

import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_UpperCamelCase			      =		logging.get_logger(__name__)







class   __lowercase				(_UpperCAmelCase			):
 def __init__(							self				,							*A_				,							**A_ ) ->None:



       '''simple docstring'''





       warnings.warn(
           '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use LayoutLMv3ImageProcessor instead.'''				,							A_				,							)
       super().__init__(*A_				,							**A_ )





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 		_lowercase			(					):
      with TemporaryDirectory() as tempdir:
            __lowerCAmelCase :						Optional[Any] = '''\n'''.join(lowercase__						)
            __lowerCAmelCase :						str = Path(lowercase__						) / '''vocab.txt'''
            vocab_file.write_text(lowercase__						)
            __lowerCAmelCase :						int = EsmTokenizer(vocab_file=str(lowercase__						)						)
      __lowerCAmelCase :						Union[str, Any] = 0  # Overlaps with 'A' but that seems to be what they want
      return hf_tokenizer







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Tuple = MODEL_MAPPING[model]()
      else:
            __lowerCAmelCase,							__lowerCAmelCase :						str = MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						int = esm.esm.embed_dim
            __lowerCAmelCase :						Optional[Any] = esm.esm.num_layers
            __lowerCAmelCase :						Optional[Any] = esm.esm.attention_heads
            __lowerCAmelCase :						Dict = 4 * embed_dim
            __lowerCAmelCase :						Dict = esm.esm.token_dropout
            __lowerCAmelCase :						int = False  # This code path does not exist in ESM-2
            __lowerCAmelCase :						str = '''rotary'''
            __lowerCAmelCase :						List[Any] = True
            __lowerCAmelCase :						List[str] = EsmFoldConfig()
            for key, val in esm.cfg.items():
                  if hasattr(lowercase__       ,     lowercase__						) and key != "trunk":
                        setattr(lowercase__       ,     lowercase__       ,     lowercase__						)
            for key, val in esm.cfg.trunk.items():
                  if hasattr(esmfold_config.trunk       ,     lowercase__						) and key != "structure_module":
                        setattr(esmfold_config.trunk       ,     lowercase__       ,     lowercase__						)
            for key, val in esm.cfg.trunk.structure_module.items():
                  if hasattr(esmfold_config.trunk.structure_module       ,     lowercase__						):
                        setattr(esmfold_config.trunk.structure_module       ,     lowercase__       ,     lowercase__						)
      elif hasattr(lowercase__       ,     '''args'''						):
            # Indicates an ESM-1b or ESM-1v model
            __lowerCAmelCase :						Dict = esm.args.embed_dim
            __lowerCAmelCase :						List[str] = esm.args.layers
            __lowerCAmelCase :						Tuple = esm.args.attention_heads
            __lowerCAmelCase :						List[Any] = esm.args.ffn_embed_dim
            __lowerCAmelCase :						str = esm.args.token_dropout
            __lowerCAmelCase :						Any = True if esm.emb_layer_norm_before else False
            __lowerCAmelCase :						Optional[Any] = '''absolute'''
            __lowerCAmelCase :						Any = False
            __lowerCAmelCase :						Tuple = None
      else:
            # Indicates an ESM-2 model
            __lowerCAmelCase :						Optional[int] = esm.embed_dim
            __lowerCAmelCase :						int = esm.num_layers
            __lowerCAmelCase :						Any = esm.attention_heads
            __lowerCAmelCase :						Dict = 4 * embed_dim  # This is hardcoded in ESM-2
            __lowerCAmelCase :						Optional[Any] = esm.token_dropout
            __lowerCAmelCase :						Optional[Any] = False  # This code path does not exist in ESM-2
            __lowerCAmelCase :						Optional[Any] = '''rotary'''
            __lowerCAmelCase :						List[Any] = False
            __lowerCAmelCase :						str = None

      if is_folding_model:
            __lowerCAmelCase :						Dict = esm.esm.alphabet
      __lowerCAmelCase :						str = tuple(alphabet.all_toks						)
      __lowerCAmelCase :						Tuple = alphabet.mask_idx
      __lowerCAmelCase :						Optional[int] = alphabet.padding_idx

      if is_folding_model:
            __lowerCAmelCase :						List[Any] = esm.esm
      else:
            __lowerCAmelCase :						Tuple = esm

      __lowerCAmelCase :						Optional[int] = EsmConfig(
          vocab_size=original_esm_model.embed_tokens.num_embeddings       ,     mask_token_id=lowercase__       ,     hidden_size=lowercase__       ,     num_hidden_layers=lowercase__       ,     num_attention_heads=lowercase__       ,     intermediate_size=lowercase__       ,     max_position_embeddings=1_0_2_6       ,     layer_norm_eps=1E-5       ,     attention_probs_dropout_prob=0.0       ,     hidden_dropout_prob=0.0       ,     pad_token_id=lowercase__       ,     emb_layer_norm_before=lowercase__       ,     token_dropout=lowercase__       ,     position_embedding_type=lowercase__       ,     is_folding_model=lowercase__       ,     esmfold_config=lowercase__       ,     vocab_list=lowercase__       ,     )
      if classification_head:
            __lowerCAmelCase :						str = esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print('''Our ESM config:'''       ,     lowercase__						)

