


'''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
    )










'''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()






