






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()
a	: List[Any]      =				logging.get_logger(__name__)

a	: Dict      =				[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

a	: str      =				{
    "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,
}

a	: List[str]      =				list("ARNDCQEGHILKMFPSTWYV")

a	: Tuple      =				restypes + ["X"]
a	: List[Any]      =				restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]






def        lowerCamelCase__   (					):


  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        lowerCamelCase__   (					__lowerCamelCase    :  int						,	__lowerCamelCase    :  List[str]	):
  __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        lowerCamelCase__   (					__lowerCamelCase    :  Tuple						,	__lowerCamelCase    :  List[str]						,	__lowerCamelCase    :  Tuple						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  Dict	):


  if model.startswith("""esmfold"""	):
    __UpperCAmelCase     =						MODEL_MAPPING[model]()
  else:
    __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=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:
    __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     =						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     =						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(999	)	)	)
        __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__":
       a	: List[Any]      =				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.")
       a	: Any      =				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
       )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


