






"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase					=     logging.getLogger(__name__)






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							int         =     p * torch.log(__A						)
       lowercase__:							Union[str, Any]         =     0
       return -plogp.sum(dim=-1						)






def 							A(    snake_case_						):
       """simple docstring"""





       logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(__A						)						)						)						)
       for row in range(len(__A						)						):
              if tensor.dtype != torch.long:
                     logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data						)						)
              else:
                     logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data						)						)






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_=True		,  snake_case_=True		,  snake_case_=None		,  snake_case_=False						):
       """simple docstring"""





       lowercase__    ,      lowercase__:							Union[str, Any]         =     model.config.num_hidden_layers, model.config.num_attention_heads
       lowercase__:							Tuple         =     torch.zeros(__A		,  __A						).to(args.device						)
       lowercase__:							Any         =     torch.zeros(__A		,  __A						).to(args.device						)

       if head_mask is None:
              lowercase__:							Optional[int]         =     torch.ones(__A		,  __A						).to(args.device						)

       head_mask.requires_grad_(requires_grad=__A						)
       # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
       if actually_pruned:
              lowercase__:							Optional[int]         =     None

       lowercase__:							Tuple         =     None
       lowercase__:							Dict         =     None
       lowercase__:							int         =     0.0

       for step, inputs in enumerate(tqdm(__A		,  desc="Iteration"		,  disable=args.local_rank not in [-1, 0]						)						):
              for k, v in inputs.items():
                     lowercase__:							Dict         =     v.to(args.device						)

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              lowercase__:							List[str]         =     model(**__A		,  head_mask=__A						)
              lowercase__    ,      lowercase__    ,      lowercase__:							List[Any]         =     (
                  outputs[0],
                  outputs[1],
                  outputs[-1],
              )  # Loss and logits are the first, attention the last
              loss.backward()  # Backpropagate to populate the gradients in the head mask

              if compute_entropy:
                     for layer, attn in enumerate(__A						):
                            lowercase__:							Dict         =     entropy(attn.detach()						) * inputs["attention_mask"].float().unsqueeze(1						)
                            attn_entropy[layer] += masked_entropy.sum(-1						).sum(0						).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()

              # Also store our logits/labels if we want to compute metrics afterwards
              if preds is None:
                     lowercase__:							Optional[Any]         =     logits.detach().cpu().numpy()
                     lowercase__:							List[Any]         =     inputs["labels"].detach().cpu().numpy()
              else:
                     lowercase__:							str         =     np.append(__A		,  logits.detach().cpu().numpy()		,  axis=0						)
                     lowercase__:							Dict         =     np.append(__A		,  inputs["labels"].detach().cpu().numpy()		,  axis=0						)

              tot_tokens += inputs["attention_mask"].float().detach().sum().data

       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
              lowercase__:							Optional[Any]         =     2
              lowercase__:							List[Any]         =     torch.pow(torch.pow(__A		,  __A						).sum(-1						)		,  1 / exponent						)
              head_importance /= norm_by_layer.unsqueeze(-1						) + 1E-2_0

       if not args.dont_normalize_global_importance:
              lowercase__:							Tuple         =     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

       # Print/save matrices
       np.save(os.path.join(args.output_dir		,  "attn_entropy.npy"						)		,  attn_entropy.detach().cpu().numpy()						)
       np.save(os.path.join(args.output_dir		,  "head_importance.npy"						)		,  head_importance.detach().cpu().numpy()						)

       logger.info("Attention entropies"						)
       print_ad_tensor(__A						)
       logger.info("Head importance scores"						)
       print_ad_tensor(__A						)
       logger.info("Head ranked by importance scores"						)
       lowercase__:							Union[str, Any]         =     torch.zeros(head_importance.numel()		,  dtype=torch.long		,  device=args.device						)
       lowercase__:							List[str]         =     torch.arange(
           head_importance.numel()		,  device=args.device						)
       lowercase__:							List[str]         =     head_ranks.view_as(__A						)
       print_ad_tensor(__A						)

       return attn_entropy, head_importance, preds, labels






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							List[Any]         =     compute_heads_importance(__A		,  __A		,  __A		,  compute_entropy=__A						)
       lowercase__:							Union[str, Any]         =     np.argmax(__A		,  axis=1						) if args.output_mode == "classification" else np.squeeze(__A						)
       lowercase__:							Any         =     glue_compute_metrics(args.task_name		,  __A		,  __A						)[args.metric_name]
       logger.info("Pruning: original score: %f, threshold: %f"		,  __A		,  original_score * args.masking_threshold						)

