code: 
  
  
  import argparse
  import pathlib
  from pathlib import Path
  from tempfile import TemporaryDirectory
  
  import esm as esm_module
  import torch
  from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
  from esm.esmfold.va.pretrained import esmfold_va
  
  from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
  from transformers.models.esm.modeling_esm import (
      EsmForMaskedLM,
      EsmForSequenceClassification,
      EsmIntermediate,
      EsmLayer,
      EsmOutput,
      EsmSelfAttention,
      EsmSelfOutput,
  )
  from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
  from transformers.models.esm.tokenization_esm import EsmTokenizer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				[
      (
          'protein1',
          'MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA',
      ),
      ('protein2', 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'),
      ('protein3', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'),
      ('protein4', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'),
  ]
  
  lowerCAmelCase__   	=				{
      'esm1b_t33_650M_UR50S': esm_module.pretrained.esm1b_t33_650M_UR50S,
      'esm1v_t33_650M_UR90S_1': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
      'esm1v_t33_650M_UR90S_2': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
      'esm1v_t33_650M_UR90S_3': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
      'esm1v_t33_650M_UR90S_4': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
      'esm1v_t33_650M_UR90S_5': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
      'esm2_t48_15B_UR50D': esm_module.pretrained.esm2_t48_15B_UR50D,
      'esm2_t36_3B_UR50D': esm_module.pretrained.esm2_t36_3B_UR50D,
      'esm2_t33_650M_UR50D': esm_module.pretrained.esm2_t33_650M_UR50D,
      'esm2_t30_150M_UR50D': esm_module.pretrained.esm2_t30_150M_UR50D,
      'esm2_t12_35M_UR50D': esm_module.pretrained.esm2_t12_35M_UR50D,
      'esm2_t6_8M_UR50D': esm_module.pretrained.esm2_t6_8M_UR50D,
      'esmfold_v1': esmfold_va,
  }
  
  lowerCAmelCase__   	=				list('ARNDCQEGHILKMFPSTWYV')
  
  lowerCAmelCase__   	=				restypes + ['X']
  lowerCAmelCase__   	=				restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']
  def 							_UpperCAmelCase       ():
         with TemporaryDirectory() as tempdir:
                _A   : Dict       					=				"\n".join(UpperCamelCase__		)
                _A   : Any       					=				Path(UpperCamelCase__		) / "vocab.txt"
                vocab_file.write_text(UpperCamelCase__		)
                _A   : Union[str, Any]       					=				EsmTokenizer(vocab_file=str(UpperCamelCase__		)		)
         _A   : int       					=				0  # Overlaps with 'A' but that seems to be what they want
         return hf_tokenizer
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						int		):
         _A   : Tuple       					=				our_module.load_state_dict(original_module.state_dict()		)
         if status.missing_keys:
                raise ValueError(f"Missing keys: {status.missing_keys}"		)
         if status.unexpected_keys:
                raise ValueError(f"Unexpected keys: {status.unexpected_keys}"		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str		):
         if model.startswith("esmfold"		):
                _A   : Dict       					=				MODEL_MAPPING[model]()
         else:
                _A					,    _A   : List[Any]       					=				MODEL_MAPPING[model]()
         esm.eval()  # disable dropout
  
         if model.startswith("esmfold"		):
                _A   : List[Any]       					=				esm.esm.embed_dim
                _A   : Tuple       					=				esm.esm.num_layers
                _A   : Optional[int]       					=				esm.esm.attention_heads
                _A   : Dict       					=				4 * embed_dim
                _A   : List[Any]       					=				esm.esm.token_dropout
                _A   : Any       					=				False  # This code path does not exist in ESM-2
                _A   : str       					=				"rotary"
                _A   : Optional[Any]       					=				True
                _A   : Any       					=				EsmFoldConfig()
                for key, val in esm.cfg.items():
                       if hasattr(UpperCamelCase__       ,							UpperCamelCase__		) and key != "trunk":
                              setattr(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                for key, val in esm.cfg.trunk.items():
                       if hasattr(esmfold_config.trunk       ,							UpperCamelCase__		) and key != "structure_module":
                              setattr(esmfold_config.trunk       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                for key, val in esm.cfg.trunk.structure_module.items():
                       if hasattr(esmfold_config.trunk.structure_module       ,							UpperCamelCase__		):
                              setattr(esmfold_config.trunk.structure_module       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         elif hasattr(UpperCamelCase__       ,							"args"		):
                # Indicates an ESM-1b or ESM-1v model
                _A   : List[Any]       					=				esm.args.embed_dim
                _A   : Optional[Any]       					=				esm.args.layers
                _A   : Union[str, Any]       					=				esm.args.attention_heads
                _A   : Dict       					=				esm.args.ffn_embed_dim
                _A   : str       					=				esm.args.token_dropout
                _A   : Dict       					=				True if esm.emb_layer_norm_before else False
                _A   : Optional[Any]       					=				"absolute"
                _A   : List[Any]       					=				False
                _A   : List[Any]       					=				None
         else:
                # Indicates an ESM-2 model
                _A   : Any       					=				esm.embed_dim
                _A   : Optional[int]       					=				esm.num_layers
                _A   : Union[str, Any]       					=				esm.attention_heads
                _A   : Any       					=				4 * embed_dim  # This is hardcoded in ESM-2
                _A   : Union[str, Any]       					=				esm.token_dropout
                _A   : List[str]       					=				False  # This code path does not exist in ESM-2
                _A   : Tuple       					=				"rotary"
                _A   : Tuple       					=				False
                _A   : Union[str, Any]       					=				None
  
