






"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_	:				Optional[Any]										= logging.get_logger(__name__)



def     __lowercase					(    a :				Tuple )					->					Optional[Any]:
      __snake_case      :    Any  			=MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            __snake_case      :    List[str]  			=[144, 192, 240]
            __snake_case      :    str  			=[16, 32, 64, 96, 128, 160, 640]
      elif "mobilevit_xs" in mobilevit_name:
            __snake_case      :    Optional[int]  			=[96, 120, 144]
            __snake_case      :    List[str]  			=[16, 32, 48, 64, 80, 96, 384]
      elif "mobilevit_xxs" in mobilevit_name:
            __snake_case      :    Optional[int]  			=[64, 80, 96]
            __snake_case      :    Dict  			=[16, 16, 24, 48, 64, 80, 320]
            __snake_case      :    str  			=0.0_5
            __snake_case      :    List[str]  			=2.0

      if mobilevit_name.startswith('''deeplabv3_''' ):
            __snake_case      :    Union[str, Any]  			=512
            __snake_case      :    Optional[int]  			=16
            __snake_case      :    Dict  			=21
            __snake_case      :    Union[str, Any]  			='''pascal-voc-id2label.json'''
      else:
            __snake_case      :    Optional[int]  			=1_000
            __snake_case      :    List[Any]  			='''imagenet-1k-id2label.json'''

      __snake_case      :    List[Any]  			='''huggingface/label-files'''
      __snake_case      :    Any  			=json.load(open(hf_hub_download(a       ,		a       ,		repo_type='''dataset''' )       ,		'''r''' ) )
      __snake_case      :    List[Any]  			={int(a ): v for k, v in idalabel.items()}
      __snake_case      :    str  			=idalabel
      __snake_case      :    List[Any]  			={v: k for k, v in idalabel.items()}

      return config



def     __lowercase					(    a :				List[str]       ,		a :				Union[str, Any]=False )					->					Optional[int]:
      for i in range(1       ,		6 ):
            if f'''layer_{i}.''' in name:
                  __snake_case      :    List[Any]  			=name.replace(f'''layer_{i}.'''       ,		f'''encoder.layer.{i - 1}.''' )

      if "conv_1." in name:
            __snake_case      :    Union[str, Any]  			=name.replace('''conv_1.'''       ,		'''conv_stem.''' )
      if ".block." in name:
            __snake_case      :    List[Any]  			=name.replace('''.block.'''       ,		'''.''' )

      if "exp_1x1" in name:
            __snake_case      :    Union[str, Any]  			=name.replace('''exp_1x1'''       ,		'''expand_1x1''' )
      if "red_1x1" in name:
            __snake_case      :    int  			=name.replace('''red_1x1'''       ,		'''reduce_1x1''' )
      if ".local_rep.conv_3x3." in name:
            __snake_case      :    Any  			=name.replace('''.local_rep.conv_3x3.'''       ,		'''.conv_kxk.''' )
      if ".local_rep.conv_1x1." in name:
            __snake_case      :    str  			=name.replace('''.local_rep.conv_1x1.'''       ,		'''.conv_1x1.''' )
      if ".norm." in name:
            __snake_case      :    Optional[int]  			=name.replace('''.norm.'''       ,		'''.normalization.''' )
      if ".conv." in name:
            __snake_case      :    Any  			=name.replace('''.conv.'''       ,		'''.convolution.''' )
      if ".conv_proj." in name:
            __snake_case      :    Optional[int]  			=name.replace('''.conv_proj.'''       ,		'''.conv_projection.''' )

      for i in range(0       ,		2 ):
            for j in range(0       ,		4 ):
                  if f'''.{i}.{j}.''' in name:
                        __snake_case      :    List[str]  			=name.replace(f'''.{i}.{j}.'''       ,		f'''.{i}.layer.{j}.''' )

      for i in range(2       ,		6 ):
            for j in range(0       ,		4 ):
                  if f'''.{i}.{j}.''' in name:
                        __snake_case      :    str  			=name.replace(f'''.{i}.{j}.'''       ,		f'''.{i}.''' )
                        if "expand_1x1" in name:
                              __snake_case      :    List[str]  			=name.replace('''expand_1x1'''       ,		'''downsampling_layer.expand_1x1''' )
                        if "conv_3x3" in name:
                              __snake_case      :    Optional[int]  			=name.replace('''conv_3x3'''       ,		'''downsampling_layer.conv_3x3''' )
                        if "reduce_1x1" in name:
                              __snake_case      :    Dict  			=name.replace('''reduce_1x1'''       ,		'''downsampling_layer.reduce_1x1''' )

