






"""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__       :			Optional[int]=						[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

A__       :			Tuple=						{
    "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__       :			Optional[Any]=						list("""ARNDCQEGHILKMFPSTWYV""")

A__       :			Tuple=						restypes + ["X"]
A__       :			Union[str, Any]=						restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]







def 	lowerCAmelCase_(			)       ->						List[str]:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    if model.startswith('esmfold'       ):
        UpperCamelCase__								=     MODEL_MAPPING[model]()
    else:
        UpperCamelCase__							,    UpperCamelCase__								=     MODEL_MAPPING[model]()
    esm.eval()  # disable dropout

    if model.startswith('esmfold'       ):
        UpperCamelCase__								=     esm.esm.embed_dim
        UpperCamelCase__								=     esm.esm.num_layers
        UpperCamelCase__								=     esm.esm.attention_heads
        UpperCamelCase__								=     4 * embed_dim
        UpperCamelCase__								=     esm.esm.token_dropout
        UpperCamelCase__								=     False  # This code path does not exist in ESM-2
        UpperCamelCase__								=     'rotary'
        UpperCamelCase__								=     True
        UpperCamelCase__								=     EsmFoldConfig()
        for key, val in esm.cfg.items():
            if hasattr(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ) and key != "trunk":
                setattr(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        for key, val in esm.cfg.trunk.items():
            if hasattr(esmfold_config.trunk			,						SCREAMING_SNAKE_CASE       ) and key != "structure_module":
                setattr(esmfold_config.trunk			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        for key, val in esm.cfg.trunk.structure_module.items():
            if hasattr(esmfold_config.trunk.structure_module			,						SCREAMING_SNAKE_CASE       ):
                setattr(esmfold_config.trunk.structure_module			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
    elif hasattr(SCREAMING_SNAKE_CASE			,						'args'       ):
        # Indicates an ESM-1b or ESM-1v model
        UpperCamelCase__								=     esm.args.embed_dim
        UpperCamelCase__								=     esm.args.layers
        UpperCamelCase__								=     esm.args.attention_heads
        UpperCamelCase__								=     esm.args.ffn_embed_dim
        UpperCamelCase__								=     esm.args.token_dropout
        UpperCamelCase__								=     True if esm.emb_layer_norm_before else False
        UpperCamelCase__								=     'absolute'
        UpperCamelCase__								=     False
        UpperCamelCase__								=     None
    else:
        # Indicates an ESM-2 model
        UpperCamelCase__								=     esm.embed_dim
        UpperCamelCase__								=     esm.num_layers
        UpperCamelCase__								=     esm.attention_heads
        UpperCamelCase__								=     4 * embed_dim  # This is hardcoded in ESM-2
        UpperCamelCase__								=     esm.token_dropout
        UpperCamelCase__								=     False  # This code path does not exist in ESM-2
        UpperCamelCase__								=     'rotary'
        UpperCamelCase__								=     False
        UpperCamelCase__								=     None

    if is_folding_model:
        UpperCamelCase__								=     esm.esm.alphabet
    UpperCamelCase__								=     tuple(alphabet.all_toks       )
    UpperCamelCase__								=     alphabet.mask_idx
    UpperCamelCase__								=     alphabet.padding_idx

    if is_folding_model:
        UpperCamelCase__								=     esm.esm
    else:
        UpperCamelCase__								=     esm

    UpperCamelCase__								=     EsmConfig(
        vocab_size=original_esm_model.embed_tokens.num_embeddings			,						mask_token_id=SCREAMING_SNAKE_CASE			,						hidden_size=SCREAMING_SNAKE_CASE			,						num_hidden_layers=SCREAMING_SNAKE_CASE			,						num_attention_heads=SCREAMING_SNAKE_CASE			,						intermediate_size=SCREAMING_SNAKE_CASE			,						max_position_embeddings=10_26			,						layer_norm_eps=1E-5			,						attention_probs_dropout_prob=0.0			,						hidden_dropout_prob=0.0			,						pad_token_id=SCREAMING_SNAKE_CASE			,						emb_layer_norm_before=SCREAMING_SNAKE_CASE			,						token_dropout=SCREAMING_SNAKE_CASE			,						position_embedding_type=SCREAMING_SNAKE_CASE			,						is_folding_model=SCREAMING_SNAKE_CASE			,						esmfold_config=SCREAMING_SNAKE_CASE			,						vocab_list=SCREAMING_SNAKE_CASE			,						)
    if classification_head:
        UpperCamelCase__								=     esm.classification_heads['mnli'].out_proj.weight.shape[0]
    print('Our ESM config:'			,						SCREAMING_SNAKE_CASE       )