         if is_folding_model:
                _A   : Dict       					=				esm.esm.alphabet
         _A   : Dict       					=				tuple(alphabet.all_toks		)
         _A   : List[str]       					=				alphabet.mask_idx
         _A   : Dict       					=				alphabet.padding_idx
  
         if is_folding_model:
                _A   : List[Any]       					=				esm.esm
         else:
                _A   : Union[str, Any]       					=				esm
  
         _A   : List[Any]       					=				EsmConfig(
             vocab_size=original_esm_model.embed_tokens.num_embeddings       ,							mask_token_id=UpperCamelCase__       ,							hidden_size=UpperCamelCase__       ,							num_hidden_layers=UpperCamelCase__       ,							num_attention_heads=UpperCamelCase__       ,							intermediate_size=UpperCamelCase__       ,							max_position_embeddings=1026       ,							layer_norm_eps=1E-5       ,							attention_probs_dropout_prob=0.0       ,							hidden_dropout_prob=0.0       ,							pad_token_id=UpperCamelCase__       ,							emb_layer_norm_before=UpperCamelCase__       ,							token_dropout=UpperCamelCase__       ,							position_embedding_type=UpperCamelCase__       ,							is_folding_model=UpperCamelCase__       ,							esmfold_config=UpperCamelCase__       ,							vocab_list=UpperCamelCase__       ,							)
         if classification_head:
                _A   : int       					=				esm.classification_heads["mnli"].out_proj.weight.shape[0]
         print("Our ESM config:"       ,							UpperCamelCase__		)
  
         if model.startswith("esmfold"		):
                _A   : str       					=				EsmForProteinFolding
         elif classification_head:
                _A   : Optional[int]       					=				EsmForSequenceClassification
         else:
                _A   : Tuple       					=				EsmForMaskedLM
         _A   : Any       					=				model_class(UpperCamelCase__		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         _A   : Optional[int]       					=				original_esm_model.embed_tokens.weight
         if position_embedding_type == "absolute":
                _A   : Dict       					=				original_esm_model.embed_positions.weight
  
         if config.emb_layer_norm_before:
                _A   : List[Any]       					=				original_esm_model.emb_layer_norm_before.weight
                _A   : Optional[int]       					=				original_esm_model.emb_layer_norm_before.bias
  
         _A   : int       					=				original_esm_model.emb_layer_norm_after.weight
         _A   : Tuple       					=				original_esm_model.emb_layer_norm_after.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                _A   : EsmLayer       					=				model.esm.encoder.layer[i]
                # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
                _A   : List[Any]       					=				original_esm_model.layers[i]
  
                # self attention
                _A   : EsmSelfAttention       					=				layer.attention.self
                assert (
                    esm_layer.self_attn.k_proj.weight.data.shape
                    == esm_layer.self_attn.q_proj.weight.data.shape
                    == esm_layer.self_attn.v_proj.weight.data.shape
                    == torch.Size((config.hidden_size, config.hidden_size)		)
                )
  
