





'''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 							UpperCAmelCase_  (						__lowerCamelCase : Any     ,__lowerCamelCase : Optional[int]			):
   lowercase_      :Optional[int]         =			checkpoint

   lowercase_      :Dict         =			{}

   lowercase_      :int         =			vae_state_dict["encoder.conv_in.weight"]
   lowercase_      :Optional[int]         =			vae_state_dict["encoder.conv_in.bias"]
   lowercase_      :Optional[Any]         =			vae_state_dict["encoder.conv_out.weight"]
   lowercase_      :List[str]         =			vae_state_dict["encoder.conv_out.bias"]
   lowercase_      :Dict         =			vae_state_dict["encoder.norm_out.weight"]
   lowercase_      :Tuple         =			vae_state_dict["encoder.norm_out.bias"]

   lowercase_      :str         =			vae_state_dict["decoder.conv_in.weight"]
   lowercase_      :List[Any]         =			vae_state_dict["decoder.conv_in.bias"]
   lowercase_      :List[str]         =			vae_state_dict["decoder.conv_out.weight"]
   lowercase_      :Dict         =			vae_state_dict["decoder.conv_out.bias"]
   lowercase_      :Tuple         =			vae_state_dict["decoder.norm_out.weight"]
   lowercase_      :Optional[Any]         =			vae_state_dict["decoder.norm_out.bias"]

   lowercase_      :str         =			vae_state_dict["quant_conv.weight"]
   lowercase_      :List[str]         =			vae_state_dict["quant_conv.bias"]
   lowercase_      :Dict         =			vae_state_dict["post_quant_conv.weight"]
   lowercase_      :List[str]         =			vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   lowercase_      :List[Any]         =			len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "encoder.down" in layer}			)
   lowercase_      :str         =			{
       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
   lowercase_      :str         =			len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "decoder.up" in layer}			)
   lowercase_      :Any         =			{
       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			):
      lowercase_      :Union[str, 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:
         lowercase_      :str         =			vae_state_dict.pop(
             F'encoder.down.{i}.downsample.conv.weight'			)
         lowercase_      :int         =			vae_state_dict.pop(
             F'encoder.down.{i}.downsample.conv.bias'			)

      lowercase_      :Tuple         =			renew_vae_resnet_paths(__lowerCamelCase			)
      lowercase_      :Union[str, Any]         =			{"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
      assign_to_checkpoint(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,additional_replacements=[meta_path]     ,config=__lowerCamelCase			)

   lowercase_      :Optional[int]         =			[key for key in vae_state_dict if "encoder.mid.block" in key]
   lowercase_      :Any         =			2
   for i in range(1     ,num_mid_res_blocks + 1			):
      lowercase_      :str         =			[key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

      lowercase_      :List[Any]         =			renew_vae_resnet_paths(__lowerCamelCase			)
      lowercase_      :Dict         =			{"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,additional_replacements=[meta_path]     ,config=__lowerCamelCase			)

   lowercase_      :List[Any]         =			[key for key in vae_state_dict if "encoder.mid.attn" in key]
   lowercase_      :Dict         =			renew_vae_attention_paths(__lowerCamelCase			)
   lowercase_      :List[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			)

   for i in range(__lowerCamelCase			):
      lowercase_      :Optional[Any]         =			num_up_blocks - 1 - i
      lowercase_      :str         =			[
          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:
         lowercase_      :Optional[Any]         =			vae_state_dict[
             F'decoder.up.{block_id}.upsample.conv.weight'
         ]
         lowercase_      :int         =			vae_state_dict[
             F'decoder.up.{block_id}.upsample.conv.bias'
         ]

      lowercase_      :List[Any]         =			renew_vae_resnet_paths(__lowerCamelCase			)
      lowercase_      :List[Any]         =			{"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
      assign_to_checkpoint(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,additional_replacements=[meta_path]     ,config=__lowerCamelCase			)

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

      lowercase_      :Tuple         =			renew_vae_resnet_paths(__lowerCamelCase			)
      lowercase_      :Optional[Any]         =			{"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,additional_replacements=[meta_path]     ,config=__lowerCamelCase			)

   lowercase_      :int         =			[key for key in vae_state_dict if "decoder.mid.attn" in key]
   lowercase_      :Optional[Any]         =			renew_vae_attention_paths(__lowerCamelCase			)
   lowercase_      :List[str]         =			{"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 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : str     ,):
   # Only support V1
   lowercase_      :str         =			requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"			)
   lowercase_      :Optional[int]         =			io.BytesIO(r.content			)

   lowercase_      :Union[str, Any]         =			OmegaConf.load(__lowerCamelCase			)
   lowercase_      :Optional[Any]         =			5_12
   lowercase_      :List[str]         =			"cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"			):
      from safetensors import safe_open

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

   # Convert the VAE model.
   lowercase_      :List[str]         =			create_vae_diffusers_config(__lowerCamelCase     ,image_size=__lowerCamelCase			)
   lowercase_      :int         =			custom_convert_ldm_vae_checkpoint(__lowerCamelCase     ,__lowerCamelCase			)

   lowercase_      :Tuple         =			AutoencoderKL(**__lowerCamelCase			)
   vae.load_state_dict(__lowerCamelCase			)
   vae.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
 lowerCAmelCase :					Optional[Any] 							=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.''')

 lowerCAmelCase :					Dict 							=parser.parse_args()

 vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


