




'''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()
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 	_A			(   ):






   '''simple docstring'''







   with TemporaryDirectory() as tempdir:
      A__         =    '\n'.join(UpperCAmelCase	)
      A__         =    Path(UpperCAmelCase	) / 'vocab.txt'
      vocab_file.write_text(UpperCAmelCase	)
      A__         =    EsmTokenizer(vocab_file=str(UpperCAmelCase	)	)
   A__         =    0  # Overlaps with 'A' but that seems to be what they want
   return hf_tokenizer







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''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			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''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(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__         =    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=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__         =    esm.classification_heads['mnli'].out_proj.weight.shape[0]
   print('Our ESM config:'			,UpperCAmelCase	)

   if model.startswith('esmfold'	):
      A__         =    EsmForProteinFolding
   elif classification_head:
      A__         =    EsmForSequenceClassification
   else:
      A__         =    EsmForMaskedLM
   A__         =    model_class(UpperCAmelCase	)
   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'			,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__         =    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=UpperCAmelCase			,add_special_tokens=UpperCAmelCase	)
      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(UpperCAmelCase	)
      # Prepare tokenizer and make sure it matches
      with TemporaryDirectory() as tempdir:
         A__         =    '\n'.join(alphabet.all_toks	)
         A__         =    Path(UpperCAmelCase	) / 'vocab.txt'
         vocab_file.write_text(UpperCAmelCase	)
         A__         =    EsmTokenizer(vocab_file=str(UpperCAmelCase	)	)

      A__         =    hf_tokenizer([row[1] for row in sample_data]			,return_tensors='pt'			,padding=UpperCAmelCase	)
      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(**UpperCAmelCase			,output_hidden_states=UpperCAmelCase	)
         A__         =    our_output['logits']
         if classification_head:
            A__         =    esm.model.classification_heads['mnli'](esm.extract_features(UpperCAmelCase	)	)
         else:
            A__         =    esm(hf_tokens['input_ids']			,repr_layers=list(range(999	)	)	)
            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(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__         =    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(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
    )








'''simple docstring'''







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if not isinstance(UpperCAmelCase			,UpperCAmelCase	) or not all(
       isinstance(UpperCAmelCase			,UpperCAmelCase	) and item >= 0 for item in citations	):
      raise ValueError('The citations should be a list of non negative integers.'	)

   citations.sort()
   A__         =    len(UpperCAmelCase	)

   for i in range(UpperCAmelCase	):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()