                _A   : List[str]       					=				esm_layer.self_attn.q_proj.weight
                _A   : Optional[int]       					=				esm_layer.self_attn.q_proj.bias
                _A   : str       					=				esm_layer.self_attn.k_proj.weight
                _A   : List[Any]       					=				esm_layer.self_attn.k_proj.bias
                _A   : List[str]       					=				esm_layer.self_attn.v_proj.weight
                _A   : Union[str, Any]       					=				esm_layer.self_attn.v_proj.bias
  
                if getattr(esm_layer.self_attn       ,							"rot_emb"       ,							UpperCamelCase__		) is not None:
                       # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
                       # During the training of ESM-2 the model was converted to float16 precision, which also converts
                       # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
                       # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
                       # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
                       # we make sure the new model copies the data from the old inv_freq.
                       _A   : Union[str, Any]       					=				esm_layer.self_attn.rot_emb.inv_freq
  
                # LayerNorm changes for pre-activation
                _A   : Tuple       					=				esm_layer.self_attn_layer_norm.weight
                _A   : Optional[int]       					=				esm_layer.self_attn_layer_norm.bias
                _A   : Tuple       					=				esm_layer.final_layer_norm.weight
                _A   : str       					=				esm_layer.final_layer_norm.bias
  
                # self-attention output
                _A   : EsmSelfOutput       					=				layer.attention.output
                assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
                _A   : Tuple       					=				esm_layer.self_attn.out_proj.weight
                _A   : Any       					=				esm_layer.self_attn.out_proj.bias
  
                # intermediate
                _A   : EsmIntermediate       					=				layer.intermediate
                assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
                _A   : Union[str, Any]       					=				esm_layer.fca.weight
                _A   : Optional[int]       					=				esm_layer.fca.bias
  
                # output
                _A   : EsmOutput       					=				layer.output
                assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
                _A   : Union[str, Any]       					=				esm_layer.fca.weight
                _A   : Optional[Any]       					=				esm_layer.fca.bias
                # end of layer
  
         if is_folding_model:
                _A   : List[Any]       					=				esm.esm_s_combine.data
                _A   : Dict       					=				esm.afa_to_esm.data
                transfer_and_check_weights(esm.embedding       ,							model.embedding		)
                transfer_and_check_weights(esm.esm_s_mlp       ,							model.esm_s_mlp		)
                transfer_and_check_weights(esm.trunk       ,							model.trunk		)
                transfer_and_check_weights(esm.distogram_head       ,							model.distogram_head		)
                transfer_and_check_weights(esm.ptm_head       ,							model.ptm_head		)
                transfer_and_check_weights(esm.lm_head       ,							model.lm_head		)
                transfer_and_check_weights(esm.lddt_head       ,							model.lddt_head		)
  
         elif classification_head:
                _A   : Union[str, Any]       					=				esm.esm.classification_heads["mnli"].dense.weight
                _A   : Dict       					=				esm.classification_heads["mnli"].dense.bias
                _A   : Any       					=				esm.classification_heads["mnli"].out_proj.weight
                _A   : Any       					=				esm.classification_heads["mnli"].out_proj.bias
         else:
                # LM Head
                _A   : Tuple       					=				esm.lm_head.dense.weight
                _A   : Any       					=				esm.lm_head.dense.bias
                _A   : List[str]       					=				esm.lm_head.layer_norm.weight
                _A   : str       					=				esm.lm_head.layer_norm.bias
                _A   : Any       					=				esm.lm_head.weight
                _A   : int       					=				esm.lm_head.bias
  
         # Contact prediction head
         transfer_and_check_weights(esm.contact_head       ,							model.esm.contact_head		)
  
         # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
         if is_folding_model:
                # Folding models aren't trained on masked inputs and don't like mask tokens.
                _A   : Any       					=				SAMPLE_DATA[:2]
         else:
                _A   : Union[str, Any]       					=				SAMPLE_DATA
  
         if is_folding_model:
                _A   : Any       					=				get_esmfold_tokenizer()
                _A   : int       					=				hf_tokenizer(
                    [row[1] for row in sample_data]       ,							return_tensors="pt"       ,							padding=UpperCamelCase__       ,							add_special_tokens=UpperCamelCase__		)
                _A					,    _A					,    _A					,    _A					,    _A   : Optional[Any]       					=				esmfold_encode_sequences([row[1] for row in sample_data]		)
                _A   : List[Any]       					=				torch.all(hf_tokens["input_ids"] == esmfold_aas		) and torch.all(
                    hf_tokens["attention_mask"] == esmfold_mask		)
         else:
                # Let's check that we get the same results.
                _A   : Any       					=				alphabet.get_batch_converter()
                _A					,    _A					,    _A   : List[str]       					=				batch_converter(UpperCamelCase__		)
                # Prepare tokenizer and make sure it matches
                with TemporaryDirectory() as tempdir:
                       _A   : Any       					=				"\n".join(alphabet.all_toks		)
                       _A   : str       					=				Path(UpperCamelCase__		) / "vocab.txt"
                       vocab_file.write_text(UpperCamelCase__		)
                       _A   : Optional[Any]       					=				EsmTokenizer(vocab_file=str(UpperCamelCase__		)		)
  
