



'''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  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Tuple ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Linear(3  ,						4 )
       UpperCamelCase					=  nn.BatchNormad(4 )
       UpperCamelCase					=  nn.Linear(4  ,						5 )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__ ) ) )

class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  ModelForTest()
       with TemporaryDirectory() as tmp_dir:
          offload_state_dict(UpperCamelCase__  ,						model.state_dict() )
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'index.json' )
          self.assertTrue(os.path.isfile(UpperCamelCase__ ) )
          # TODO: add tests on what is inside the index

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






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  [torch.floataa, torch.floataa, torch.bfloataa]

       for dtype in dtypes:
          UpperCamelCase					=  torch.randn(2  ,						3  ,						dtype=UpperCamelCase__ )
          with TemporaryDirectory() as tmp_dir:
             UpperCamelCase					=  offload_weight(UpperCamelCase__  ,						'weight'  ,						UpperCamelCase__  ,						{} )
             UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'weight.dat' )
             self.assertTrue(os.path.isfile(UpperCamelCase__ ) )
             self.assertDictEqual(UpperCamelCase__  ,						{'weight': {'shape': [2, 3], 'dtype': str(UpperCamelCase__ ).split('.' )[1]}} )

             UpperCamelCase					=  load_offloaded_weight(UpperCamelCase__  ,						index['weight'] )
             self.assertTrue(torch.equal(UpperCamelCase__  ,						UpperCamelCase__ ) )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ModelForTest()
       UpperCamelCase					=  model.state_dict()
       UpperCamelCase					=  {k: v for k, v in state_dict.items() if 'linear2' not in k}
       UpperCamelCase					=  {k: v for k, v in state_dict.items() if 'linear2' in k}

       with TemporaryDirectory() as tmp_dir:
          offload_state_dict(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  OffloadedWeightsLoader(state_dict=UpperCamelCase__  ,						save_folder=UpperCamelCase__ )

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

       UpperCamelCase					=  {k: v for k, v in state_dict.items() if 'weight' in k}
       UpperCamelCase					=  {k: v for k, v in state_dict.items() if 'weight' not in k}

       with TemporaryDirectory() as tmp_dir:
          offload_state_dict(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  OffloadedWeightsLoader(state_dict=UpperCamelCase__  ,						save_folder=UpperCamelCase__ )

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

       with TemporaryDirectory() as tmp_dir:
          offload_state_dict(UpperCamelCase__  ,						UpperCamelCase__ )
          # Duplicates are removed
          UpperCamelCase					=  OffloadedWeightsLoader(state_dict=UpperCamelCase__  ,						save_folder=UpperCamelCase__ )

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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  {'a.1': 0, 'a.10': 1, 'a.2': 2}
       UpperCamelCase					=  extract_submodules_state_dict(UpperCamelCase__  ,						['a.1', 'a.2'] )
       self.assertDictEqual(UpperCamelCase__  ,						{'a.1': 0, 'a.2': 2} )

       UpperCamelCase					=  {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
       UpperCamelCase					=  extract_submodules_state_dict(UpperCamelCase__  ,						['a.1', 'a.2'] )
       self.assertDictEqual(UpperCamelCase__  ,						{'a.1.a': 0, 'a.2.a': 2} )






'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 50    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  [1] * (length + 1)

   for row_length in range(3    ,						length + 1    ):
      for block_length in range(3    ,						row_length + 1    ):
         for block_start in range(row_length - block_length    ):
            ways_number[row_length] += ways_number[
                row_length - block_start - block_length - 1
            ]

         ways_number[row_length] += 1

   return ways_number[length]


if __name__ == "__main__":
   print(f'''{solution() = }''')

