






"""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         =							logging.get_logger(__name__)



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				str    =				["""pixel_values"""]





	def __init__(self     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = PILImageResampling.BILINEAR     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = 1 / 255     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_							)
		A_		:						Any	    =   size if size is not None else {"""shortest_edge""": 256}
		A_		:						int	    =   get_size_dict(lowerCAmelCase_     ,     default_to_square=lowerCAmelCase_							)
		A_		:						Optional[int]	    =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		A_		:						Optional[Any]	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						Dict	    =   do_resize
		A_		:						str	    =   size
		A_		:						Tuple	    =   resample
		A_		:						int	    =   do_center_crop
		A_		:						Union[str, Any]	    =   crop_size
		A_		:						int	    =   do_rescale
		A_		:						Optional[Any]	    =   rescale_factor
		A_		:						Optional[int]	    =   do_normalize
		A_		:						Tuple	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		A_		:						Tuple	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = PILImageResampling.BICUBIC     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Optional[int]	    =   get_size_dict(lowerCAmelCase_     ,     default_to_square=lowerCAmelCase_							)
		if "shortest_edge" not in size:
			raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		A_		:						Union[str, Any]	    =   get_resize_output_image_size(lowerCAmelCase_     ,     size=size["""shortest_edge"""]     ,     default_to_square=lowerCAmelCase_							)
		return resize(lowerCAmelCase_     ,     size=lowerCAmelCase_     ,     resample=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Tuple	    =   get_size_dict(lowerCAmelCase_							)
		return center_crop(lowerCAmelCase_     ,     size=(size["""height"""], size["""width"""])     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_							):
		return rescale(lowerCAmelCase_     ,     scale=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		return normalize(lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = ChannelDimension.FIRST     ,     **lowerCAmelCase_     ,     ):
		A_		:						Dict	    =   do_resize if do_resize is not None else self.do_resize
		A_		:						Tuple	    =   size if size is not None else self.size
		A_		:						Dict	    =   get_size_dict(lowerCAmelCase_     ,     default_to_square=lowerCAmelCase_							)
		A_		:						Optional[int]	    =   resample if resample is not None else self.resample
		A_		:						Dict	    =   do_center_crop if do_center_crop is not None else self.do_center_crop
		A_		:						str	    =   crop_size if crop_size is not None else self.crop_size
		A_		:						Optional[Any]	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						Dict	    =   do_rescale if do_rescale is not None else self.do_rescale
		A_		:						Union[str, Any]	    =   rescale_factor if rescale_factor is not None else self.rescale_factor
		A_		:						Tuple	    =   do_normalize if do_normalize is not None else self.do_normalize
		A_		:						str	    =   image_mean if image_mean is not None else self.image_mean
		A_		:						List[str]	    =   image_std if image_std is not None else self.image_std

		A_		:						List[str]	    =   make_list_of_images(lowerCAmelCase_							)

		if not valid_images(lowerCAmelCase_							):
			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.
		A_		:						str	    =   [to_numpy_array(lowerCAmelCase_							) for image in images]

		if do_resize:
			A_		:						Dict	    =   [self.resize(image=lowerCAmelCase_     ,     size=lowerCAmelCase_     ,     resample=lowerCAmelCase_							) for image in images]

		if do_center_crop:
			A_		:						int	    =   [self.center_crop(image=lowerCAmelCase_     ,     size=lowerCAmelCase_							) for image in images]

		if do_rescale:
			A_		:						Optional[int]	    =   [self.rescale(image=lowerCAmelCase_     ,     scale=lowerCAmelCase_							) for image in images]

		if do_normalize:
			A_		:						List[Any]	    =   [self.normalize(image=lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_							) for image in images]

		A_		:						Union[str, Any]	    =   [to_channel_dimension_format(lowerCAmelCase_     ,     lowerCAmelCase_							) for image in images]

		A_		:						str	    =   {"""pixel_values""": images}
		return BatchFeature(data=lowerCAmelCase_     ,     tensor_type=lowerCAmelCase_							)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

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

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   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=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							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         =							parser.parse_args()

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

