



'''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 			UpperCAmelCase_							(		):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''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 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							):


     '''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(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
          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=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:
          a_									=esm.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our ESM config:"							,						lowercase__							)

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

          a_									=hf_tokenizer([row[1] for row in sample_data]							,						return_tensors="pt"							,						padding=lowercase__							)
          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(**lowercase__							,						output_hidden_states=lowercase__							)
               a_									=our_output["logits"]
               if classification_head:
                    a_									=esm.model.classification_heads["mnli"](esm.extract_features(lowercase__							)							)
               else:
                    a_									=esm(hf_tokens["input_ids"]							,						repr_layers=list(range(9_9_9							)							)							)
                    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(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
               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(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__":
  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 argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


