



'''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()
lowercase_			=  logging.get_logger(__name__)

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

lowercase_			=  {
    '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,
}

lowercase_			=  list("ARNDCQEGHILKMFPSTWYV")

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




def lowerCAmelCase  ():



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A):



     """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(__A  ,     __A) and key != "trunk":
                    setattr(__A  ,     __A  ,     __A)
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk  ,     __A) and key != "structure_module":
                    setattr(esmfold_config.trunk  ,     __A  ,     __A)
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module  ,     __A):
                    setattr(esmfold_config.trunk.structure_module  ,     __A  ,     __A)
     elif hasattr(__A  ,     '''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=__A  ,     hidden_size=__A  ,     num_hidden_layers=__A  ,     num_attention_heads=__A  ,     intermediate_size=__A  ,     max_position_embeddings=1_026  ,     layer_norm_eps=1e-5  ,     attention_probs_dropout_prob=0.0  ,     hidden_dropout_prob=0.0  ,     pad_token_id=__A  ,     emb_layer_norm_before=__A  ,     token_dropout=__A  ,     position_embedding_type=__A  ,     is_folding_model=__A  ,     esmfold_config=__A  ,     vocab_list=__A  ,     )
     if classification_head:
          _a     =  esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our ESM config:'''  ,     __A)

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

          _a     =  hf_tokenizer([row[1] for row in sample_data]  ,     return_tensors='''pt'''  ,     padding=__A)
          _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(**__A  ,     output_hidden_states=__A)
               _a     =  our_output['''logits''']
               if classification_head:
                    _a     =  esm.model.classification_heads['''mnli'''](esm.extract_features(__A))
               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(__A  ,     __A  ,     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(__A  ,     __A  ,     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(__A).mkdir(parents=__A  ,     exist_ok=__A)
          print(F'''Saving model to {pytorch_dump_folder_path}''')
          model.save_pretrained(__A)

          del esm  # Free up some memory before continuing

     print(F'''Saving tokenizer to {pytorch_dump_folder_path}''')
     hf_tokenizer.save_pretrained(__A)

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


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





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