      for i in range(2       ,		5 ):
            if f'''.global_rep.{i}.weight''' in name:
                  __snake_case      :    Dict  			=name.replace(f'''.global_rep.{i}.weight'''       ,		'''.layernorm.weight''' )
            if f'''.global_rep.{i}.bias''' in name:
                  __snake_case      :    Dict  			=name.replace(f'''.global_rep.{i}.bias'''       ,		'''.layernorm.bias''' )

      if ".global_rep." in name:
            __snake_case      :    Union[str, Any]  			=name.replace('''.global_rep.'''       ,		'''.transformer.''' )
      if ".pre_norm_mha.0." in name:
            __snake_case      :    Dict  			=name.replace('''.pre_norm_mha.0.'''       ,		'''.layernorm_before.''' )
      if ".pre_norm_mha.1.out_proj." in name:
            __snake_case      :    List[Any]  			=name.replace('''.pre_norm_mha.1.out_proj.'''       ,		'''.attention.output.dense.''' )
      if ".pre_norm_ffn.0." in name:
            __snake_case      :    Optional[Any]  			=name.replace('''.pre_norm_ffn.0.'''       ,		'''.layernorm_after.''' )
      if ".pre_norm_ffn.1." in name:
            __snake_case      :    List[Any]  			=name.replace('''.pre_norm_ffn.1.'''       ,		'''.intermediate.dense.''' )
      if ".pre_norm_ffn.4." in name:
            __snake_case      :    Dict  			=name.replace('''.pre_norm_ffn.4.'''       ,		'''.output.dense.''' )
      if ".transformer." in name:
            __snake_case      :    Any  			=name.replace('''.transformer.'''       ,		'''.transformer.layer.''' )

      if ".aspp_layer." in name:
            __snake_case      :    int  			=name.replace('''.aspp_layer.'''       ,		'''.''' )
      if ".aspp_pool." in name:
            __snake_case      :    Optional[int]  			=name.replace('''.aspp_pool.'''       ,		'''.''' )
      if "seg_head." in name:
            __snake_case      :    str  			=name.replace('''seg_head.'''       ,		'''segmentation_head.''' )
      if "segmentation_head.classifier.classifier." in name:
            __snake_case      :    List[str]  			=name.replace('''segmentation_head.classifier.classifier.'''       ,		'''segmentation_head.classifier.''' )

      if "classifier.fc." in name:
            __snake_case      :    int  			=name.replace('''classifier.fc.'''       ,		'''classifier.''' )
      elif (not base_model) and ("segmentation_head." not in name):
            __snake_case      :    List[str]  			='''mobilevit.''' + name

      return name



def     __lowercase					(    a :				Optional[Any]       ,		a :				List[str]       ,		a :				Dict=False )					->					str:
      if base_model:
            __snake_case      :    Dict  			=''''''
      else:
            __snake_case      :    List[Any]  			='''mobilevit.'''

      for key in orig_state_dict.copy().keys():
            __snake_case      :    Any  			=orig_state_dict.pop(a )

            if key[:8] == "encoder.":
                  __snake_case      :    str  			=key[8:]

            if "qkv" in key:
                  __snake_case      :    Tuple  			=key.split('''.''' )
                  __snake_case      :    Dict  			=int(key_split[0][6:] ) - 1
                  __snake_case      :    List[Any]  			=int(key_split[3] )
                  __snake_case      :    str  			=model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}''' )
                  __snake_case      :    Any  			=layer.transformer.layer[transformer_num].attention.attention.all_head_size
                  __snake_case      :    int  			=(
                      f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
                  )
                  if "weight" in key:
                        __snake_case      :    Optional[Any]  			=val[:dim, :]
                        __snake_case      :    Union[str, Any]  			=val[dim : dim * 2, :]
                        __snake_case      :    List[Any]  			=val[-dim:, :]
                  else:
                        __snake_case      :    int  			=val[:dim]
                        __snake_case      :    List[str]  			=val[dim : dim * 2]
                        __snake_case      :    Tuple  			=val[-dim:]
            else:
                  __snake_case      :    Dict  			=val

      return orig_state_dict



def     __lowercase					(    )					->					List[str]:
      __snake_case      :    Any  			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __snake_case      :    Dict  			=Image.open(requests.get(a       ,		stream=a ).raw )
      return im



