
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       ):


       def __init__(    self				)						->    Union[str, Any]:
              super().__init__()
              SCREAMING_SNAKE_CASE_							:				int		=nn.Linear(3						,       4				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=nn.BatchNormad(4				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=nn.Linear(4						,       5				)





       def 	_snake_case				(    self						,       __A				)						->    Union[str, Any]:
              return self.lineara(self.batchnorm(self.lineara(__A				)				)				)






class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=ModelForTest()
              with TemporaryDirectory() as tmp_dir:
                     offload_state_dict(__A						,       model.state_dict()				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=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"]:
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=os.path.join(__A						,       F'{key}.dat'				)
                            self.assertTrue(os.path.isfile(__A				)				)
                            # TODO: add tests on the fact weights are properly loaded


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[torch.floataa, torch.floataa, torch.bfloataa]

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

                            SCREAMING_SNAKE_CASE_							:				Any		=load_offloaded_weight(__A						,       index['''weight''']				)
                            self.assertTrue(torch.equal(__A						,       __A				)				)


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=ModelForTest()
              SCREAMING_SNAKE_CASE_							:				List[str]		=model.state_dict()
              SCREAMING_SNAKE_CASE_							:				str		={k: v for k, v in state_dict.items() if 'linear2' not in k}
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={k: v for k, v in state_dict.items() if 'linear2' in k}

              with TemporaryDirectory() as tmp_dir:
                     offload_state_dict(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Any		=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]				)				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={k: v for k, v in state_dict.items() if 'weight' in k}
              SCREAMING_SNAKE_CASE_							:				int		={k: v for k, v in state_dict.items() if 'weight' not in k}

              with TemporaryDirectory() as tmp_dir:
                     offload_state_dict(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=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
                     SCREAMING_SNAKE_CASE_							:				List[str]		=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 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				str		={'a.1': 0, 'a.10': 1, 'a.2': 2}
              SCREAMING_SNAKE_CASE_							:				Dict		=extract_submodules_state_dict(__A						,       ['''a.1''', '''a.2''']				)
              self.assertDictEqual(__A						,       {'''a.1''': 0, '''a.2''': 2}				)

              SCREAMING_SNAKE_CASE_							:				Tuple		={'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
              SCREAMING_SNAKE_CASE_							:				str		=extract_submodules_state_dict(__A						,       ['''a.1''', '''a.2''']				)
              self.assertDictEqual(__A						,       {'''a.1.a''': 0, '''a.2.a''': 2}				)






'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
            raise TypeError('Input value must be an \'int\' type'    )

      SCREAMING_SNAKE_CASE   :      int    					=						0
      while number:
            position += 1
            number >>= 1

      return position


if __name__ == "__main__":
      import doctest

      doctest.testmod()


