"""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 					A							(    nn.Module					):


   def __init__(self					:						Optional[Any]		)						->    List[Any]:
         """simple docstring"""



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





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



         return self.lineara(self.batchnorm(self.lineara(__UpperCAmelCase		)		)		)





class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						Optional[int]		)						->    Optional[int]:
         """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       lowercase_				(self					:						Any		)						->    Union[str, Any]:
         """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       lowercase_				(self					:						Tuple		)						->    Optional[Any]:
         """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       lowercase_				(self					:						Optional[int]		)						->    Union[str, Any]:
         """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}		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



