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 __snake_case    (						nn.Module		):






   """simple docstring"""




   def __init__(   self      :    List[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    int	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     return self.lineara(self.batchnorm(self.lineara(_lowercase				)				)				)







class __snake_case    (						unittest.TestCase		):






   """simple docstring"""




   def      __a						(   self      :    int				):



     """simple docstring"""






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

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




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






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

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

         SCREAMING_SNAKE_CASE__      							=				load_offloaded_weight(_lowercase	,     index["""weight"""]				)
         self.assertTrue(torch.equal(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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

     with TemporaryDirectory() as tmp_dir:
       offload_state_dict(_lowercase	,     _lowercase				)
       SCREAMING_SNAKE_CASE__      							=				OffloadedWeightsLoader(state_dict=_lowercase	,     save_folder=_lowercase				)

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

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

     with TemporaryDirectory() as tmp_dir:
       offload_state_dict(_lowercase	,     _lowercase				)
       SCREAMING_SNAKE_CASE__      							=				OffloadedWeightsLoader(state_dict=_lowercase	,     save_folder=_lowercase				)

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

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

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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

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

from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
