






'''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()
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 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   with TemporaryDirectory() as tempdir:
      lowercase_		:      Optional[int]          =       "\n".join(SCREAMING_SNAKE_CASE_							)
      lowercase_		:      Dict          =       Path(SCREAMING_SNAKE_CASE_							) / "vocab.txt"
      vocab_file.write_text(SCREAMING_SNAKE_CASE_							)
      lowercase_		:      Optional[int]          =       EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE_							)							)
   lowercase_		:      Optional[int]          =       0  # Overlaps with 'A' but that seems to be what they want
   return hf_tokenizer




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Optional[int]          =       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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   if model.startswith("esmfold"							):
      lowercase_		:      List[Any]          =       MODEL_MAPPING[model]()
   else:
      lowercase_      ,						lowercase_		:      Optional[int]          =       MODEL_MAPPING[model]()
   esm.eval()  # disable dropout

   if model.startswith("esmfold"							):
      lowercase_		:      Optional[int]          =       esm.esm.embed_dim
      lowercase_		:      int          =       esm.esm.num_layers
      lowercase_		:      List[str]          =       esm.esm.attention_heads
      lowercase_		:      Any          =       4 * embed_dim
      lowercase_		:      Optional[Any]          =       esm.esm.token_dropout
      lowercase_		:      Any          =       False  # This code path does not exist in ESM-2
      lowercase_		:      str          =       "rotary"
      lowercase_		:      Optional[Any]          =       True
      lowercase_		:      Union[str, Any]          =       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
      lowercase_		:      Optional[Any]          =       esm.args.embed_dim
      lowercase_		:      Any          =       esm.args.layers
      lowercase_		:      Dict          =       esm.args.attention_heads
      lowercase_		:      Dict          =       esm.args.ffn_embed_dim
      lowercase_		:      List[str]          =       esm.args.token_dropout
      lowercase_		:      Union[str, Any]          =       True if esm.emb_layer_norm_before else False
      lowercase_		:      Tuple          =       "absolute"
      lowercase_		:      Dict          =       False
      lowercase_		:      Tuple          =       None
   else:
      # Indicates an ESM-2 model
      lowercase_		:      Union[str, Any]          =       esm.embed_dim
      lowercase_		:      Union[str, Any]          =       esm.num_layers
      lowercase_		:      int          =       esm.attention_heads
      lowercase_		:      Union[str, Any]          =       4 * embed_dim  # This is hardcoded in ESM-2
      lowercase_		:      int          =       esm.token_dropout
      lowercase_		:      Optional[int]          =       False  # This code path does not exist in ESM-2
      lowercase_		:      Tuple          =       "rotary"
      lowercase_		:      Any          =       False
      lowercase_		:      Tuple          =       None

   if is_folding_model:
      lowercase_		:      List[Any]          =       esm.esm.alphabet
   lowercase_		:      List[str]          =       tuple(alphabet.all_toks							)
   lowercase_		:      Tuple          =       alphabet.mask_idx
   lowercase_		:      Dict          =       alphabet.padding_idx

   if is_folding_model:
      lowercase_		:      List[str]          =       esm.esm
   else:
      lowercase_		:      Optional[int]          =       esm

   lowercase_		:      List[str]          =       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=1026  ,						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:
      lowercase_		:      int          =       esm.classification_heads["mnli"].out_proj.weight.shape[0]
   print("Our ESM config:"  ,						SCREAMING_SNAKE_CASE_							)

   if model.startswith("esmfold"							):
      lowercase_		:      str          =       EsmForProteinFolding
   elif classification_head:
      lowercase_		:      Optional[int]          =       EsmForSequenceClassification
   else:
      lowercase_		:      Union[str, Any]          =       EsmForMaskedLM
   lowercase_		:      List[str]          =       model_class(SCREAMING_SNAKE_CASE_							)
   model.eval()

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

   if config.emb_layer_norm_before:
      lowercase_		:      Tuple          =       original_esm_model.emb_layer_norm_before.weight
      lowercase_		:      Tuple          =       original_esm_model.emb_layer_norm_before.bias

   lowercase_		:      Optional[int]          =       original_esm_model.emb_layer_norm_after.weight
   lowercase_		:      Optional[Any]          =       original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      lowercase_		:      Any          =       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)							)
      )

      lowercase_		:      int          =       esm_layer.self_attn.q_proj.weight
      lowercase_		:      int          =       esm_layer.self_attn.q_proj.bias
      lowercase_		:      Tuple          =       esm_layer.self_attn.k_proj.weight
      lowercase_		:      Optional[Any]          =       esm_layer.self_attn.k_proj.bias
      lowercase_		:      Any          =       esm_layer.self_attn.v_proj.weight
      lowercase_		:      Optional[int]          =       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.
         lowercase_		:      Optional[int]          =       esm_layer.self_attn.rot_emb.inv_freq

