





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_UpperCAmelCase										=					get_logger(__name__)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       if text.startswith(UpperCamelCase_	):
              return text[len(UpperCamelCase_	) :]
       return text  # or whatever




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int:
       return {remove_prefix(UpperCamelCase_       ,		"model."	): v for k, v in sd.items()}




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	{}
       for k in state_dicts[0].keys():
              UpperCamelCase_ 							=	[sd[k] for sd in state_dicts]
              UpperCamelCase_ 							=	sum(UpperCamelCase_	) / len(UpperCamelCase_	)
              assert isinstance(UpperCamelCase_       ,		torch.Tensor	)
              UpperCamelCase_ 							=	new_t
       return new_sd




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		None:
       UpperCamelCase_ 							=	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_	)
       if os.path.isfile(UpperCamelCase_	):
              UpperCamelCase_ 							=	[pl_ckpt_path]
       else:
              assert os.path.isdir(UpperCamelCase_	)
              UpperCamelCase_ 							=	list(Path(UpperCamelCase_	).glob("*.ckpt"	)	)
              assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

       if len(UpperCamelCase_	) > 1:
              logger.info(F'''averaging the weights of {ckpt_files}'''	)

       UpperCamelCase_ 							=	[sanitize(torch.load(UpperCamelCase_       ,		map_location="cpu"	)["state_dict"]	) for x in ckpt_files]
       UpperCamelCase_ 							=	average_state_dicts(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	hf_model.load_state_dict(UpperCamelCase_       ,		strict=UpperCamelCase_	)
       assert not missing, F'''missing keys: {missing}'''
       hf_model.save_pretrained(UpperCamelCase_	)
       try:
              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(UpperCamelCase_	)
              tok.save_pretrained(UpperCamelCase_	)
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)








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()
_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					[
    (
        'protein1',
        'MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA',
    ),
    ('protein2', 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'),
    ('protein3', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'),
    ('protein4', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'),
]

_UpperCAmelCase										=					{
    '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,
}

_UpperCAmelCase										=					list('ARNDCQEGHILKMFPSTWYV')

_UpperCAmelCase										=					restypes + ['X']
_UpperCAmelCase										=					restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']




def 			lowerCAmelCase_			(				)  ->		Union[str, Any]:
       with TemporaryDirectory() as tempdir:
              UpperCamelCase_ 							=	"\n".join(UpperCamelCase_	)
              UpperCamelCase_ 							=	Path(UpperCamelCase_	) / "vocab.txt"
              vocab_file.write_text(UpperCamelCase_	)
              UpperCamelCase_ 							=	EsmTokenizer(vocab_file=str(UpperCamelCase_	)	)
       UpperCamelCase_ 							=	0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	MODEL_MAPPING[model]()
       else:
              UpperCamelCase_							,			UpperCamelCase_ 							=	MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	esm.esm.embed_dim
              UpperCamelCase_ 							=	esm.esm.num_layers
              UpperCamelCase_ 							=	esm.esm.attention_heads
              UpperCamelCase_ 							=	4 * embed_dim
              UpperCamelCase_ 							=	esm.esm.token_dropout
              UpperCamelCase_ 							=	False  # This code path does not exist in ESM-2
              UpperCamelCase_ 							=	"rotary"
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	esm.args.embed_dim
              UpperCamelCase_ 							=	esm.args.layers
              UpperCamelCase_ 							=	esm.args.attention_heads
              UpperCamelCase_ 							=	esm.args.ffn_embed_dim
              UpperCamelCase_ 							=	esm.args.token_dropout
              UpperCamelCase_ 							=	True if esm.emb_layer_norm_before else False
              UpperCamelCase_ 							=	"absolute"
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	None
       else:
              # Indicates an ESM-2 model
              UpperCamelCase_ 							=	esm.embed_dim
              UpperCamelCase_ 							=	esm.num_layers
              UpperCamelCase_ 							=	esm.attention_heads
              UpperCamelCase_ 							=	4 * embed_dim  # This is hardcoded in ESM-2
              UpperCamelCase_ 							=	esm.token_dropout
              UpperCamelCase_ 							=	False  # This code path does not exist in ESM-2
              UpperCamelCase_ 							=	"rotary"
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	None

       if is_folding_model:
              UpperCamelCase_ 							=	esm.esm.alphabet
       UpperCamelCase_ 							=	tuple(alphabet.all_toks	)
       UpperCamelCase_ 							=	alphabet.mask_idx
       UpperCamelCase_ 							=	alphabet.padding_idx

       if is_folding_model:
              UpperCamelCase_ 							=	esm.esm
       else:
              UpperCamelCase_ 							=	esm

       UpperCamelCase_ 							=	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:
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our ESM config:"       ,		UpperCamelCase_	)

       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	EsmForProteinFolding
       elif classification_head:
              UpperCamelCase_ 							=	EsmForSequenceClassification
       else:
              UpperCamelCase_ 							=	EsmForMaskedLM
       UpperCamelCase_ 							=	model_class(UpperCamelCase_	)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       UpperCamelCase_ 							=	original_esm_model.embed_tokens.weight
       if position_embedding_type == "absolute":
              UpperCamelCase_ 							=	original_esm_model.embed_positions.weight

       if config.emb_layer_norm_before:
              UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_before.weight
              UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_before.bias

       UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_after.weight
       UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_after.bias

       for i in range(config.num_hidden_layers	):
              # Encoder: start of layer
              UpperCamelCase_ 							=	model.esm.encoder.layer[i]
              # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
              UpperCamelCase_ 							=	original_esm_model.layers[i]

