


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__   	=				logging.get_logger(__name__)
def 							_UpperCAmelCase       ():
       _A   : Dict       					=				DetaConfig(
           num_queries=900       ,							encoder_ffn_dim=2048       ,							decoder_ffn_dim=2048       ,							num_feature_levels=5       ,							assign_first_stage=UpperCamelCase__       ,							with_box_refine=UpperCamelCase__       ,							two_stage=UpperCamelCase__       ,							)

       # set labels
       _A   : Any       					=				91
       _A   : List[Any]       					=				"huggingface/label-files"
       _A   : Optional[Any]       					=				"coco-detection-id2label.json"
       _A   : Tuple       					=				json.load(open(cached_download(hf_hub_url(UpperCamelCase__       ,							UpperCamelCase__       ,							repo_type="dataset"		)		)       ,							"r"		)		)
       _A   : List[Any]       					=				{int(UpperCamelCase__		): v for k, v in idalabel.items()}
       _A   : int       					=				idalabel
       _A   : Union[str, Any]       					=				{v: k for k, v in idalabel.items()}

       return config
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]		):
       _A   : Dict       					=				[]

       # stem
       # fmt: off
       rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")		)
       rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")		)
       rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")		)
       rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")		)
       rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")		)
       # stages
       for stage_idx in range(len(config.backbone_config.depths		)		):
              for layer_idx in range(config.backbone_config.depths[stage_idx]		):
                     # shortcut
                     if layer_idx == 0:
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                                )		)
                     # 3 convs
                     for i in range(3		):
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                                )		)
                            rename_keys.append(
                                (
                                    f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                                    f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                                )		)
    # transformer encoder
       for i in range(config.encoder_layers		):
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")		)
              rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")		)

       # transformer decoder
       for i in range(config.decoder_layers		):
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")		)
              rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")		)

       # fmt: on

       return rename_keys
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]		):
       _A   : Optional[Any]       					=				dct.pop(UpperCamelCase__		)
       _A   : List[Any]       					=				val
def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
       _A   : Any       					=				config.d_model
       for i in range(config.decoder_layers		):
              # read in weights + bias of input projection layer of self-attention
              _A   : Optional[Any]       					=				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
              _A   : int       					=				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"		)
              # next, add query, keys and values (in that order) to the state dict
              _A   : Tuple       					=				in_proj_weight[:hidden_size, :]
              _A   : List[str]       					=				in_proj_bias[:hidden_size]
              _A   : Any       					=				in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              _A   : List[Any]       					=				in_proj_bias[hidden_size : hidden_size * 2]
              _A   : Any       					=				in_proj_weight[-hidden_size:, :]
              _A   : Any       					=				in_proj_bias[-hidden_size:]
def 							_UpperCAmelCase       ():
       _A   : Union[str, Any]       					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       _A   : int       					=				Image.open(requests.get(UpperCamelCase__       ,							stream=UpperCamelCase__		).raw		)

       return im
@torch.no_grad()
def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[Any]		):
       _A   : int       					=				get_deta_config()

       # load original state dict
       if model_name == "deta-resnet-50":
              _A   : Optional[int]       					=				"adet_checkpoint0011.pth"
       elif model_name == "deta-resnet-50-24-epochs":
              _A   : Dict       					=				"adet_2x_checkpoint0023.pth"
       else:
              raise ValueError(f"Model name {model_name} not supported"		)
       _A   : Union[str, Any]       					=				hf_hub_download(repo_id="nielsr/deta-checkpoints"       ,							filename=UpperCamelCase__		)
       _A   : Tuple       					=				torch.load(UpperCamelCase__       ,							map_location="cpu"		)["model"]

       # rename keys
       _A   : Union[str, Any]       					=				create_rename_keys(UpperCamelCase__		)
       for src, dest in rename_keys:
              rename_key(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       read_in_decoder_q_k_v(UpperCamelCase__       ,							UpperCamelCase__		)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     _A   : Union[str, Any]       					=				state_dict.pop(UpperCamelCase__		)
                     _A   : Any       					=				val
              if "input_proj" in key:
                     _A   : Union[str, Any]       					=				state_dict.pop(UpperCamelCase__		)
                     _A   : int       					=				val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     _A   : Tuple       					=				state_dict.pop(UpperCamelCase__		)
                     _A   : Optional[Any]       					=				val

    # finally, create HuggingFace model and load state dict
       _A   : str       					=				DetaForObjectDetection(UpperCamelCase__		)
       model.load_state_dict(UpperCamelCase__		)
       model.eval()

       _A   : List[Any]       					=				"cuda" if torch.cuda.is_available() else "cpu"
       model.to(UpperCamelCase__		)

       # load image processor
       _A   : int       					=				DetaImageProcessor(format="coco_detection"		)

       # verify our conversion on image
       _A   : int       					=				prepare_img()
       _A   : int       					=				processor(images=UpperCamelCase__       ,							return_tensors="pt"		)
       _A   : List[Any]       					=				encoding["pixel_values"]
       _A   : Optional[Any]       					=				model(pixel_values.to(UpperCamelCase__		)		)

       # verify logits
       if model_name == "deta-resnet-50":
              _A   : List[str]       					=				torch.tensor(
                  [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]		)
              _A   : List[str]       					=				torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]		)
       elif model_name == "deta-resnet-50-24-epochs":
              _A   : Any       					=				torch.tensor(
                  [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]		)
              _A   : str       					=				torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]		)

       assert torch.allclose(outputs.logits[0, :3, :3]       ,							expected_logits.to(UpperCamelCase__		)       ,							atol=1E-4		)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,							expected_boxes.to(UpperCamelCase__		)       ,							atol=1E-4		)
       print("Everything ok!"		)

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."		)
              Path(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
              model.save_pretrained(UpperCamelCase__		)
              processor.save_pretrained(UpperCamelCase__		)

       # Push to hub
       if push_to_hub:
              print("Pushing model and processor to hub..."		)
              model.push_to_hub(f"jozhang97/{model_name}"		)
              processor.push_to_hub(f"jozhang97/{model_name}"		)


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

   parser.add_argument(
       '--model_name',
       type=str,
       default='deta-resnet-50',
       choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
       help='Name of the 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.',
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )
   lowerCAmelCase__   	=				parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
       _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
       _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
       _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data

       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
       _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
       _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data

       _A   : str       					=				4

       def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
              _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

              _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
              _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

       def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
              _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                     _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                            module_index += 1

              if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
                     _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                     _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks		):
              _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                     _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                     _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                     _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1

       _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
       module_index += 1
       _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data

       return new_model_architecture.state_dict()


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

    parser.add_argument(
        '--checkpoint_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
        type=str,
        required=False,
        help='Path to the checkpoint to convert.',
    )

    parser.add_argument(
        '--config_file',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
        type=str,
        required=False,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument(
        '--dump_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
        type=str,
        required=False,
        help='Path to the output model.',
    )

    lowerCAmelCase__   	=				parser.parse_args()

    lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')

    with open(args.config_file) as f:
        lowerCAmelCase__   	=				json.loads(f.read())

    lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

    if "sde" in config:
        del config["sde"]

    lowerCAmelCase__   	=				UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

        lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)
