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()
  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__	:      List[Any]				=						{
      """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__	:      Any				=						list("""ARNDCQEGHILKMFPSTWYV""")
  
  A__	:      Union[str, Any]				=						restypes + ["""X"""]
  A__	:      Optional[int]				=						restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]
  
  
  
  def     UpperCAmelCase__      (			)  ->	Union[str, Any]:
       with TemporaryDirectory() as tempdir:
            __lowerCamelCase     :			Tuple              =	'\n'.join(UpperCAmelCase_							)
            __lowerCamelCase     :			Dict              =	Path(UpperCAmelCase_							) / 'vocab.txt'
            vocab_file.write_text(UpperCAmelCase_							)
            __lowerCamelCase     :			Optional[Any]              =	EsmTokenizer(vocab_file=str(UpperCAmelCase_							)							)
       __lowerCamelCase     :			Tuple              =	0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       List[Any]							)  ->	int:
       __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     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str							)  ->	Any:
       if model.startswith('esmfold'							):
            __lowerCamelCase     :			Union[str, Any]              =	MODEL_MAPPING[model]()
       else:
            __lowerCamelCase     :			str              =	MODEL_MAPPING[model]()
       esm.eval()  # disable dropout
  
       if model.startswith('esmfold'							):
            __lowerCamelCase     :			List[Any]              =	esm.esm.embed_dim
            __lowerCamelCase     :			List[str]              =	esm.esm.num_layers
            __lowerCamelCase     :			Optional[Any]              =	esm.esm.attention_heads
            __lowerCamelCase     :			Any              =	4 * embed_dim
            __lowerCamelCase     :			Tuple              =	esm.esm.token_dropout
            __lowerCamelCase     :			Optional[Any]              =	False  # This code path does not exist in ESM-2
            __lowerCamelCase     :			Tuple              =	'rotary'
            __lowerCamelCase     :			List[str]              =	True
            __lowerCamelCase     :			str              =	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
            __lowerCamelCase     :			str              =	esm.args.embed_dim
            __lowerCamelCase     :			Dict              =	esm.args.layers
            __lowerCamelCase     :			str              =	esm.args.attention_heads
            __lowerCamelCase     :			Optional[int]              =	esm.args.ffn_embed_dim
            __lowerCamelCase     :			str              =	esm.args.token_dropout
            __lowerCamelCase     :			List[Any]              =	True if esm.emb_layer_norm_before else False
            __lowerCamelCase     :			Tuple              =	'absolute'
            __lowerCamelCase     :			Optional[Any]              =	False
            __lowerCamelCase     :			Union[str, Any]              =	None
       else:
            # Indicates an ESM-2 model
            __lowerCamelCase     :			int              =	esm.embed_dim
            __lowerCamelCase     :			str              =	esm.num_layers
            __lowerCamelCase     :			Optional[int]              =	esm.attention_heads
            __lowerCamelCase     :			Optional[Any]              =	4 * embed_dim  # This is hardcoded in ESM-2
            __lowerCamelCase     :			List[Any]              =	esm.token_dropout
            __lowerCamelCase     :			str              =	False  # This code path does not exist in ESM-2
            __lowerCamelCase     :			int              =	'rotary'
            __lowerCamelCase     :			str              =	False
            __lowerCamelCase     :			int              =	None
  
       if is_folding_model:
            __lowerCamelCase     :			Optional[Any]              =	esm.esm.alphabet
       __lowerCamelCase     :			Dict              =	tuple(alphabet.all_toks							)
       __lowerCamelCase     :			Optional[int]              =	alphabet.mask_idx
       __lowerCamelCase     :			int              =	alphabet.padding_idx
  
       if is_folding_model:
            __lowerCamelCase     :			Optional[int]              =	esm.esm
       else:
            __lowerCamelCase     :			int              =	esm
  
       __lowerCamelCase     :			Dict              =	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:
            __lowerCamelCase     :			Any              =	esm.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our ESM config:'   ,					UpperCAmelCase_							)
  
       if model.startswith('esmfold'							):
            __lowerCamelCase     :			List[Any]              =	EsmForProteinFolding
       elif classification_head:
            __lowerCamelCase     :			List[Any]              =	EsmForSequenceClassification
       else:
            __lowerCamelCase     :			Dict              =	EsmForMaskedLM
       __lowerCamelCase     :			Union[str, Any]              =	model_class(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[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     :			List[str]              =	original_esm_model.emb_layer_norm_before.weight
            __lowerCamelCase     :			Any              =	original_esm_model.emb_layer_norm_before.bias
  
       __lowerCamelCase     :			int              =	original_esm_model.emb_layer_norm_after.weight
       __lowerCamelCase     :			Dict              =	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     :			int              =	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     :			Optional[int]              =	esm_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Optional[Any]              =	esm_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			str              =	esm_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			int              =	esm_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Dict              =	esm_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			Union[str, Any]              =	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.
                 __lowerCamelCase     :			Union[str, Any]              =	esm_layer.self_attn.rot_emb.inv_freq
  