       lowercase__:							Tuple         =     torch.ones_like(__A						)
       lowercase__:							List[Any]         =     max(1		,  int(new_head_mask.numel() * args.masking_amount						)						)

       lowercase__:							Tuple         =     original_score
       while current_score >= original_score * args.masking_threshold:
              lowercase__:							Dict         =     new_head_mask.clone()  # save current head mask
              # heads from least important to most - keep only not-masked heads
              lowercase__:							List[Any]         =     float("Inf"						)
              lowercase__:							Union[str, Any]         =     head_importance.view(-1						).sort()[1]

              if len(__A						) <= num_to_mask:
                     break

              # mask heads
              lowercase__:							Tuple         =     current_heads_to_mask[:num_to_mask]
              logger.info("Heads to mask: %s"		,  str(current_heads_to_mask.tolist()						)						)
              lowercase__:							Tuple         =     new_head_mask.view(-1						)
              lowercase__:							Dict         =     0.0
              lowercase__:							int         =     new_head_mask.view_as(__A						)
              lowercase__:							List[Any]         =     new_head_mask.clone().detach()
              print_ad_tensor(__A						)

              # Compute metric and head importance again
              lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							Any         =     compute_heads_importance(
                  __A		,  __A		,  __A		,  compute_entropy=__A		,  head_mask=__A						)
              lowercase__:							Tuple         =     np.argmax(__A		,  axis=1						) if args.output_mode == "classification" else np.squeeze(__A						)
              lowercase__:							Any         =     glue_compute_metrics(args.task_name		,  __A		,  __A						)[args.metric_name]
              logger.info(
                  "Masking: current score: %f, remaining heads %d (%.1f percents)"		,  __A		,  new_head_mask.sum()		,  new_head_mask.sum() / new_head_mask.numel() * 100		,  )

       logger.info("Final head mask"						)
       print_ad_tensor(__A						)
       np.save(os.path.join(args.output_dir		,  "head_mask.npy"						)		,  head_mask.detach().cpu().numpy()						)

       return head_mask






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     datetime.now()
       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							str         =     compute_heads_importance(
           __A		,  __A		,  __A		,  compute_entropy=__A		,  compute_importance=__A		,  head_mask=__A						)
       lowercase__:							str         =     np.argmax(__A		,  axis=1						) if args.output_mode == "classification" else np.squeeze(__A						)
       lowercase__:							str         =     glue_compute_metrics(args.task_name		,  __A		,  __A						)[args.metric_name]
       lowercase__:							List[str]         =     datetime.now() - before_time

       lowercase__:							List[str]         =     sum(p.numel() for p in model.parameters()						)
       lowercase__:							str         =     {
           layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A						)						)
       }

       assert sum(len(__A						) for h in heads_to_prune.values()						) == (1 - head_mask.long()).sum().item()
       model.prune_heads(__A						)
       lowercase__:							Tuple         =     sum(p.numel() for p in model.parameters()						)

       lowercase__:							Union[str, Any]         =     datetime.now()
       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							Optional[Any]         =     compute_heads_importance(
           __A		,  __A		,  __A		,  compute_entropy=__A		,  compute_importance=__A		,  head_mask=__A		,  actually_pruned=__A		,  )
       lowercase__:							str         =     np.argmax(__A		,  axis=1						) if args.output_mode == "classification" else np.squeeze(__A						)
       lowercase__:							Optional[int]         =     glue_compute_metrics(args.task_name		,  __A		,  __A						)[args.metric_name]
       lowercase__:							Dict         =     datetime.now() - before_time

       logger.info(
           "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"		,  __A		,  __A		,  pruned_num_params / original_num_params * 100		,  )
       logger.info("Pruning: score with masking: %f score with pruning: %f"		,  __A		,  __A						)
       logger.info("Pruning: speed ratio (new timing / original timing): %f percents"		,  original_time / new_time * 100						)






def 							A(    ):
       """simple docstring"""





       lowercase__:							Optional[int]         =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--data_dir"		,  default=__A		,  type=__A		,  required=__A		,  help="The input data dir. Should contain the .tsv files (or other data files) for the task."		,  )
       parser.add_argument(
           "--model_name_or_path"		,  default=__A		,  type=__A		,  required=__A		,  help="Path to pretrained model or model identifier from huggingface.co/models"		,  )
       parser.add_argument(
           "--task_name"		,  default=__A		,  type=__A		,  required=__A		,  help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()						)		,  )
       parser.add_argument(
           "--output_dir"		,  default=__A		,  type=__A		,  required=__A		,  help="The output directory where the model predictions and checkpoints will be written."		,  )

