


"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				['pixel_values']

		def __init__(  self							, _a = True							, _a = None							, _a = PILImageResampling.BICUBIC							, _a = True							, _a = True							, _a = 1 / 255							, _a = None							, _a = True							, _a = None							, _a = None							, **_a							, ):
				super().__init__(**_a      )
				__a           =			size if size is not None else {'''height''': 224, '''width''': 224}
				__a           =			get_size_dict(_a      )
				__a           =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				__a           =			get_size_dict(_a							, default_to_square=_a							, param_name='''crop_size'''      )

				__a           =			do_resize
				__a           =			do_rescale
				__a           =			do_normalize
				__a           =			do_center_crop
				__a           =			crop_size
				__a           =			size
				__a           =			resample
				__a           =			rescale_factor
				__a           =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__a           =			image_std if image_std is not None else IMAGENET_DEFAULT_STD

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = PILImageResampling.BILINEAR							, _a = None							, **_a							, ):
				__a           =			get_size_dict(_a      )

				if "shortest_edge" in size:
						__a           =			get_resize_output_image_size(_a							, size=size['''shortest_edge''']							, default_to_square=_a      )
						# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
						__a           =			(size['''height'''], size['''width'''])
				else:
						raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''      )
				return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a							, ):
				__a           =			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 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a      ):
				return rescale(_a							, scale=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, **_a							, ):
				return normalize(_a							, mean=_a							, std=_a							, data_format=_a							, **_a      )





		def 			__UpperCAmelCase       (  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							, ):
				__a           =			do_resize if do_resize is not None else self.do_resize
				__a           =			do_rescale if do_rescale is not None else self.do_rescale
				__a           =			do_normalize if do_normalize is not None else self.do_normalize
				__a           =			do_center_crop if do_center_crop is not None else self.do_center_crop
				__a           =			crop_size if crop_size is not None else self.crop_size
				__a           =			get_size_dict(_a							, param_name='''crop_size'''							, default_to_square=_a      )
				__a           =			resample if resample is not None else self.resample
				__a           =			rescale_factor if rescale_factor is not None else self.rescale_factor
				__a           =			image_mean if image_mean is not None else self.image_mean
				__a           =			image_std if image_std is not None else self.image_std

				__a           =			size if size is not None else self.size
				__a           =			get_size_dict(_a      )

				if not is_batched(_a      ):
						__a           =			[images]

				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.'''      )

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

				if do_resize:
						__a           =			[self.resize(image=_a							, size=_a							, resample=_a      ) for image in images]

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

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

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

				__a           =			[to_channel_dimension_format(_a							, _a      ) for image in images]

				__a           =			{'''pixel_values''': images}
				return BatchFeature(data=_a							, tensor_type=_a      )








"""simple docstring"""





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    checkpoint

   UpperCAmelCase_					:					int			     =    {}

   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_in.weight"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["encoder.conv_in.bias"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["encoder.conv_out.weight"]
   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["encoder.norm_out.weight"]
   UpperCAmelCase_					:					Union[str, Any]			     =    vae_state_dict["encoder.norm_out.bias"]

   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_in.weight"]
   UpperCAmelCase_					:					int			     =    vae_state_dict["decoder.conv_in.bias"]
   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["decoder.norm_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.norm_out.bias"]

   UpperCAmelCase_					:					str			     =    vae_state_dict["quant_conv.weight"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["quant_conv.bias"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["post_quant_conv.weight"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_					:					Optional[Any]			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "encoder.down" in layer}  )
   UpperCAmelCase_					:					Optional[Any]			     =    {
       layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   # Retrieves the keys for the decoder up blocks only
   UpperCAmelCase_					:					Dict			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "decoder.up" in layer}  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Any			     =    [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

      if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

      UpperCAmelCase_					:					List[str]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[Any]			     =    {"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					List[Any]			     =    [key for key in vae_state_dict if "encoder.mid.block" in key]
   UpperCAmelCase_					:					Tuple			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					List[Any]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[int]			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "encoder.mid.attn" in key]
   UpperCAmelCase_					:					Union[str, Any]			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Optional[Any]			     =    num_up_blocks - 1 - i
      UpperCAmelCase_					:					Any			     =    [
          key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
      ]

      if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					str			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

      UpperCAmelCase_					:					Dict			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					List[str]			     =    {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[Any]			     =    [key for key in vae_state_dict if "decoder.mid.block" in key]
   UpperCAmelCase_					:					List[Any]			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					Tuple			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "decoder.mid.attn" in key]
   UpperCAmelCase_					:					Any			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )
   return new_checkpoint







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		):
   # Only support V1
   UpperCAmelCase_					:					List[str]			     =    requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"  )
   UpperCAmelCase_					:					List[Any]			     =    io.BytesIO(r.content  )

   UpperCAmelCase_					:					Any			     =    OmegaConf.load(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    512
   UpperCAmelCase_					:					Optional[Any]			     =    "cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

      UpperCAmelCase_					:					int			     =    {}
      with safe_open(__lowerCamelCase,		framework="pt",		device="cpu"  ) as f:
         for key in f.keys():
            UpperCAmelCase_					:					Tuple			     =    f.get_tensor(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Any			     =    torch.load(__lowerCamelCase,		map_location=__lowerCamelCase  )["state_dict"]

   # Convert the VAE model.
   UpperCAmelCase_					:					Dict			     =    create_vae_diffusers_config(__lowerCamelCase,		image_size=__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    custom_convert_ldm_vae_checkpoint(__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    AutoencoderKL(**__lowerCamelCase  )
   vae.load_state_dict(__lowerCamelCase  )
   vae.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()

    parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

    _a		       =							parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