              # self attention
              UpperCamelCase_ 							=	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)	)
              )

              UpperCamelCase_ 							=	esm_layer.self_attn.q_proj.weight
              UpperCamelCase_ 							=	esm_layer.self_attn.q_proj.bias
              UpperCamelCase_ 							=	esm_layer.self_attn.k_proj.weight
              UpperCamelCase_ 							=	esm_layer.self_attn.k_proj.bias
              UpperCamelCase_ 							=	esm_layer.self_attn.v_proj.weight
              UpperCamelCase_ 							=	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.
                     UpperCamelCase_ 							=	esm_layer.self_attn.rot_emb.inv_freq

              # LayerNorm changes for pre-activation
              UpperCamelCase_ 							=	esm_layer.self_attn_layer_norm.weight
              UpperCamelCase_ 							=	esm_layer.self_attn_layer_norm.bias
              UpperCamelCase_ 							=	esm_layer.final_layer_norm.weight
              UpperCamelCase_ 							=	esm_layer.final_layer_norm.bias

              # self-attention output
              UpperCamelCase_ 							=	layer.attention.output
              assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
              UpperCamelCase_ 							=	esm_layer.self_attn.out_proj.weight
              UpperCamelCase_ 							=	esm_layer.self_attn.out_proj.bias

              # intermediate
              UpperCamelCase_ 							=	layer.intermediate
              assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
              UpperCamelCase_ 							=	esm_layer.fca.weight
              UpperCamelCase_ 							=	esm_layer.fca.bias

              # output
              UpperCamelCase_ 							=	layer.output
              assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
              UpperCamelCase_ 							=	esm_layer.fca.weight
              UpperCamelCase_ 							=	esm_layer.fca.bias
              # end of layer

       if is_folding_model:
              UpperCamelCase_ 							=	esm.esm_s_combine.data
              UpperCamelCase_ 							=	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:
              UpperCamelCase_ 							=	esm.esm.classification_heads["mnli"].dense.weight
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].dense.bias
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].out_proj.weight
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              UpperCamelCase_ 							=	esm.lm_head.dense.weight
              UpperCamelCase_ 							=	esm.lm_head.dense.bias
              UpperCamelCase_ 							=	esm.lm_head.layer_norm.weight
              UpperCamelCase_ 							=	esm.lm_head.layer_norm.bias
              UpperCamelCase_ 							=	esm.lm_head.weight
              UpperCamelCase_ 							=	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.
              UpperCamelCase_ 							=	SAMPLE_DATA[:2]
       else:
              UpperCamelCase_ 							=	SAMPLE_DATA

       if is_folding_model:
              UpperCamelCase_ 							=	get_esmfold_tokenizer()
              UpperCamelCase_ 							=	hf_tokenizer(
                  [row[1] for row in sample_data]       ,		return_tensors="pt"       ,		padding=UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_	)
              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	esmfold_encode_sequences([row[1] for row in sample_data]	)
              UpperCamelCase_ 							=	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.
              UpperCamelCase_ 							=	alphabet.get_batch_converter()
              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	batch_converter(UpperCamelCase_	)
              # Prepare tokenizer and make sure it matches
              with TemporaryDirectory() as tempdir:
                     UpperCamelCase_ 							=	"\n".join(alphabet.all_toks	)
                     UpperCamelCase_ 							=	Path(UpperCamelCase_	) / "vocab.txt"
                     vocab_file.write_text(UpperCamelCase_	)
                     UpperCamelCase_ 							=	EsmTokenizer(vocab_file=str(UpperCamelCase_	)	)

              UpperCamelCase_ 							=	hf_tokenizer([row[1] for row in sample_data]       ,		return_tensors="pt"       ,		padding=UpperCamelCase_	)
              UpperCamelCase_ 							=	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.
                     UpperCamelCase_ 							=	esm.cuda().infer([row[1] for row in sample_data]	)
                     UpperCamelCase_ 							=	model.cuda()(
                         input_ids=hf_tokens["input_ids"].cuda()       ,		attention_mask=hf_tokens["attention_mask"].cuda()	)
              else:
                     UpperCamelCase_ 							=	model(**UpperCamelCase_       ,		output_hidden_states=UpperCamelCase_	)
                     UpperCamelCase_ 							=	our_output["logits"]
                     if classification_head:
                            UpperCamelCase_ 							=	esm.model.classification_heads["mnli"](esm.extract_features(UpperCamelCase_	)	)
                     else:
                            UpperCamelCase_ 							=	esm(hf_tokens["input_ids"]       ,		repr_layers=list(range(999	)	)	)
                            UpperCamelCase_ 							=	their_output["logits"]

              if is_folding_model:
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output["positions"] - their_output["positions"]	)	).item()
                     UpperCamelCase_ 							=	torch.allclose(our_output["positions"]       ,		their_output["positions"]       ,		atol=1e-5	)
              else:
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output - their_output	)	).item()
                     UpperCamelCase_ 							=	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
                     UpperCamelCase_ 							=	model.predict_contacts(hf_tokens["input_ids"]       ,		hf_tokens["attention_mask"]	)
                     UpperCamelCase_ 							=	esm.predict_contacts(hf_tokens["input_ids"]	)
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output - their_output	)	).item()
                     UpperCamelCase_ 							=	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__":
     _UpperCAmelCase										=					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.')
     _UpperCAmelCase										=					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
     )