            # LayerNorm changes for pre-activation
            __lowerCamelCase     :			Dict              =	esm_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			Tuple              =	esm_layer.self_attn_layer_norm.bias
            __lowerCamelCase     :			int              =	esm_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[int]              =	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[Any]              =	esm_layer.self_attn.out_proj.bias
  
            # intermediate
            __lowerCamelCase     :			EsmIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCamelCase     :			Optional[Any]              =	esm_layer.fca.weight
            __lowerCamelCase     :			List[str]              =	esm_layer.fca.bias
  
            # output
            __lowerCamelCase     :			EsmOutput              =	layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	esm_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	esm_layer.fca.bias
            # end of layer
  
       if is_folding_model:
            __lowerCamelCase     :			Union[str, Any]              =	esm.esm_s_combine.data
            __lowerCamelCase     :			Tuple              =	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     :			str              =	esm.esm.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Any              =	esm.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Union[str, Any]              =	esm.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			List[str]              =	esm.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			Optional[Any]              =	esm.lm_head.dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	esm.lm_head.dense.bias
            __lowerCamelCase     :			Optional[int]              =	esm.lm_head.layer_norm.weight
            __lowerCamelCase     :			Any              =	esm.lm_head.layer_norm.bias
            __lowerCamelCase     :			Any              =	esm.lm_head.weight
            __lowerCamelCase     :			List[Any]              =	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     :			Tuple              =	SAMPLE_DATA[:2]
       else:
            __lowerCamelCase     :			Optional[Any]              =	SAMPLE_DATA
  
       if is_folding_model:
            __lowerCamelCase     :			Dict              =	get_esmfold_tokenizer()
            __lowerCamelCase     :			Optional[Any]              =	hf_tokenizer(
                [row[1] for row in sample_data]   ,					return_tensors='pt'   ,					padding=UpperCAmelCase_   ,					add_special_tokens=UpperCAmelCase_							)
            __lowerCamelCase     :			Any              =	esmfold_encode_sequences([row[1] for row in sample_data]							)
            __lowerCamelCase     :			str              =	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     :			Optional[int]              =	batch_converter(UpperCAmelCase_							)
            # Prepare tokenizer and make sure it matches
            with TemporaryDirectory() as tempdir:
                 __lowerCamelCase     :			str              =	'\n'.join(alphabet.all_toks							)
                 __lowerCamelCase     :			List[Any]              =	Path(UpperCAmelCase_							) / 'vocab.txt'
                 vocab_file.write_text(UpperCAmelCase_							)
                 __lowerCamelCase     :			Tuple              =	EsmTokenizer(vocab_file=str(UpperCAmelCase_							)							)
  
            __lowerCamelCase     :			Any              =	hf_tokenizer([row[1] for row in sample_data]   ,					return_tensors='pt'   ,					padding=UpperCAmelCase_							)
            __lowerCamelCase     :			Dict              =	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     :			Optional[int]              =	esm.cuda().infer([row[1] for row in sample_data]							)
                 __lowerCamelCase     :			Tuple              =	model.cuda()(
                     input_ids=hf_tokens['input_ids'].cuda()   ,					attention_mask=hf_tokens['attention_mask'].cuda()							)
            else:
                 __lowerCamelCase     :			Optional[int]              =	model(**UpperCAmelCase_   ,					output_hidden_states=UpperCAmelCase_							)
                 __lowerCamelCase     :			Dict              =	our_output['logits']
                 if classification_head:
                      __lowerCamelCase     :			List[Any]              =	esm.model.classification_heads['mnli'](esm.extract_features(UpperCAmelCase_							)							)
                 else:
                      __lowerCamelCase     :			Union[str, Any]              =	esm(hf_tokens['input_ids']   ,					repr_layers=list(range(9_99							)							)							)
                      __lowerCamelCase     :			str              =	their_output['logits']
  
            if is_folding_model:
                 __lowerCamelCase     :			List[str]              =	torch.max(torch.abs(our_output['positions'] - their_output['positions']							)							).item()
                 __lowerCamelCase     :			Tuple              =	torch.allclose(our_output['positions']   ,					their_output['positions']   ,					atol=1e-5							)
            else:
                 __lowerCamelCase     :			Any              =	torch.max(torch.abs(our_output - their_output							)							).item()
                 __lowerCamelCase     :			List[Any]              =	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
                 __lowerCamelCase     :			List[str]              =	model.predict_contacts(hf_tokens['input_ids']   ,					hf_tokens['attention_mask']							)
                 __lowerCamelCase     :			Tuple              =	esm.predict_contacts(hf_tokens['input_ids']							)
                 __lowerCamelCase     :			Dict              =	torch.max(torch.abs(our_output - their_output							)							).item()
                 __lowerCamelCase     :			Tuple              =	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__":
      A__	:      Tuple				=						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__	:      Optional[int]				=						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'''
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
       __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
       if not bin_string:
            raise ValueError('Empty string was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
       if is_negative:
            __lowerCamelCase     :			List[Any]              =	bin_string[1:]
       if not all(char in '01' for char in bin_string							):
            raise ValueError('Non-binary value was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	0
       for char in bin_string:
            __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
       return -decimal_number if is_negative else decimal_number
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
  
  
  
  
  
  