





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_				=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_				=			[]
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
            F'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
            F'decoder.layers.{i}.encoder_attn.out_proj.bias',
        )
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
    )

    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Dict       ,      snake_case_	:  Optional[Any]       )      ->				Optional[Any]:
   __snake_case							       =   state_dict.pop(__lowerCamelCase       )
   __snake_case							       =   val






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				int:
   __snake_case							       =   OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         __snake_case							       =   key.replace('''backbone.0.body'''       ,      '''backbone.conv_encoder.model'''       )
         __snake_case							       =   value
      else:
         __snake_case							       =   value

   return new_state_dict






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  Optional[int]=False       )      ->				Tuple:
   __snake_case							       =   ""
   if is_panoptic:
      __snake_case							       =   "conditional_detr."

   # first: transformer encoder
   for i in range(6       ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      __snake_case							       =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      __snake_case							       =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      __snake_case							       =   in_proj_weight[:256, :]
      __snake_case							       =   in_proj_bias[:256]
      __snake_case							       =   in_proj_weight[256:512, :]
      __snake_case							       =   in_proj_bias[256:512]
      __snake_case							       =   in_proj_weight[-256:, :]
      __snake_case							       =   in_proj_bias[-256:]






def lowerCamelCase__    (							)      ->				Optional[Any]:
   __snake_case							       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
   __snake_case							       =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase       ).raw       )

   return im






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				List[Any]:
   __snake_case							       =   ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      __snake_case							       =   "resnet101"
   if "dc5" in model_name:
      __snake_case							       =   True
   __snake_case							       =   "panoptic" in model_name
   if is_panoptic:
      __snake_case							       =   250
   else:
      __snake_case							       =   91
      __snake_case							       =   "huggingface/label-files"
      __snake_case							       =   "coco-detection-id2label.json"
      __snake_case							       =   json.load(open(hf_hub_download(__lowerCamelCase       ,      __lowerCamelCase       ,      repo_type='''dataset'''       )       ,      '''r'''       )       )
      __snake_case							       =   {int(__lowerCamelCase       ): v for k, v in idalabel.items()}
      __snake_case							       =   idalabel
      __snake_case							       =   {v: k for k, v in idalabel.items()}

   # load image processor
   __snake_case							       =   "coco_panoptic" if is_panoptic else "coco_detection"
   __snake_case							       =   ConditionalDetrImageProcessor(format=__lowerCamelCase       )

   # prepare image
   __snake_case							       =   prepare_img()
   __snake_case							       =   image_processor(images=__lowerCamelCase       ,      return_tensors='''pt'''       )
   __snake_case							       =   encoding["pixel_values"]

   logger.info(f"""Converting model {model_name}..."""       )

   # load original model from torch hub
   __snake_case							       =   torch.hub.load('''DeppMeng/ConditionalDETR'''       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase       ).eval()
   __snake_case							       =   conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         __snake_case							       =   "conditional_detr." + src
      rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       )
   __snake_case							       =   rename_backbone_keys(__lowerCamelCase       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase       )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   __snake_case							       =   "conditional_detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('''conditional_detr'''       )
             and not key.startswith('''class_labels_classifier'''       )
             and not key.startswith('''bbox_predictor'''       )
         ):
            __snake_case							       =   state_dict.pop(__lowerCamelCase       )
            __snake_case							       =   val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            __snake_case							       =   state_dict.pop(__lowerCamelCase       )
            __snake_case							       =   val
         elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
            continue
         else:
            __snake_case							       =   state_dict.pop(__lowerCamelCase       )
            __snake_case							       =   val
      else:
         if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
            __snake_case							       =   state_dict.pop(__lowerCamelCase       )
            __snake_case							       =   val
    # finally, create HuggingFace model and load state dict
   __snake_case							       =   ConditionalDetrForSegmentation(__lowerCamelCase       ) if is_panoptic else ConditionalDetrForObjectDetection(__lowerCamelCase       )
   model.load_state_dict(__lowerCamelCase       )
   model.eval()
   model.push_to_hub(repo_id=__lowerCamelCase       ,      organization='''DepuMeng'''       ,      commit_message='''Add model'''       )
   # verify our conversion
   __snake_case							       =   conditional_detr(__lowerCamelCase       )
   __snake_case							       =   model(__lowerCamelCase       )
   assert torch.allclose(outputs.logits       ,      original_outputs['''pred_logits''']       ,      atol=1e-4       )
   assert torch.allclose(outputs.pred_boxes       ,      original_outputs['''pred_boxes''']       ,      atol=1e-4       )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks       ,      original_outputs['''pred_masks''']       ,      atol=1e-4       )

   # Save model and image processor
   logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
   Path(__lowerCamelCase       ).mkdir(exist_ok=__lowerCamelCase       )
   model.save_pretrained(__lowerCamelCase       )
   image_processor.save_pretrained(__lowerCamelCase       )


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

    parser.add_argument(
        '--model_name',
        default='conditional_detr_resnet50',
        type=str,
        help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
    )
    snake_case_				=			parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""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)