    if model.startswith('esmfold'       ):
        UpperCamelCase__								=     EsmForProteinFolding
    elif classification_head:
        UpperCamelCase__								=     EsmForSequenceClassification
    else:
        UpperCamelCase__								=     EsmForMaskedLM
    UpperCamelCase__								=     model_class(SCREAMING_SNAKE_CASE       )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    UpperCamelCase__								=     original_esm_model.embed_tokens.weight
    if position_embedding_type == "absolute":
        UpperCamelCase__								=     original_esm_model.embed_positions.weight

    if config.emb_layer_norm_before:
        UpperCamelCase__								=     original_esm_model.emb_layer_norm_before.weight
        UpperCamelCase__								=     original_esm_model.emb_layer_norm_before.bias

    UpperCamelCase__								=     original_esm_model.emb_layer_norm_after.weight
    UpperCamelCase__								=     original_esm_model.emb_layer_norm_after.bias

    for i in range(config.num_hidden_layers       ):
        # Encoder: start of layer
        UpperCamelCase__								=     model.esm.encoder.layer[i]
        # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
        UpperCamelCase__								=     original_esm_model.layers[i]

        # self attention
        UpperCamelCase__								=     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)       )
        )

        UpperCamelCase__								=     esm_layer.self_attn.q_proj.weight
        UpperCamelCase__								=     esm_layer.self_attn.q_proj.bias
        UpperCamelCase__								=     esm_layer.self_attn.k_proj.weight
        UpperCamelCase__								=     esm_layer.self_attn.k_proj.bias
        UpperCamelCase__								=     esm_layer.self_attn.v_proj.weight
        UpperCamelCase__								=     esm_layer.self_attn.v_proj.bias

        if getattr(esm_layer.self_attn			,						'rot_emb'			,						SCREAMING_SNAKE_CASE       ) 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.
            UpperCamelCase__								=     esm_layer.self_attn.rot_emb.inv_freq

        # LayerNorm changes for pre-activation
        UpperCamelCase__								=     esm_layer.self_attn_layer_norm.weight
        UpperCamelCase__								=     esm_layer.self_attn_layer_norm.bias
        UpperCamelCase__								=     esm_layer.final_layer_norm.weight
        UpperCamelCase__								=     esm_layer.final_layer_norm.bias

        # self-attention output
        UpperCamelCase__								=     layer.attention.output
        assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
        UpperCamelCase__								=     esm_layer.self_attn.out_proj.weight
        UpperCamelCase__								=     esm_layer.self_attn.out_proj.bias

        # intermediate
        UpperCamelCase__								=     layer.intermediate
        assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
        UpperCamelCase__								=     esm_layer.fca.weight
        UpperCamelCase__								=     esm_layer.fca.bias

        # output
        UpperCamelCase__								=     layer.output
        assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
        UpperCamelCase__								=     esm_layer.fca.weight
        UpperCamelCase__								=     esm_layer.fca.bias
        # end of layer

    if is_folding_model:
        UpperCamelCase__								=     esm.esm_s_combine.data
        UpperCamelCase__								=     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:
        UpperCamelCase__								=     esm.esm.classification_heads['mnli'].dense.weight
        UpperCamelCase__								=     esm.classification_heads['mnli'].dense.bias
        UpperCamelCase__								=     esm.classification_heads['mnli'].out_proj.weight
        UpperCamelCase__								=     esm.classification_heads['mnli'].out_proj.bias
    else:
        # LM Head
        UpperCamelCase__								=     esm.lm_head.dense.weight
        UpperCamelCase__								=     esm.lm_head.dense.bias
        UpperCamelCase__								=     esm.lm_head.layer_norm.weight
        UpperCamelCase__								=     esm.lm_head.layer_norm.bias
        UpperCamelCase__								=     esm.lm_head.weight
        UpperCamelCase__								=     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.
        UpperCamelCase__								=     SAMPLE_DATA[:2]
    else:
        UpperCamelCase__								=     SAMPLE_DATA