                _A   : List[Any]       					=				hf_tokenizer([row[1] for row in sample_data]       ,							return_tensors="pt"       ,							padding=UpperCamelCase__		)
                _A   : List[Any]       					=				torch.all(hf_tokens["input_ids"] == batch_tokens		)
  
         print("Do both models tokenizers output the same tokens?"       ,							"🔥" if success else "💩"		)
         if not success:
                raise Exception("Tokenization does not match!"		)
  
         with torch.no_grad():
                if is_folding_model:
                       # Let's test the model in parts
                       # ESMFold always converts the ESM stem to float16, which requires float16 ops
                       # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
                       # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
                       # original and the converted model on the GPU at the same time.
                       _A   : Any       					=				esm.cuda().infer([row[1] for row in sample_data]		)
                       _A   : str       					=				model.cuda()(
                           input_ids=hf_tokens["input_ids"].cuda()       ,							attention_mask=hf_tokens["attention_mask"].cuda()		)
                else:
                       _A   : Union[str, Any]       					=				model(**UpperCamelCase__       ,							output_hidden_states=UpperCamelCase__		)
                       _A   : Optional[Any]       					=				our_output["logits"]
                       if classification_head:
                              _A   : List[str]       					=				esm.model.classification_heads["mnli"](esm.extract_features(UpperCamelCase__		)		)
                       else:
                              _A   : int       					=				esm(hf_tokens["input_ids"]       ,							repr_layers=list(range(999		)		)		)
                              _A   : str       					=				their_output["logits"]
  
                if is_folding_model:
                       _A   : Dict       					=				torch.max(torch.abs(our_output["positions"] - their_output["positions"]		)		).item()
                       _A   : List[str]       					=				torch.allclose(our_output["positions"]       ,							their_output["positions"]       ,							atol=1E-5		)
                else:
                       _A   : List[Any]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
                       _A   : List[Any]       					=				torch.allclose(UpperCamelCase__       ,							UpperCamelCase__       ,							atol=1E-5		)
  
                print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-5
                print("Do both models output the same tensors?"       ,							"🔥" if success else "💩"		)
  
                if not success:
                       raise Exception("Something went wRoNg"		)
  
                if not is_folding_model:
                       # Let's check contact prediction too
                       _A   : int       					=				model.predict_contacts(hf_tokens["input_ids"]       ,							hf_tokens["attention_mask"]		)
                       _A   : Any       					=				esm.predict_contacts(hf_tokens["input_ids"]		)
                       _A   : Optional[int]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
                       _A   : Optional[Any]       					=				torch.allclose(UpperCamelCase__       ,							UpperCamelCase__       ,							atol=1E-5		)
  
                       print("Contact prediction testing:"		)
                       print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-5
                       print("Do both models output the same tensors?"       ,							"🔥" if success else "💩"		)
  
                       if not success:
                              raise Exception("Something went wRoNg"		)
  
                pathlib.Path(UpperCamelCase__		).mkdir(parents=UpperCamelCase__       ,							exist_ok=UpperCamelCase__		)
                print(f"Saving model to {pytorch_dump_folder_path}"		)
                model.save_pretrained(UpperCamelCase__		)
  
                del esm  # Free up some memory before continuing
  
         print(f"Saving tokenizer to {pytorch_dump_folder_path}"		)
         hf_tokenizer.save_pretrained(UpperCamelCase__		)
  
         if push_to_repo:
                model.push_to_hub(repo_id=UpperCamelCase__       ,							use_auth_token=UpperCamelCase__		)
                hf_tokenizer.push_to_hub(repo_id=UpperCamelCase__       ,							use_auth_token=UpperCamelCase__		)
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--pytorch_dump_folder_path', type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--classification_head', action='store_true', help='Whether to convert a final classification head.'
      )
      parser.add_argument('--model', default=None, type=str, required=True, help='Name of model to convert.')
      parser.add_argument('--push_to_repo', type=str, help='Repo to upload to (including username!).')
      parser.add_argument('--auth_token', type=str, help='HuggingFace auth token.')
      lowerCAmelCase__   	=				parser.parse_args()
      convert_esm_checkpoint_to_pytorch(
          args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
      )
  
style_context: 
  
  
  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)
  