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Any = EsmForProteinFolding
      elif classification_head:
            __lowerCAmelCase :						Union[str, Any] = EsmForSequenceClassification
      else:
            __lowerCAmelCase :						Any = EsmForMaskedLM
      __lowerCAmelCase :						str = model_class(lowercase__						)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      __lowerCAmelCase :						Union[str, Any] = original_esm_model.embed_tokens.weight
      if position_embedding_type == "absolute":
            __lowerCAmelCase :						Union[str, Any] = original_esm_model.embed_positions.weight

      if config.emb_layer_norm_before:
            __lowerCAmelCase :						Optional[int] = original_esm_model.emb_layer_norm_before.weight
            __lowerCAmelCase :						int = original_esm_model.emb_layer_norm_before.bias

      __lowerCAmelCase :						Union[str, Any] = original_esm_model.emb_layer_norm_after.weight
      __lowerCAmelCase :						Any = original_esm_model.emb_layer_norm_after.bias

      for i in range(config.num_hidden_layers						):
            # Encoder: start of layer
            __lowerCAmelCase :						EsmLayer = model.esm.encoder.layer[i]
            # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
            __lowerCAmelCase :						Union[str, Any] = original_esm_model.layers[i]

            # self attention
            __lowerCAmelCase :						EsmSelfAttention = 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)						)
            )

            __lowerCAmelCase :						str = esm_layer.self_attn.q_proj.weight
            __lowerCAmelCase :						Dict = esm_layer.self_attn.q_proj.bias
            __lowerCAmelCase :						Dict = esm_layer.self_attn.k_proj.weight
            __lowerCAmelCase :						Optional[Any] = esm_layer.self_attn.k_proj.bias
            __lowerCAmelCase :						Tuple = esm_layer.self_attn.v_proj.weight
            __lowerCAmelCase :						str = esm_layer.self_attn.v_proj.bias

            if getattr(esm_layer.self_attn       ,     '''rot_emb'''       ,     lowercase__						) 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.
                  __lowerCAmelCase :						str = esm_layer.self_attn.rot_emb.inv_freq

            # LayerNorm changes for pre-activation
            __lowerCAmelCase :						Union[str, Any] = esm_layer.self_attn_layer_norm.weight
            __lowerCAmelCase :						List[Any] = esm_layer.self_attn_layer_norm.bias
            __lowerCAmelCase :						List[Any] = esm_layer.final_layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm_layer.final_layer_norm.bias

            # self-attention output
            __lowerCAmelCase :						EsmSelfOutput = layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            __lowerCAmelCase :						str = esm_layer.self_attn.out_proj.weight
            __lowerCAmelCase :						List[str] = esm_layer.self_attn.out_proj.bias

            # intermediate
            __lowerCAmelCase :						EsmIntermediate = layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						str = esm_layer.fca.weight
            __lowerCAmelCase :						int = esm_layer.fca.bias

            # output
            __lowerCAmelCase :						EsmOutput = layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						Tuple = esm_layer.fca.weight
            __lowerCAmelCase :						List[Any] = esm_layer.fca.bias
            # end of layer

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

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

            __lowerCAmelCase :						Any = hf_tokenizer([row[1] for row in sample_data]       ,     return_tensors='''pt'''       ,     padding=lowercase__						)
            __lowerCAmelCase :						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.
                  __lowerCAmelCase :						int = esm.cuda().infer([row[1] for row in sample_data]						)
                  __lowerCAmelCase :						Union[str, Any] = model.cuda()(
                      input_ids=hf_tokens['''input_ids'''].cuda()       ,     attention_mask=hf_tokens['''attention_mask'''].cuda()						)
            else:
                  __lowerCAmelCase :						List[Any] = model(**lowercase__       ,     output_hidden_states=lowercase__						)
                  __lowerCAmelCase :						Optional[int] = our_output['''logits''']
                  if classification_head:
                        __lowerCAmelCase :						Optional[int] = esm.model.classification_heads['''mnli'''](esm.extract_features(lowercase__						)						)
                  else:
                        __lowerCAmelCase :						Any = esm(hf_tokens['''input_ids''']       ,     repr_layers=list(range(9_9_9						)						)						)
                        __lowerCAmelCase :						Optional[Any] = their_output['''logits''']

            if is_folding_model:
                  __lowerCAmelCase :						Dict = torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']						)						).item()
                  __lowerCAmelCase :						Dict = torch.allclose(our_output['''positions''']       ,     their_output['''positions''']       ,     atol=1E-5						)
            else:
                  __lowerCAmelCase :						Optional[Any] = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     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
                  __lowerCAmelCase :						Tuple = model.predict_contacts(hf_tokens['''input_ids''']       ,     hf_tokens['''attention_mask''']						)
                  __lowerCAmelCase :						Optional[int] = esm.predict_contacts(hf_tokens['''input_ids''']						)
                  __lowerCAmelCase :						str = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     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(lowercase__						).mkdir(parents=lowercase__       ,     exist_ok=lowercase__						)
            print(f"""Saving model to {pytorch_dump_folder_path}"""						)
            model.save_pretrained(lowercase__						)

            del esm  # Free up some memory before continuing

      print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""						)
      hf_tokenizer.save_pretrained(lowercase__						)

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


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
 )


