



"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase    :int     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=['''pixel_values''']





     def __init__(					self       ,	A = True       ,	A = None       ,	A = PILImageResampling.BILINEAR       ,	A = True       ,	A = None       ,	A = True       ,	A = 1 / 2_5_5       ,	A = True       ,	A = None       ,	A = None       ,	**A       ,	)							->					None:
         super().__init__(**A		)
         _UpperCAmelCase							:  str											=    size if size is not None else {'''shortest_edge''': 2_5_6}
         _UpperCAmelCase							:  Optional[Any]											=    get_size_dict(A       ,	default_to_square=A		)
         _UpperCAmelCase							:  Any											=    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         _UpperCAmelCase							:  List[str]											=    get_size_dict(A		)
         _UpperCAmelCase							:  Optional[int]											=    do_resize
         _UpperCAmelCase							:  Any											=    size
         _UpperCAmelCase							:  List[str]											=    resample
         _UpperCAmelCase							:  str											=    do_center_crop
         _UpperCAmelCase							:  int											=    crop_size
         _UpperCAmelCase							:  Union[str, Any]											=    do_rescale
         _UpperCAmelCase							:  Any											=    rescale_factor
         _UpperCAmelCase							:  Union[str, Any]											=    do_normalize
         _UpperCAmelCase							:  Union[str, Any]											=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _UpperCAmelCase							:  str											=    image_std if image_std is not None else IMAGENET_STANDARD_STD





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A = PILImageResampling.BICUBIC       ,	A = None       ,	**A       ,	)							->					np.ndarray:
         _UpperCAmelCase							:  Optional[int]											=    get_size_dict(A       ,	default_to_square=A		)
         if "shortest_edge" not in size:
             raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
         _UpperCAmelCase							:  Any											=    get_resize_output_image_size(A       ,	size=size['''shortest_edge''']       ,	default_to_square=A		)
         return resize(A       ,	size=A       ,	resample=A       ,	data_format=A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A = None       ,	**A       ,	)							->					np.ndarray:
         _UpperCAmelCase							:  Any											=    get_size_dict(A		)
         return center_crop(A       ,	size=(size['''height'''], size['''width'''])       ,	data_format=A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A = None       ,	**A		)							->					np.ndarray:
         return rescale(A       ,	scale=A       ,	data_format=A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A = None       ,	**A       ,	)							->					np.ndarray:
         return normalize(A       ,	mean=A       ,	std=A       ,	data_format=A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = ChannelDimension.FIRST       ,	**A       ,	)							->					Optional[Any]:
         _UpperCAmelCase							:  Tuple											=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							:  str											=    size if size is not None else self.size
         _UpperCAmelCase							:  List[str]											=    get_size_dict(A       ,	default_to_square=A		)
         _UpperCAmelCase							:  Optional[Any]											=    resample if resample is not None else self.resample
         _UpperCAmelCase							:  str											=    do_center_crop if do_center_crop is not None else self.do_center_crop
         _UpperCAmelCase							:  Tuple											=    crop_size if crop_size is not None else self.crop_size
         _UpperCAmelCase							:  List[str]											=    get_size_dict(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							:  Tuple											=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							:  Union[str, Any]											=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							:  List[Any]											=    image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							:  Union[str, Any]											=    image_std if image_std is not None else self.image_std

         _UpperCAmelCase							:  str											=    make_list_of_images(A		)

         if not valid_images(A		):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

         if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''		)

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

         # All transformations expect numpy arrays.
         _UpperCAmelCase							:  str											=    [to_numpy_array(A		) for image in images]

         if do_resize:
             _UpperCAmelCase							:  Union[str, Any]											=    [self.resize(image=A       ,	size=A       ,	resample=A		) for image in images]

         if do_center_crop:
             _UpperCAmelCase							:  Optional[int]											=    [self.center_crop(image=A       ,	size=A		) for image in images]

         if do_rescale:
             _UpperCAmelCase							:  int											=    [self.rescale(image=A       ,	scale=A		) for image in images]

         if do_normalize:
             _UpperCAmelCase							:  Union[str, Any]											=    [self.normalize(image=A       ,	mean=A       ,	std=A		) for image in images]

         _UpperCAmelCase							:  List[str]											=    [to_channel_dimension_format(A       ,	A		) for image in images]

         _UpperCAmelCase							:  Tuple											=    {'''pixel_values''': images}
         return BatchFeature(data=A       ,	tensor_type=A		)







"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    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							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					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)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

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


