




"""simple docstring"""


import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)






class 					lowercase(     nn.Module ):







 '''simple docstring'''





 def __init__(				self:		List[str]		):




   '''simple docstring'''



   super().__init__()
   _snake_case							:		Optional[Any]								=  nn.Linear(3,      4		)
   _snake_case							:		List[Any]								=  nn.BatchNormad(4		)
   _snake_case							:		Any								=  nn.Linear(4,      5		)


 def  UpperCamelCase_   (				self:		str,      a_:		Optional[Any]		):




   '''simple docstring'''



   return self.lineara(self.batchnorm(self.lineara(a_		)		)		)







class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  ModelForTest()
   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,      model.state_dict()		)
     _snake_case							:		Dict								=  os.path.join(a_,      """index.json"""		)
     self.assertTrue(os.path.isfile(a_		)		)
     # TODO: add tests on what is inside the index

     for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
       _snake_case							:		Tuple								=  os.path.join(a_,      f"{key}.dat"		)
       self.assertTrue(os.path.isfile(a_		)		)
       # TODO: add tests on the fact weights are properly loaded


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  [torch.floataa, torch.floataa, torch.bfloataa]

   for dtype in dtypes:
     _snake_case							:		List[Any]								=  torch.randn(2,      3,      dtype=a_		)
     with TemporaryDirectory() as tmp_dir:
       _snake_case							:		Optional[int]								=  offload_weight(a_,      """weight""",      a_,      {}		)
       _snake_case							:		Optional[int]								=  os.path.join(a_,      """weight.dat"""		)
       self.assertTrue(os.path.isfile(a_		)		)
       self.assertDictEqual(a_,      {"""weight""": {"""shape""": [2, 3], """dtype""": str(a_		).split("""."""		)[1]}}		)

       _snake_case							:		int								=  load_offloaded_weight(a_,      index["""weight"""]		)
       self.assertTrue(torch.equal(a_,      a_		)		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		int								=  ModelForTest()
   _snake_case							:		int								=  model.state_dict()
   _snake_case							:		Any								=  {k: v for k, v in state_dict.items() if """linear2""" not in k}
   _snake_case							:		Optional[int]								=  {k: v for k, v in state_dict.items() if """linear2""" in k}

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,      a_		)
     _snake_case							:		Optional[int]								=  OffloadedWeightsLoader(state_dict=a_,      save_folder=a_		)

     # Every key is there with the right value
     self.assertEqual(sorted(a_		),      sorted(state_dict.keys()		)		)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,      weight_map[key]		)		)

   _snake_case							:		Tuple								=  {k: v for k, v in state_dict.items() if """weight""" in k}
   _snake_case							:		List[str]								=  {k: v for k, v in state_dict.items() if """weight""" not in k}

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,      a_		)
     _snake_case							:		Dict								=  OffloadedWeightsLoader(state_dict=a_,      save_folder=a_		)

     # Every key is there with the right value
     self.assertEqual(sorted(a_		),      sorted(state_dict.keys()		)		)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,      weight_map[key]		)		)

   with TemporaryDirectory() as tmp_dir:
     offload_state_dict(a_,      a_		)
     # Duplicates are removed
     _snake_case							:		int								=  OffloadedWeightsLoader(state_dict=a_,      save_folder=a_		)

     # Every key is there with the right value
     self.assertEqual(sorted(a_		),      sorted(state_dict.keys()		)		)
     for key, param in state_dict.items():
       self.assertTrue(torch.allclose(a_,      weight_map[key]		)		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
   _snake_case							:		Tuple								=  extract_submodules_state_dict(a_,      ["""a.1""", """a.2"""]		)
   self.assertDictEqual(a_,      {"""a.1""": 0, """a.2""": 2}		)

   _snake_case							:		Optional[int]								=  {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
   _snake_case							:		Tuple								=  extract_submodules_state_dict(a_,      ["""a.1""", """a.2"""]		)
   self.assertDictEqual(a_,      {"""a.1.a""": 0, """a.2.a""": 2}		)






"""simple docstring"""







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_nodes(node.left				) + count_nodes(node.right				) + 1

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_coins(node.left				) + count_coins(node.right				) + node.data

  if count_nodes(SCREAMING_SNAKE_CASE				) != count_coins(SCREAMING_SNAKE_CASE				):
    raise ValueError("The nodes number should be same as the number of coins"				)

  # Main calculation
  def get_distrib(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

    if node is None:
      return CoinsDistribResult(0				,      1				)

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							node.data - coins_to_left - coins_to_right

    return CoinsDistribResult(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  return get_distrib(SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





