





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     A__					(						)     ->     int:






   """simple docstring"""







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






   """simple docstring"""







   _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     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			bool							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Optional[Any]:






   """simple docstring"""







   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(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	) and key != "trunk":
            setattr(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      for key, val in esm.cfg.trunk.items():
         if hasattr(esmfold_config.trunk							,  SCREAMING_SNAKE_CASE_	) and key != "structure_module":
            setattr(esmfold_config.trunk							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      for key, val in esm.cfg.trunk.structure_module.items():
         if hasattr(esmfold_config.trunk.structure_module							,  SCREAMING_SNAKE_CASE_	):
            setattr(esmfold_config.trunk.structure_module							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif hasattr(SCREAMING_SNAKE_CASE_							,  '''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=SCREAMING_SNAKE_CASE_							,  hidden_size=SCREAMING_SNAKE_CASE_							,  num_hidden_layers=SCREAMING_SNAKE_CASE_							,  num_attention_heads=SCREAMING_SNAKE_CASE_							,  intermediate_size=SCREAMING_SNAKE_CASE_							,  max_position_embeddings=10_26							,  layer_norm_eps=1E-5							,  attention_probs_dropout_prob=0.0							,  hidden_dropout_prob=0.0							,  pad_token_id=SCREAMING_SNAKE_CASE_							,  emb_layer_norm_before=SCREAMING_SNAKE_CASE_							,  token_dropout=SCREAMING_SNAKE_CASE_							,  position_embedding_type=SCREAMING_SNAKE_CASE_							,  is_folding_model=SCREAMING_SNAKE_CASE_							,  esmfold_config=SCREAMING_SNAKE_CASE_							,  vocab_list=SCREAMING_SNAKE_CASE_							,  )
   if classification_head:
      _UpperCAmelCase											=      esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
   print('''Our ESM config:'''							,  SCREAMING_SNAKE_CASE_	)

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

      _UpperCAmelCase											=      hf_tokenizer([row[1] for row in sample_data]							,  return_tensors='''pt'''							,  padding=SCREAMING_SNAKE_CASE_	)
      _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(**SCREAMING_SNAKE_CASE_							,  output_hidden_states=SCREAMING_SNAKE_CASE_	)
         _UpperCAmelCase											=      our_output['''logits''']
         if classification_head:
            _UpperCAmelCase											=      esm.model.classification_heads['''mnli'''](esm.extract_features(SCREAMING_SNAKE_CASE_	)	)
         else:
            _UpperCAmelCase											=      esm(hf_tokens['''input_ids''']							,  repr_layers=list(range(9_99	)	)	)
            _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(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  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(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  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(SCREAMING_SNAKE_CASE_	).mkdir(parents=SCREAMING_SNAKE_CASE_							,  exist_ok=SCREAMING_SNAKE_CASE_	)
      print(F'''Saving model to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(SCREAMING_SNAKE_CASE_	)

      del esm  # Free up some memory before continuing

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

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


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
   )






def     A__					(						SCREAMING_SNAKE_CASE_      :			List[Any]							,  SCREAMING_SNAKE_CASE_      :			Dict	)     ->     Any:






   """simple docstring"""







   if n == 0:
      return 1

   elif n % 2 == 1:
      return binary_exponentiation(SCREAMING_SNAKE_CASE_							,  n - 1	) * a

   else:
      _UpperCAmelCase											=      binary_exponentiation(SCREAMING_SNAKE_CASE_							,  n / 2	)
      return b * b


if __name__ == "__main__":
   try:
      UpperCAmelCase_											=  int(input("Enter Base : ").strip())
      UpperCAmelCase_											=  int(input("Enter Power : ").strip())
   except ValueError:
      print("Invalid literal for integer")

   UpperCAmelCase_											=  binary_exponentiation(BASE, POWER)
   print(f'''{BASE}^({POWER}) : {RESULT}''')