





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 				A_				(      nn.Module	):







       """simple docstring"""





       def __init__(    self	:	Optional[Any]  )						->					List[Any]:
         super().__init__()
         _lowercase							= nn.Linear(3						,4  )
         _lowercase							= nn.BatchNormad(4  )
         _lowercase							= nn.Linear(4						,5  )







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





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	List[str]  )						->					Dict:
         _lowercase							= ModelForTest()
         with TemporaryDirectory() as tmp_dir:
           offload_state_dict(__A						,model.state_dict()  )
           _lowercase							= 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"]:
             _lowercase							= 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	:	int  )						->					List[Any]:
         _lowercase							= [torch.floataa, torch.floataa, torch.bfloataa]

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

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







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

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

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

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

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






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )