






'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

UpperCamelCase    :      Any  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						if isinstance(__lowerCAmelCase   ,    (list, tuple)						) and isinstance(videos[0]   ,    (list, tuple)						) and is_valid_image(videos[0][0]						):
												return videos

						elif isinstance(__lowerCAmelCase   ,    (list, tuple)						) and is_valid_image(videos[0]						):
												return [videos]

						elif is_valid_image(__lowerCAmelCase						):
												return [[videos]]

						raise ValueError(F'''Could not make batched video from {videos}'''						)





class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					['pixel_values']






		def __init__(      self   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,_lowerCAmelCase = PILImageResampling.BILINEAR   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,_lowerCAmelCase = True   ,_lowerCAmelCase = 1 / 2_55   ,_lowerCAmelCase = True   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								super().__init__(**_lowerCAmelCase       )
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 2_56}
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase   ,default_to_square=_lowerCAmelCase       )
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase   ,param_name="""crop_size"""       )

								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  resample
								lowerCamelCase__         =  do_rescale
								lowerCamelCase__         =  rescale_factor
								lowerCamelCase__         =  offset
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCamelCase__         =  image_std if image_std is not None else IMAGENET_STANDARD_STD






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = PILImageResampling.BILINEAR   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase   ,default_to_square=_lowerCAmelCase       )
								if "shortest_edge" in size:
														lowerCamelCase__         =  get_resize_output_image_size(_lowerCAmelCase   ,size["""shortest_edge"""]   ,default_to_square=_lowerCAmelCase       )
								elif "height" in size and "width" in size:
														lowerCamelCase__         =  (size["""height"""], size["""width"""])
								else:
														raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''       )
								return resize(_lowerCAmelCase   ,size=_lowerCAmelCase   ,resample=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
								if "height" not in size or "width" not in size:
														raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''       )
								return center_crop(_lowerCAmelCase   ,size=(size["""height"""], size["""width"""])   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  image.astype(np.floataa       )
								if offset:
														lowerCamelCase__         =  image - (scale / 2)
								return rescale(_lowerCAmelCase   ,scale=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								return normalize(_lowerCAmelCase   ,mean=_lowerCAmelCase   ,std=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      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   ,):
								if do_resize and size is None or resample is None:
														raise ValueError("""Size and resample 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."""       )

								if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""       )

								# All transformations expect numpy arrays.
								lowerCamelCase__         =  to_numpy_array(_lowerCAmelCase       )

								if do_resize:
														lowerCamelCase__         =  self.resize(image=_lowerCAmelCase   ,size=_lowerCAmelCase   ,resample=_lowerCAmelCase       )

								if do_center_crop:
														lowerCamelCase__         =  self.center_crop(_lowerCAmelCase   ,size=_lowerCAmelCase       )

								if do_rescale:
														lowerCamelCase__         =  self.rescale(image=_lowerCAmelCase   ,scale=_lowerCAmelCase   ,offset=_lowerCAmelCase       )

								if do_normalize:
														lowerCamelCase__         =  self.normalize(image=_lowerCAmelCase   ,mean=_lowerCAmelCase   ,std=_lowerCAmelCase       )

								lowerCamelCase__         =  to_channel_dimension_format(_lowerCAmelCase   ,_lowerCAmelCase       )
								return image






		def 			UpperCamelCase_					(      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 = None   ,_lowerCAmelCase = ChannelDimension.FIRST   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__         =  resample if resample is not None else self.resample
								lowerCamelCase__         =  do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase__         =  do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__         =  rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__         =  offset if offset is not None else self.offset
								lowerCamelCase__         =  do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__         =  image_mean if image_mean is not None else self.image_mean
								lowerCamelCase__         =  image_std if image_std is not None else self.image_std

								lowerCamelCase__         =  size if size is not None else self.size
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase   ,default_to_square=_lowerCAmelCase       )
								lowerCamelCase__         =  crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase   ,param_name="""crop_size"""       )

								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."""       )

								lowerCamelCase__         =  make_batched(_lowerCAmelCase       )

								lowerCamelCase__         =  [
								    [
								        self._preprocess_image(
								            image=_lowerCAmelCase   ,do_resize=_lowerCAmelCase   ,size=_lowerCAmelCase   ,resample=_lowerCAmelCase   ,do_center_crop=_lowerCAmelCase   ,crop_size=_lowerCAmelCase   ,do_rescale=_lowerCAmelCase   ,rescale_factor=_lowerCAmelCase   ,offset=_lowerCAmelCase   ,do_normalize=_lowerCAmelCase   ,image_mean=_lowerCAmelCase   ,image_std=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,)
								        for img in video
								    ]
								    for video in videos
								]

								lowerCamelCase__         =  {"""pixel_values""": videos}
								return BatchFeature(data=_lowerCAmelCase   ,tensor_type=_lowerCAmelCase       )









'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

						# extract state_dict for VQVAE
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

    # extract state_dict for UNetLDM
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  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   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		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)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

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

