
"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			:       Union[str, Any]   		=       logging.get_logger(__name__)


if is_vision_available():
   import PIL

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BICUBIC	,    _a = True	,    _a = None	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = None	,    _a = True	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				int       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				Optional[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				Optional[int]       		=							crop_size if crop_size is not None else {"height": 224, "width": 224}
          lowercase    :				int       		=							get_size_dict(_a	,    default_to_square=_a	,    param_name="crop_size"				)

          lowercase    :				Optional[int]       		=							do_resize
          lowercase    :				str       		=							size
          lowercase    :				Any       		=							resample
          lowercase    :				Tuple       		=							do_center_crop
          lowercase    :				Dict       		=							crop_size
          lowercase    :				Dict       		=							do_rescale
          lowercase    :				List[Any]       		=							rescale_factor
          lowercase    :				str       		=							do_normalize
          lowercase    :				Dict       		=							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowercase    :				Optional[Any]       		=							image_std if image_std is not None else OPENAI_CLIP_STD
          lowercase    :				List[Any]       		=							do_convert_rgb
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PILImageResampling.BICUBIC	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							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()}"""				)
          lowercase    :				Union[str, 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 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							get_size_dict(_a				)
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
          return center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return normalize(_a	,    mean=_a	,    std=_a	,    data_format=_a	,    **_a				)

   def 			__magic_name__			(			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 = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[int]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Optional[int]       		=							size if size is not None else self.size
          lowercase    :				int       		=							get_size_dict(_a	,    param_name="size"	,    default_to_square=_a				)
          lowercase    :				str       		=							resample if resample is not None else self.resample
          lowercase    :				str       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				List[Any]       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Optional[int]       		=							get_size_dict(_a	,    param_name="crop_size"	,    default_to_square=_a				)
          lowercase    :				Union[str, Any]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				Optional[int]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Any       		=							do_normalize if do_normalize is not None else self.do_normalize
          lowercase    :				str       		=							image_mean if image_mean is not None else self.image_mean
          lowercase    :				Optional[Any]       		=							image_std if image_std is not None else self.image_std
          lowercase    :				Tuple       		=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowercase    :				Optional[Any]       		=							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."				)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
                 lowercase    :				Any       		=							[convert_to_rgb(_a				) for image in images]

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

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

          if do_center_crop:
                 lowercase    :				List[Any]       		=							[self.center_crop(image=_a	,    size=_a				) for image in images]

          if do_rescale:
                 lowercase    :				Dict       		=							[self.rescale(image=_a	,    scale=_a				) for image in images]

          if do_normalize:
                 lowercase    :				int       		=							[self.normalize(image=_a	,    mean=_a	,    std=_a				) for image in images]

          lowercase    :				str       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				List[Any]       		=							{"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   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

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

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							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     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[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)
   _A			:       Dict   		=       parser.parse_args()

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


