code: 
  
  
  """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()
  lowerCamelCase_     :     Union[str, Any]    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						[
      (
          """protein1""",
          """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
      ),
      ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
      ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
      ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
  ]
  
  lowerCamelCase_     :     List[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,
  }
  
  lowerCamelCase_     :     Optional[int]    		=						list("""ARNDCQEGHILKMFPSTWYV""")
  
  lowerCamelCase_     :     Any    		=						restypes + ["""X"""]
  lowerCamelCase_     :     Tuple    		=						restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      with TemporaryDirectory() as tempdir:
          a					="\n".join(UpperCamelCase__	)
          a					=Path(UpperCamelCase__	) / "vocab.txt"
          vocab_file.write_text(UpperCamelCase__	)
          a					=EsmTokenizer(vocab_file=str(UpperCamelCase__	)	)
      a					=0  # Overlaps with 'A' but that seems to be what they want
      return hf_tokenizer
  def _A		(		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 _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if model.startswith('''esmfold'''	):
          a					=MODEL_MAPPING[model]()
      else:
          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(UpperCamelCase__ ,					UpperCamelCase__	) and key != "trunk":
                  setattr(UpperCamelCase__ ,					UpperCamelCase__ ,					UpperCamelCase__	)
          for key, val in esm.cfg.trunk.items():
              if hasattr(esmfold_config.trunk ,					UpperCamelCase__	) and key != "structure_module":
                  setattr(esmfold_config.trunk ,					UpperCamelCase__ ,					UpperCamelCase__	)
          for key, val in esm.cfg.trunk.structure_module.items():
              if hasattr(esmfold_config.trunk.structure_module ,					UpperCamelCase__	):
                  setattr(esmfold_config.trunk.structure_module ,					UpperCamelCase__ ,					UpperCamelCase__	)
      elif hasattr(UpperCamelCase__ ,					'''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=UpperCamelCase__ ,					hidden_size=UpperCamelCase__ ,					num_hidden_layers=UpperCamelCase__ ,					num_attention_heads=UpperCamelCase__ ,					intermediate_size=UpperCamelCase__ ,					max_position_embeddings=10_26 ,					layer_norm_eps=1E-5 ,					attention_probs_dropout_prob=0.0 ,					hidden_dropout_prob=0.0 ,					pad_token_id=UpperCamelCase__ ,					emb_layer_norm_before=UpperCamelCase__ ,					token_dropout=UpperCamelCase__ ,					position_embedding_type=UpperCamelCase__ ,					is_folding_model=UpperCamelCase__ ,					esmfold_config=UpperCamelCase__ ,					vocab_list=UpperCamelCase__ ,					)
      if classification_head:
          a					=esm.classification_heads["mnli"].out_proj.weight.shape[0]
      print('''Our ESM config:''' ,					UpperCamelCase__	)
  
      if model.startswith('''esmfold'''	):
          a					=EsmForProteinFolding
      elif classification_head:
          a					=EsmForSequenceClassification
      else:
          a					=EsmForMaskedLM
      a					=model_class(UpperCamelCase__	)
      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''' ,					UpperCamelCase__	) 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=UpperCamelCase__ ,					add_special_tokens=UpperCamelCase__	)
          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					=batch_converter(UpperCamelCase__	)
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
              a					="\n".join(alphabet.all_toks	)
              a					=Path(UpperCamelCase__	) / "vocab.txt"
              vocab_file.write_text(UpperCamelCase__	)
              a					=EsmTokenizer(vocab_file=str(UpperCamelCase__	)	)
  
          a					=hf_tokenizer([row[1] for row in sample_data] ,					return_tensors='''pt''' ,					padding=UpperCamelCase__	)
          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(**UpperCamelCase__ ,					output_hidden_states=UpperCamelCase__	)
              a					=our_output["logits"]
              if classification_head:
                  a					=esm.model.classification_heads["mnli"](esm.extract_features(UpperCamelCase__	)	)
              else:
                  a					=esm(hf_tokens['''input_ids'''] ,					repr_layers=list(range(9_99	)	)	)
                  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(UpperCamelCase__ ,					UpperCamelCase__ ,					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(UpperCamelCase__ ,					UpperCamelCase__ ,					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(UpperCamelCase__	).mkdir(parents=UpperCamelCase__ ,					exist_ok=UpperCamelCase__	)
          print(f'''Saving model to {pytorch_dump_folder_path}'''	)
          model.save_pretrained(UpperCamelCase__	)
  
          del esm  # Free up some memory before continuing
  
      print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''	)
      hf_tokenizer.save_pretrained(UpperCamelCase__	)
  
      if push_to_repo:
          model.push_to_hub(repo_id=UpperCamelCase__ ,					use_auth_token=UpperCamelCase__	)
          hf_tokenizer.push_to_hub(repo_id=UpperCamelCase__ ,					use_auth_token=UpperCamelCase__	)
  
  
  if __name__ == "__main__":
        lowerCamelCase_     :     Union[str, 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.""")
        lowerCamelCase_     :     Optional[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
        )
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from math import gcd
  def _A		(		lowercase = 1_20_00	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=0
      for d in range(max_d + 1	):
          for n in range(d // 3 + 1 ,					(d + 1) // 2	):
              if gcd(lowercase ,					lowercase	) == 1:
                  fractions_number += 1
      return fractions_number
  
  
  if __name__ == "__main__":
         print(F'{solution() = }')