






"""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  lowerCamelCase__ (	nn.Module			):






    def __init__(			self ):
          super().__init__()
          UpperCAmelCase          =	nn.Linear(3     ,4 )
          UpperCAmelCase          =	nn.BatchNormad(4 )
          UpperCAmelCase          =	nn.Linear(4     ,5 )






    def     _UpperCamelCase							(			self     ,A ):
          return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_ ) ) )







class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self ):
          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"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if (voltage, current, resistance).count(0							) != 1:
            raise ValueError("""One and only one argument must be 0"""							)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""							)
      if voltage == 0:
            return {"voltage": float(current * resistance							)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