       # Other parameters
       parser.add_argument(
           "--config_name"		,  default=""		,  type=__A		,  help="Pretrained config name or path if not the same as model_name_or_path"		,  )
       parser.add_argument(
           "--tokenizer_name"		,  default=""		,  type=__A		,  help="Pretrained tokenizer name or path if not the same as model_name_or_path"		,  )
       parser.add_argument(
           "--cache_dir"		,  default=__A		,  type=__A		,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"		,  )
       parser.add_argument(
           "--data_subset"		,  type=__A		,  default=-1		,  help="If > 0: limit the data to a subset of data_subset instances."						)
       parser.add_argument(
           "--overwrite_output_dir"		,  action="store_true"		,  help="Whether to overwrite data in output directory"						)
       parser.add_argument(
           "--overwrite_cache"		,  action="store_true"		,  help="Overwrite the cached training and evaluation sets"						)

       parser.add_argument(
           "--dont_normalize_importance_by_layer"		,  action="store_true"		,  help="Don't normalize importance score by layers"						)
       parser.add_argument(
           "--dont_normalize_global_importance"		,  action="store_true"		,  help="Don't normalize all importance scores between 0 and 1"		,  )

       parser.add_argument(
           "--try_masking"		,  action="store_true"		,  help="Whether to try to mask head until a threshold of accuracy."						)
       parser.add_argument(
           "--masking_threshold"		,  default=0.9		,  type=__A		,  help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."		,  )
       parser.add_argument(
           "--masking_amount"		,  default=0.1		,  type=__A		,  help="Amount to heads to masking at each masking step."						)
       parser.add_argument("--metric_name"		,  default="acc"		,  type=__A		,  help="Metric to use for head masking."						)

       parser.add_argument(
           "--max_seq_length"		,  default=128		,  type=__A		,  help=(
               "The maximum total input sequence length after WordPiece tokenization. \n"
               "Sequences longer than this will be truncated, sequences shorter padded."
           )		,  )
       parser.add_argument("--batch_size"		,  default=1		,  type=__A		,  help="Batch size."						)

       parser.add_argument("--seed"		,  type=__A		,  default=42						)
       parser.add_argument("--local_rank"		,  type=__A		,  default=-1		,  help="local_rank for distributed training on gpus"						)
       parser.add_argument("--no_cuda"		,  action="store_true"		,  help="Whether not to use CUDA when available"						)
       parser.add_argument("--server_ip"		,  type=__A		,  default=""		,  help="Can be used for distant debugging."						)
       parser.add_argument("--server_port"		,  type=__A		,  default=""		,  help="Can be used for distant debugging."						)
       lowercase__:							Tuple         =     parser.parse_args()

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print("Waiting for debugger attach"						)
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,  redirect_output=__A						)
              ptvsd.wait_for_attach()

       # Setup devices and distributed training
       if args.local_rank == -1 or args.no_cuda:
              lowercase__:							Optional[int]         =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
              lowercase__:							Union[str, Any]         =     0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank						)
              lowercase__:							List[Any]         =     torch.device("cuda"		,  args.local_rank						)
              lowercase__:							Any         =     1
              torch.distributed.init_process_group(backend="nccl"						)  # Initializes the distributed backend

       # Setup logging
       logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN						)
       logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device		,  args.n_gpu		,  bool(args.local_rank != -1						)						)						)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(args.local_rank						):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()

       # Set seeds
       set_seed(args.seed						)

       # Prepare GLUE task
       lowercase__:							Union[str, Any]         =     args.task_name.lower()
       if args.task_name not in glue_processors:
              raise ValueError("Task not found: %s" % (args.task_name)						)
       lowercase__:							str         =     glue_processors[args.task_name]()
       lowercase__:							Dict         =     glue_output_modes[args.task_name]
       lowercase__:							int         =     processor.get_labels()
       lowercase__:							Dict         =     len(__A						)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       lowercase__:							List[Any]         =     AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path		,  num_labels=__A		,  finetuning_task=args.task_name		,  output_attentions=__A		,  cache_dir=args.cache_dir		,  )
       lowercase__:							Any         =     AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path		,  cache_dir=args.cache_dir		,  )
       lowercase__:							List[Any]         =     AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path		,  from_tf=bool(".ckpt" in args.model_name_or_path						)		,  config=__A		,  cache_dir=args.cache_dir		,  )

