


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
    )




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)
