in recent Numpy versions
			return data[tuple(__snake_case					)]


__a			:	List[Any]							     =		TypeVar("""T""")




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Dict[Any, Union[dict, list, tuple, Any]]:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for k, v in dic.items():
						if isinstance(__snake_case     ,		__snake_case					):
									UpperCamelCase_     				= dict_map(__snake_case     ,		__snake_case     ,		__snake_case					)
						else:
									UpperCamelCase_     				= tree_map(__snake_case     ,		__snake_case     ,		__snake_case					)

			return new_dict




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			list:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			tuple:

			'''simple docstring'''


			...




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			if isinstance(__snake_case     ,		__snake_case					):
						return dict_map(__snake_case     ,		__snake_case     ,		__snake_case					)
			elif isinstance(__snake_case     ,		__snake_case					):
						return [tree_map(__snake_case     ,		__snake_case     ,		__snake_case					) for x in tree]
			elif isinstance(__snake_case     ,		__snake_case					):
						return tuple(tree_map(__snake_case     ,		__snake_case     ,		__snake_case					) for x in tree					)
			elif isinstance(__snake_case     ,		__snake_case					):
						return fn(__snake_case					)
			else:
						print(type(__snake_case					)					)
						raise ValueError('Not supported'					)





__a			:	Tuple							     =		partial(tree_map, leaf_type=torch.Tensor)
