


"""simple docstring"""






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()
__snake_case												=							logging.get_logger(__name__)

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

__snake_case												=							{
    '''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,
}

__snake_case												=							list('''ARNDCQEGHILKMFPSTWYV''')

__snake_case												=							restypes + ['''X''']
__snake_case												=							restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']
def    A_ ( ):






     """simple docstring"""

     with TemporaryDirectory() as tempdir:
          _a     						=					'''\n'''.join(_lowerCAmelCase							)
          _a     						=					Path(_lowerCAmelCase							) / '''vocab.txt'''
          vocab_file.write_text(_lowerCAmelCase							)
          _a     						=					EsmTokenizer(vocab_file=str(_lowerCAmelCase							)							)
     _a     						=					0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer
def    A_ ( _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Dict							):






     """simple docstring"""

     _a     						=					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    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str,  _lowerCAmelCase  :       bool,  _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if model.startswith('''esmfold'''							):
          _a     						=					MODEL_MAPPING[model]()
     else:
          _a						,   _a     						=					MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith('''esmfold'''							):
          _a     						=					esm.esm.embed_dim
          _a     						=					esm.esm.num_layers
          _a     						=					esm.esm.attention_heads
          _a     						=					4 * embed_dim
          _a     						=					esm.esm.token_dropout
          _a     						=					False  # This code path does not exist in ESM-2
          _a     						=					'''rotary'''
          _a     						=					True
          _a     						=					EsmFoldConfig()
          for key, val in esm.cfg.items():
               if hasattr(_lowerCAmelCase,  _lowerCAmelCase							) and key != "trunk":
                    setattr(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk,  _lowerCAmelCase							) and key != "structure_module":
                    setattr(esmfold_config.trunk,  _lowerCAmelCase,  _lowerCAmelCase							)
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module,  _lowerCAmelCase							):
                    setattr(esmfold_config.trunk.structure_module,  _lowerCAmelCase,  _lowerCAmelCase							)
     elif hasattr(_lowerCAmelCase,  '''args'''							):
          # Indicates an ESM-1b or ESM-1v model
          _a     						=					esm.args.embed_dim
          _a     						=					esm.args.layers
          _a     						=					esm.args.attention_heads
          _a     						=					esm.args.ffn_embed_dim
          _a     						=					esm.args.token_dropout
          _a     						=					True if esm.emb_layer_norm_before else False
          _a     						=					'''absolute'''
          _a     						=					False
          _a     						=					None
     else:
          # Indicates an ESM-2 model
          _a     						=					esm.embed_dim
          _a     						=					esm.num_layers
          _a     						=					esm.attention_heads
          _a     						=					4 * embed_dim  # This is hardcoded in ESM-2
          _a     						=					esm.token_dropout
          _a     						=					False  # This code path does not exist in ESM-2
          _a     						=					'''rotary'''
          _a     						=					False
          _a     						=					None

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

     if is_folding_model:
          _a     						=					esm.esm
     else:
          _a     						=					esm

     _a     						=					EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings,  mask_token_id=_lowerCAmelCase,  hidden_size=_lowerCAmelCase,  num_hidden_layers=_lowerCAmelCase,  num_attention_heads=_lowerCAmelCase,  intermediate_size=_lowerCAmelCase,  max_position_embeddings=10_26,  layer_norm_eps=1e-5,  attention_probs_dropout_prob=0.0,  hidden_dropout_prob=0.0,  pad_token_id=_lowerCAmelCase,  emb_layer_norm_before=_lowerCAmelCase,  token_dropout=_lowerCAmelCase,  position_embedding_type=_lowerCAmelCase,  is_folding_model=_lowerCAmelCase,  esmfold_config=_lowerCAmelCase,  vocab_list=_lowerCAmelCase,  )
     if classification_head:
          _a     						=					esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our ESM config:''',  _lowerCAmelCase							)

     if model.startswith('''esmfold'''							):
          _a     						=					EsmForProteinFolding
     elif classification_head:
          _a     						=					EsmForSequenceClassification
     else:
          _a     						=					EsmForMaskedLM
     _a     						=					model_class(_lowerCAmelCase							)
     model.eval()

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

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

     _a     						=					original_esm_model.emb_layer_norm_after.weight
     _a     						=					original_esm_model.emb_layer_norm_after.bias

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

          # self attention
          _a     						=					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     						=					esm_layer.self_attn.q_proj.weight
          _a     						=					esm_layer.self_attn.q_proj.bias
          _a     						=					esm_layer.self_attn.k_proj.weight
          _a     						=					esm_layer.self_attn.k_proj.bias
          _a     						=					esm_layer.self_attn.v_proj.weight
          _a     						=					esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn,  '''rot_emb''',  _lowerCAmelCase							) 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     						=					esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

          _a     						=					hf_tokenizer([row[1] for row in sample_data],  return_tensors='''pt''',  padding=_lowerCAmelCase							)
          _a     						=					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     						=					esm.cuda().infer([row[1] for row in sample_data]							)
               _a     						=					model.cuda()(
                   input_ids=hf_tokens['''input_ids'''].cuda(),  attention_mask=hf_tokens['''attention_mask'''].cuda()							)
          else:
               _a     						=					model(**_lowerCAmelCase,  output_hidden_states=_lowerCAmelCase							)
               _a     						=					our_output['''logits''']
               if classification_head:
                    _a     						=					esm.model.classification_heads['''mnli'''](esm.extract_features(_lowerCAmelCase							)							)
               else:
                    _a     						=					esm(hf_tokens['''input_ids'''],  repr_layers=list(range(9_99							)							)							)
                    _a     						=					their_output['''logits''']

          if is_folding_model:
               _a     						=					torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']							)							).item()
               _a     						=					torch.allclose(our_output['''positions'''],  their_output['''positions'''],  atol=1e-5							)
          else:
               _a     						=					torch.max(torch.abs(our_output - their_output							)							).item()
               _a     						=					torch.allclose(_lowerCAmelCase,  _lowerCAmelCase,  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     						=					model.predict_contacts(hf_tokens['''input_ids'''],  hf_tokens['''attention_mask''']							)
               _a     						=					esm.predict_contacts(hf_tokens['''input_ids''']							)
               _a     						=					torch.max(torch.abs(our_output - their_output							)							).item()
               _a     						=					torch.allclose(_lowerCAmelCase,  _lowerCAmelCase,  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(_lowerCAmelCase							).mkdir(parents=_lowerCAmelCase,  exist_ok=_lowerCAmelCase							)
          print(f'Saving model to {pytorch_dump_folder_path}'							)
          model.save_pretrained(_lowerCAmelCase							)

          del esm  # Free up some memory before continuing

     print(f'Saving tokenizer to {pytorch_dump_folder_path}'							)
     hf_tokenizer.save_pretrained(_lowerCAmelCase							)

     if push_to_repo:
          model.push_to_hub(repo_id=_lowerCAmelCase,  use_auth_token=_lowerCAmelCase							)
          hf_tokenizer.push_to_hub(repo_id=_lowerCAmelCase,  use_auth_token=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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.''')
   __snake_case												=							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
   )



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
   from doctest import testmod

   testmod()