





"""simple docstring"""





from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							snake_case							(			__lowerCAmelCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						{'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
        return Dataset.from_dict(lowerCamelCase__ )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self._create_example_records()
        __UpperCAmelCase    :						List[str]       =						Dataset.from_list(lowerCamelCase__ )
        self.assertListEqual(dset.column_names						,				['''col_1''', '''col_2'''] )
        for i, r in enumerate(lowerCamelCase__ ):
          self.assertDictEqual(lowerCamelCase__						,				example_records[i] )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self._create_example_records()
        __UpperCAmelCase    :						Dict       =						Dataset.from_list(lowerCamelCase__ )
        __UpperCAmelCase    :						Optional[int]       =						Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
        self.assertEqual(dset.info						,				dset_from_dict.info )

      def       A_					(							self       :      Union[str, Any] ):  # checks what happens with missing columns


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						[{'''col_1''': 1}, {'''col_2''': '''x'''}]
        __UpperCAmelCase    :						str       =						Dataset.from_list(lowerCamelCase__ )
        self.assertDictEqual(dset[0]						,				{'''col_1''': 1} )
        self.assertDictEqual(dset[1]						,				{'''col_1''': None} )  # NB: first record is used for columns

      def       A_					(							self       :      Any ):  # checks if the type can be inferred from the second record


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						[{'''col_1''': []}, {'''col_1''': [1, 2]}]
        __UpperCAmelCase    :						str       =						Dataset.from_list(lowerCamelCase__ )
        self.assertEqual(dset.info.features['''col_1''']						,				Sequence(Value('''int64''' ) ) )







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						Dataset.from_list([] )
        self.assertEqual(len(lowerCamelCase__ )						,				0 )
        self.assertListEqual(dset.column_names						,				[] )






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, 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=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       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)
  lowercase__				:Dict				   =       parser.parse_args()

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