      # LayerNorm changes for pre-activation
      lowercase_		:      List[Any]          =       esm_layer.self_attn_layer_norm.weight
      lowercase_		:      Optional[Any]          =       esm_layer.self_attn_layer_norm.bias
      lowercase_		:      Dict          =       esm_layer.final_layer_norm.weight
      lowercase_		:      List[Any]          =       esm_layer.final_layer_norm.bias

      # self-attention output
      lowercase_		:      List[Any]          =       layer.attention.output
      assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
      lowercase_		:      Any          =       esm_layer.self_attn.out_proj.weight
      lowercase_		:      int          =       esm_layer.self_attn.out_proj.bias

      # intermediate
      lowercase_		:      Optional[Any]          =       layer.intermediate
      assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
      lowercase_		:      str          =       esm_layer.fca.weight
      lowercase_		:      Union[str, Any]          =       esm_layer.fca.bias

      # output
      lowercase_		:      Optional[Any]          =       layer.output
      assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
      lowercase_		:      Optional[Any]          =       esm_layer.fca.weight
      lowercase_		:      Optional[Any]          =       esm_layer.fca.bias
      # end of layer

   if is_folding_model:
      lowercase_		:      Optional[int]          =       esm.esm_s_combine.data
      lowercase_		:      Any          =       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:
      lowercase_		:      Any          =       esm.esm.classification_heads["mnli"].dense.weight
      lowercase_		:      Tuple          =       esm.classification_heads["mnli"].dense.bias
      lowercase_		:      Dict          =       esm.classification_heads["mnli"].out_proj.weight
      lowercase_		:      int          =       esm.classification_heads["mnli"].out_proj.bias
   else:
      # LM Head
      lowercase_		:      Union[str, Any]          =       esm.lm_head.dense.weight
      lowercase_		:      Union[str, Any]          =       esm.lm_head.dense.bias
      lowercase_		:      Optional[int]          =       esm.lm_head.layer_norm.weight
      lowercase_		:      Optional[int]          =       esm.lm_head.layer_norm.bias
      lowercase_		:      int          =       esm.lm_head.weight
      lowercase_		:      Optional[Any]          =       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.
      lowercase_		:      Union[str, Any]          =       SAMPLE_DATA[:2]
   else:
      lowercase_		:      str          =       SAMPLE_DATA

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

      lowercase_		:      Optional[Any]          =       hf_tokenizer([row[1] for row in sample_data]  ,						return_tensors="pt"  ,						padding=SCREAMING_SNAKE_CASE_							)
      lowercase_		:      List[Any]          =       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.
         lowercase_		:      List[Any]          =       esm.cuda().infer([row[1] for row in sample_data]							)
         lowercase_		:      Any          =       model.cuda()(
             input_ids=hf_tokens["input_ids"].cuda()  ,						attention_mask=hf_tokens["attention_mask"].cuda()							)
      else:
         lowercase_		:      Tuple          =       model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_							)
         lowercase_		:      Dict          =       our_output["logits"]
         if classification_head:
            lowercase_		:      List[str]          =       esm.model.classification_heads["mnli"](esm.extract_features(SCREAMING_SNAKE_CASE_							)							)
         else:
            lowercase_		:      Union[str, Any]          =       esm(hf_tokens["input_ids"]  ,						repr_layers=list(range(999							)							)							)
            lowercase_		:      int          =       their_output["logits"]

      if is_folding_model:
         lowercase_		:      Any          =       torch.max(torch.abs(our_output["positions"] - their_output["positions"]							)							).item()
         lowercase_		:      Optional[int]          =       torch.allclose(our_output["positions"]  ,						their_output["positions"]  ,						atol=1e-5							)
      else:
         lowercase_		:      List[str]          =       torch.max(torch.abs(our_output - their_output							)							).item()
         lowercase_		:      Tuple          =       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
         lowercase_		:      int          =       model.predict_contacts(hf_tokens["input_ids"]  ,						hf_tokens["attention_mask"]							)
         lowercase_		:      Optional[int]          =       esm.predict_contacts(hf_tokens["input_ids"]							)
         lowercase_		:      int          =       torch.max(torch.abs(our_output - their_output							)							).item()
         lowercase_		:      List[Any]          =       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
 )












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