       # Distributed and parallel training
       model.to(args.device						)
       if args.local_rank != -1:
              lowercase__:							Optional[int]         =     nn.parallel.DistributedDataParallel(
                  __A		,  device_ids=[args.local_rank]		,  output_device=args.local_rank		,  find_unused_parameters=__A						)
       elif args.n_gpu > 1:
              lowercase__:							Optional[Any]         =     nn.DataParallel(__A						)

       # Print/save training arguments
       os.makedirs(args.output_dir		,  exist_ok=__A						)
       torch.save(__A		,  os.path.join(args.output_dir		,  "run_args.bin"						)						)
       logger.info("Training/evaluation parameters %s"		,  __A						)

       # Prepare dataset for the GLUE task
       lowercase__:							Tuple         =     GlueDataset(__A		,  tokenizer=__A		,  mode="dev"						)
       if args.data_subset > 0:
              lowercase__:							Dict         =     Subset(__A		,  list(range(min(args.data_subset		,  len(__A						)						)						)						)						)
       lowercase__:							Any         =     SequentialSampler(__A						) if args.local_rank == -1 else DistributedSampler(__A						)
       lowercase__:							List[str]         =     DataLoader(
           __A		,  sampler=__A		,  batch_size=args.batch_size		,  collate_fn=__A						)

       # Compute head entropy and importance score
       compute_heads_importance(__A		,  __A		,  __A						)

       # Try head masking (set heads to zero until the score goes under a threshole)
       # and head pruning (remove masked heads and see the effect on the network)
       if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
              lowercase__:							Optional[int]         =     mask_heads(__A		,  __A		,  __A						)
              prune_heads(__A		,  __A		,  __A		,  __A						)


if __name__ == "__main__":
  main()










'''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__    :   Optional[int]								=			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__    :   Optional[int]								=			{
    '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__    :   List[Any]								=			list('ARNDCQEGHILKMFPSTWYV')

a__    :   Any								=			restypes + ['X']
a__    :   List[str]								=			restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']







def _UpperCamelCase			(	)				->						str:


      '''simple docstring'''



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







def _UpperCamelCase			(	__A ,     __A       )				->						List[str]:


      '''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 _UpperCamelCase			(	__A ,     __A ,     __A ,     __A ,     __A       )				->						int:


      '''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(__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
            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=__A ,     hidden_size=__A ,     num_hidden_layers=__A ,     num_attention_heads=__A ,     intermediate_size=__A ,     max_position_embeddings=1026 ,     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:
            UpperCamelCase__      =							esm.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our ESM config:" ,     __A       )

      if model.startswith("esmfold"       ):
            UpperCamelCase__      =							EsmForProteinFolding
      elif classification_head:
            UpperCamelCase__      =							EsmForSequenceClassification
      else:
            UpperCamelCase__      =							EsmForMaskedLM
      UpperCamelCase__      =							model_class(__A       )
      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" ,     __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.
                  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=__A ,     add_special_tokens=__A       )
            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(__A       )
            # Prepare tokenizer and make sure it matches
            with TemporaryDirectory() as tempdir:
                  UpperCamelCase__      =							"\n".join(alphabet.all_toks       )
                  UpperCamelCase__      =							Path(__A       ) / "vocab.txt"
                  vocab_file.write_text(__A       )
                  UpperCamelCase__      =							EsmTokenizer(vocab_file=str(__A       )       )

            UpperCamelCase__      =							hf_tokenizer([row[1] for row in sample_data] ,     return_tensors="pt" ,     padding=__A       )
            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(**__A ,     output_hidden_states=__A       )
                  UpperCamelCase__      =							our_output["logits"]
                  if classification_head:
                        UpperCamelCase__      =							esm.model.classification_heads["mnli"](esm.extract_features(__A       )       )
                  else:
                        UpperCamelCase__      =							esm(hf_tokens["input_ids"] ,     repr_layers=list(range(999       )       )       )
                        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(__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
                  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(__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__":
  a__    :   str								=			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[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
  )