@torch.no_grad()
def     __lowercase					(    a :				str       ,		a :				Dict       ,		a :				Optional[Any]       ,		a :				str=False )					->					int:
      __snake_case      :    Union[str, Any]  			=get_mobilevit_config(a )

      # load original state_dict
      __snake_case      :    int  			=torch.load(a       ,		map_location='''cpu''' )

      # load 🤗 model
      if mobilevit_name.startswith('''deeplabv3_''' ):
            __snake_case      :    str  			=MobileViTForSemanticSegmentation(a ).eval()
      else:
            __snake_case      :    Optional[Any]  			=MobileViTForImageClassification(a ).eval()

      __snake_case      :    Tuple  			=convert_state_dict(a       ,		a )
      model.load_state_dict(a )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      __snake_case      :    Optional[Any]  			=MobileViTImageProcessor(crop_size=config.image_size       ,		size=config.image_size + 32 )
      __snake_case      :    str  			=image_processor(images=prepare_img()       ,		return_tensors='''pt''' )
      __snake_case      :    List[Any]  			=model(**a )
      __snake_case      :    Optional[Any]  			=outputs.logits

      if mobilevit_name.startswith('''deeplabv3_''' ):
            assert logits.shape == (1, 21, 32, 32)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  __snake_case      :    Dict  			=torch.tensor(
                      [
                          [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                          [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                          [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                      ] )
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  __snake_case      :    Optional[int]  			=torch.tensor(
                      [
                          [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                          [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                          [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                      ] )
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  __snake_case      :    List[Any]  			=torch.tensor(
                      [
                          [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                          [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                          [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                      ] )
            else:
                  raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

            assert torch.allclose(logits[0, :3, :3, :3]       ,		a       ,		atol=1e-4 )
      else:
            assert logits.shape == (1, 1_000)

            if mobilevit_name == "mobilevit_s":
                  __snake_case      :    Union[str, Any]  			=torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] )
            elif mobilevit_name == "mobilevit_xs":
                  __snake_case      :    str  			=torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] )
            elif mobilevit_name == "mobilevit_xxs":
                  __snake_case      :    str  			=torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] )
            else:
                  raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

            assert torch.allclose(logits[0, :3]       ,		a       ,		atol=1e-4 )

      Path(a ).mkdir(exist_ok=a )
      print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}''' )
      model.save_pretrained(a )
      print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
      image_processor.save_pretrained(a )

      if push_to_hub:
            __snake_case      :    str  			={
                '''mobilevit_s''': '''mobilevit-small''',
                '''mobilevit_xs''': '''mobilevit-x-small''',
                '''mobilevit_xxs''': '''mobilevit-xx-small''',
                '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
                '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
                '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
            }

            print('''Pushing to the hub...''' )
            __snake_case      :    Union[str, Any]  			=model_mapping[mobilevit_name]
            image_processor.push_to_hub(a       ,		organization='''apple''' )
            model.push_to_hub(a       ,		organization='''apple''' )


if __name__ == "__main__":
    UpperCamelCase_	:				Optional[Any]										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--mobilevit_name""",
        default="""mobilevit_s""",
        type=str,
        help=(
            """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
            """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
        ),
    )
    parser.add_argument(
        """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )

    UpperCamelCase_	:				Union[str, Any]										= parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )










"""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     __lowercase					(    a :				Any       ,		a :				List[Any] )					->					Dict:
      __snake_case      :    Any  			=checkpoint

      __snake_case      :    Dict  			={}

      __snake_case      :    List[Any]  			=vae_state_dict['''encoder.conv_in.weight''']
      __snake_case      :    List[str]  			=vae_state_dict['''encoder.conv_in.bias''']
      __snake_case      :    Union[str, Any]  			=vae_state_dict['''encoder.conv_out.weight''']
      __snake_case      :    Union[str, Any]  			=vae_state_dict['''encoder.conv_out.bias''']
      __snake_case      :    str  			=vae_state_dict['''encoder.norm_out.weight''']
      __snake_case      :    str  			=vae_state_dict['''encoder.norm_out.bias''']

