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()
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						[
      (
          """protein1""",
          """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
      ),
      ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
      ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
      ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
  ]
  
  __SCREAMING_SNAKE_CASE					=						{
      """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,
  }
  
  __SCREAMING_SNAKE_CASE					=						list("""ARNDCQEGHILKMFPSTWYV""")
  
  __SCREAMING_SNAKE_CASE					=						restypes + ["""X"""]
  __SCREAMING_SNAKE_CASE					=						restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]
  
  
  
  def 						__a      (  )     ->   Dict:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         with TemporaryDirectory() as tempdir:
                _a          =				"\n".join(a					)
                _a          =				Path(a					) / "vocab.txt"
                vocab_file.write_text(a					)
                _a          =				EsmTokenizer(vocab_file=str(a					)					)
         _a          =				0  # Overlaps with 'A' but that seems to be what they want
         return hf_tokenizer
  
  
  
  def 						__a      (  a,		a					)     ->   Tuple:
  
  
  
  
  
         """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      (  a,		a,		a,		a,		a					)     ->   Tuple:
  
  
  
  
  
         """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(a,		a					) and key != "trunk":
                              setattr(a,		a,		a					)
                for key, val in esm.cfg.trunk.items():
                       if hasattr(esmfold_config.trunk,		a					) and key != "structure_module":
                              setattr(esmfold_config.trunk,		a,		a					)
                for key, val in esm.cfg.trunk.structure_module.items():
                       if hasattr(esmfold_config.trunk.structure_module,		a					):
                              setattr(esmfold_config.trunk.structure_module,		a,		a					)
         elif hasattr(a,		"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=a,		hidden_size=a,		num_hidden_layers=a,		num_attention_heads=a,		intermediate_size=a,		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=a,		emb_layer_norm_before=a,		token_dropout=a,		position_embedding_type=a,		is_folding_model=a,		esmfold_config=a,		vocab_list=a,		)
         if classification_head:
                _a          =				esm.classification_heads["mnli"].out_proj.weight.shape[0]
         print("Our ESM config:",		a					)
  
         if model.startswith("esmfold"					):
                _a          =				EsmForProteinFolding
         elif classification_head:
                _a          =				EsmForSequenceClassification
         else:
                _a          =				EsmForMaskedLM
         _a          =				model_class(a					)
         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",		a					) 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=a,		add_special_tokens=a					)
                _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(a					)
                # Prepare tokenizer and make sure it matches
                with TemporaryDirectory() as tempdir:
                       _a          =				"\n".join(alphabet.all_toks					)
                       _a          =				Path(a					) / "vocab.txt"
                       vocab_file.write_text(a					)
                       _a          =				EsmTokenizer(vocab_file=str(a					)					)
  
                _a          =				hf_tokenizer([row[1] for row in sample_data],		return_tensors="pt",		padding=a					)
                _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(**a,		output_hidden_states=a					)
                       _a          =				our_output["logits"]
                       if classification_head:
                              _a          =				esm.model.classification_heads["mnli"](esm.extract_features(a					)					)
                       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(a,		a,		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(a,		a,		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(a					).mkdir(parents=a,		exist_ok=a					)
                print(F'Saving model to {pytorch_dump_folder_path}'					)
                model.save_pretrained(a					)
  
                del esm  # Free up some memory before continuing
  
         print(F'Saving tokenizer to {pytorch_dump_folder_path}'					)
         hf_tokenizer.save_pretrained(a					)
  
         if push_to_repo:
                model.push_to_hub(repo_id=a,		use_auth_token=a					)
                hf_tokenizer.push_to_hub(repo_id=a,		use_auth_token=a					)
  
  
  if __name__ == "__main__":
     __SCREAMING_SNAKE_CASE					=						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.""")
     __SCREAMING_SNAKE_CASE					=						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"""
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vit import ViTImageProcessor
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Tuple				,  *UpperCamelCase__							:Tuple				,  **UpperCamelCase__							:Any       ):
            warnings.warn(
                "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use ViTImageProcessor instead."				,  UpperCamelCase__				,  )
            super().__init__(*UpperCamelCase__				,  **UpperCamelCase__       )
  