





"""simple docstring"""





import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch




class 							snake_case							(			TensorFormatter[Mapping, 'torch.Tensor', Mapping] ):

      '''simple docstring'''



      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Tuple=None						,				**__lowercase       :      List[Any] ):


        '''simple docstring'''




        super().__init__(features=__lowercase )
        __UpperCAmelCase    :						Any       =						torch_tensor_kwargs
        import torch  # noqa import torch at initialization

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Tuple ):


        '''simple docstring'''




        import torch

        if isinstance(__lowercase						,				__lowercase ) and column:
          if all(
              isinstance(__lowercase						,				torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
              for x in column ):
            return torch.stack(__lowercase )
        return column

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        import torch

        if isinstance(__lowercase						,				(str, bytes, type(__lowercase )) ):
          return value
        elif isinstance(__lowercase						,				(np.character, np.ndarray) ) and np.issubdtype(value.dtype						,				np.character ):
          return value.tolist()

        __UpperCAmelCase    :						Union[str, Any]       =						{}

        if isinstance(__lowercase						,				(np.number, np.ndarray) ) and np.issubdtype(value.dtype						,				np.integer ):
          __UpperCAmelCase    :						Any       =						{'''dtype''': torch.intaa}
        elif isinstance(__lowercase						,				(np.number, np.ndarray) ) and np.issubdtype(value.dtype						,				np.floating ):
          __UpperCAmelCase    :						int       =						{'''dtype''': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(__lowercase						,				PIL.Image.Image ):
            __UpperCAmelCase    :						List[Any]       =						np.asarray(__lowercase )
        return torch.tensor(__lowercase						,				**{**default_dtype, **self.torch_tensor_kwargs} )

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      List[Any] ):


        '''simple docstring'''




        import torch

        # support for torch, tf, jax etc.
        if hasattr(__lowercase						,				'''__array__''' ) and not isinstance(__lowercase						,				torch.Tensor ):
          __UpperCAmelCase    :						List[str]       =						data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__lowercase						,				np.ndarray ):
          if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
            return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        elif isinstance(__lowercase						,				(list, tuple) ):
          return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        return self._tensorize(__lowercase )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      dict ):


        '''simple docstring'''




        return map_nested(self._recursive_tensorize						,				__lowercase						,				map_list=__lowercase )

      def       A_					(							self       :      Dict						,				__lowercase       :      pa.Table ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						self.numpy_arrow_extractor().extract_row(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.python_features_decoder.decode_row(__lowercase )
        return self.recursive_tensorize(__lowercase )

      def       A_					(							self       :      List[str]						,				__lowercase       :      pa.Table ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						self.numpy_arrow_extractor().extract_column(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						self.python_features_decoder.decode_column(__lowercase						,				pa_table.column_names[0] )
        __UpperCAmelCase    :						int       =						self.recursive_tensorize(__lowercase )
        __UpperCAmelCase    :						int       =						self._consolidate(__lowercase )
        return column







      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      pa.Table ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.numpy_arrow_extractor().extract_batch(__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						self.python_features_decoder.decode_batch(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.recursive_tensorize(__lowercase )
        for column_name in batch:
          __UpperCAmelCase    :						List[Any]       =						self._consolidate(batch[column_name] )
        return batch






"""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)