      __snake_case      :    Tuple  			=vae_state_dict['''decoder.conv_in.weight''']
      __snake_case      :    str  			=vae_state_dict['''decoder.conv_in.bias''']
      __snake_case      :    List[str]  			=vae_state_dict['''decoder.conv_out.weight''']
      __snake_case      :    Tuple  			=vae_state_dict['''decoder.conv_out.bias''']
      __snake_case      :    Union[str, Any]  			=vae_state_dict['''decoder.norm_out.weight''']
      __snake_case      :    List[str]  			=vae_state_dict['''decoder.norm_out.bias''']

      __snake_case      :    Tuple  			=vae_state_dict['''quant_conv.weight''']
      __snake_case      :    List[str]  			=vae_state_dict['''quant_conv.bias''']
      __snake_case      :    Optional[int]  			=vae_state_dict['''post_quant_conv.weight''']
      __snake_case      :    Tuple  			=vae_state_dict['''post_quant_conv.bias''']

      # Retrieves the keys for the encoder down blocks only
      __snake_case      :    Union[str, Any]  			=len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
      __snake_case      :    Union[str, Any]  			={
          layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(a )
      }

      # Retrieves the keys for the decoder up blocks only
      __snake_case      :    List[Any]  			=len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
      __snake_case      :    List[Any]  			={
          layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(a )
      }

      for i in range(a ):
            __snake_case      :    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:
                  __snake_case      :    Optional[Any]  			=vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.weight''' )
                  __snake_case      :    Optional[int]  			=vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.bias''' )

            __snake_case      :    Optional[Any]  			=renew_vae_resnet_paths(a )
            __snake_case      :    Union[str, Any]  			={'''old''': f'''down.{i}.block''', '''new''': f'''down_blocks.{i}.resnets'''}
            assign_to_checkpoint(a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		config=a )

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

            __snake_case      :    List[str]  			=renew_vae_resnet_paths(a )
            __snake_case      :    List[str]  			={'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
            assign_to_checkpoint(a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		config=a )

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

      for i in range(a ):
            __snake_case      :    List[Any]  			=num_up_blocks - 1 - i
            __snake_case      :    Dict  			=[
                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:
                  __snake_case      :    int  			=vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.weight'''
                  ]
                  __snake_case      :    Optional[Any]  			=vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.bias'''
                  ]

            __snake_case      :    List[str]  			=renew_vae_resnet_paths(a )
            __snake_case      :    int  			={'''old''': f'''up.{block_id}.block''', '''new''': f'''up_blocks.{i}.resnets'''}
            assign_to_checkpoint(a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		config=a )

      __snake_case      :    Any  			=[key for key in vae_state_dict if '''decoder.mid.block''' in key]
      __snake_case      :    Dict  			=2
      for i in range(1       ,		num_mid_res_blocks + 1 ):
            __snake_case      :    Dict  			=[key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

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

      __snake_case      :    Optional[Any]  			=[key for key in vae_state_dict if '''decoder.mid.attn''' in key]
      __snake_case      :    Dict  			=renew_vae_attention_paths(a )
      __snake_case      :    List[str]  			={'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
      assign_to_checkpoint(a       ,		a       ,		a       ,		additional_replacements=[meta_path]       ,		config=a )
      conv_attn_to_linear(a )
      return new_checkpoint



def     __lowercase					(    a :				str       ,		a :				str       ,		)					->					Optional[int]:
      # Only support V1
      __snake_case      :    Union[str, Any]  			=requests.get(
          ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
      __snake_case      :    Union[str, Any]  			=io.BytesIO(r.content )

      __snake_case      :    Any  			=OmegaConf.load(a )
      __snake_case      :    Union[str, Any]  			=512
      __snake_case      :    List[str]  			='''cuda''' if torch.cuda.is_available() else '''cpu'''
      if checkpoint_path.endswith('''safetensors''' ):
            from safetensors import safe_open

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

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

      __snake_case      :    str  			=AutoencoderKL(**a )
      vae.load_state_dict(a )
      vae.save_pretrained(a )


if __name__ == "__main__":
    UpperCamelCase_	:				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.""")

    UpperCamelCase_	:				Optional[Any]										= parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