    if is_folding_model:
        UpperCamelCase__								=     get_esmfold_tokenizer()
        UpperCamelCase__								=     hf_tokenizer(
            [row[1] for row in sample_data]			,						return_tensors='pt'			,						padding=SCREAMING_SNAKE_CASE			,						add_special_tokens=SCREAMING_SNAKE_CASE       )
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     esmfold_encode_sequences([row[1] for row in sample_data]       )
        UpperCamelCase__								=     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.
        UpperCamelCase__								=     alphabet.get_batch_converter()
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     batch_converter(SCREAMING_SNAKE_CASE       )
        # Prepare tokenizer and make sure it matches
        with TemporaryDirectory() as tempdir:
            UpperCamelCase__								=     '\n'.join(alphabet.all_toks       )
            UpperCamelCase__								=     Path(SCREAMING_SNAKE_CASE       ) / 'vocab.txt'
            vocab_file.write_text(SCREAMING_SNAKE_CASE       )
            UpperCamelCase__								=     EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE       )       )

        UpperCamelCase__								=     hf_tokenizer([row[1] for row in sample_data]			,						return_tensors='pt'			,						padding=SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     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.
            UpperCamelCase__								=     esm.cuda().infer([row[1] for row in sample_data]       )
            UpperCamelCase__								=     model.cuda()(
                input_ids=hf_tokens['input_ids'].cuda()			,						attention_mask=hf_tokens['attention_mask'].cuda()       )
        else:
            UpperCamelCase__								=     model(**SCREAMING_SNAKE_CASE			,						output_hidden_states=SCREAMING_SNAKE_CASE       )
            UpperCamelCase__								=     our_output['logits']
            if classification_head:
                UpperCamelCase__								=     esm.model.classification_heads['mnli'](esm.extract_features(SCREAMING_SNAKE_CASE       )       )
            else:
                UpperCamelCase__								=     esm(hf_tokens['input_ids']			,						repr_layers=list(range(9_99       )       )       )
                UpperCamelCase__								=     their_output['logits']

        if is_folding_model:
            UpperCamelCase__								=     torch.max(torch.abs(our_output['positions'] - their_output['positions']       )       ).item()
            UpperCamelCase__								=     torch.allclose(our_output['positions']			,						their_output['positions']			,						atol=1E-5       )
        else:
            UpperCamelCase__								=     torch.max(torch.abs(our_output - their_output       )       ).item()
            UpperCamelCase__								=     torch.allclose(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						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
            UpperCamelCase__								=     model.predict_contacts(hf_tokens['input_ids']			,						hf_tokens['attention_mask']       )
            UpperCamelCase__								=     esm.predict_contacts(hf_tokens['input_ids']       )
            UpperCamelCase__								=     torch.max(torch.abs(our_output - their_output       )       ).item()
            UpperCamelCase__								=     torch.allclose(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						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(SCREAMING_SNAKE_CASE       ).mkdir(parents=SCREAMING_SNAKE_CASE			,						exist_ok=SCREAMING_SNAKE_CASE       )
        print(F'Saving model to {pytorch_dump_folder_path}'       )
        model.save_pretrained(SCREAMING_SNAKE_CASE       )

        del esm  # Free up some memory before continuing

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'       )
    hf_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE       )

    if push_to_repo:
        model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE			,						use_auth_token=SCREAMING_SNAKE_CASE       )
        hf_tokenizer.push_to_hub(repo_id=SCREAMING_SNAKE_CASE			,						use_auth_token=SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			int=						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__       :			Union[str, 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
  )





"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




