



'''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        UpperCAmelCase_			(			nn.Module      ):
    """simple docstring"""







    def __init__(       self   :	Optional[Any]		):
          super().__init__()
          snake_case__    :       List[str]       		= nn.Linear(3					,	4		)
          snake_case__    :       Optional[Any]       		= nn.BatchNormad(4		)
          snake_case__    :       Union[str, Any]       		= nn.Linear(4					,	5		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Optional[int]		):
          return self.lineara(self.batchnorm(self.lineara(snake_case_		)		)		)







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       List[Any]       		= ModelForTest()
          with TemporaryDirectory() as tmp_dir:
                offload_state_dict(snake_case_					,	model.state_dict()		)
                snake_case__    :       Tuple       		= os.path.join(snake_case_					,	"""index.json"""		)
                self.assertTrue(os.path.isfile(snake_case_		)		)
                # TODO: add tests on what is inside the index

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




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= [torch.floataa, torch.floataa, torch.bfloataa]

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

                      snake_case__    :       Any       		= load_offloaded_weight(snake_case_					,	index["""weight"""]		)
                      self.assertTrue(torch.equal(snake_case_					,	snake_case_		)		)




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Union[str, Any]       		= ModelForTest()
          snake_case__    :       Optional[Any]       		= model.state_dict()
          snake_case__    :       int       		= {k: v for k, v in state_dict.items() if """linear2""" not in k}
          snake_case__    :       Dict       		= {k: v for k, v in state_dict.items() if """linear2""" in k}

          with TemporaryDirectory() as tmp_dir:
                offload_state_dict(snake_case_					,	snake_case_		)
                snake_case__    :       Optional[int]       		= OffloadedWeightsLoader(state_dict=snake_case_					,	save_folder=snake_case_		)

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

          snake_case__    :       Any       		= {k: v for k, v in state_dict.items() if """weight""" in k}
          snake_case__    :       List[str]       		= {k: v for k, v in state_dict.items() if """weight""" not in k}

          with TemporaryDirectory() as tmp_dir:
                offload_state_dict(snake_case_					,	snake_case_		)
                snake_case__    :       Dict       		= OffloadedWeightsLoader(state_dict=snake_case_					,	save_folder=snake_case_		)

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

          with TemporaryDirectory() as tmp_dir:
                offload_state_dict(snake_case_					,	snake_case_		)
                # Duplicates are removed
                snake_case__    :       Dict       		= OffloadedWeightsLoader(state_dict=snake_case_					,	save_folder=snake_case_		)

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




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Tuple       		= {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
          snake_case__    :       Optional[Any]       		= extract_submodules_state_dict(snake_case_					,	["""a.1""", """a.2"""]		)
          self.assertDictEqual(snake_case_					,	{"""a.1""": 0, """a.2""": 2}		)

          snake_case__    :       List[str]       		= {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
          snake_case__    :       List[str]       		= extract_submodules_state_dict(snake_case_					,	["""a.1""", """a.2"""]		)
          self.assertDictEqual(snake_case_					,	{"""a.1.a""": 0, """a.2.a""": 2}		)





'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
