text: # Copyright 2021 AlQuraishi Laboratory
  # Copyright 2021 DeepMind Technologies Limited
  # 
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import argparse
  import glob
  import logging
  import numpy as np
  import os
  from pathlib import Path
  
  import random
  import sys
  import time
  import torch
  import tqdm
  
  from openfold.config import model_config
  from openfold.data import templates, feature_pipeline, data_pipeline
  from openfold.model.model import AlphaFold
  from openfold.utils.import_weights import (
      import_jax_weights_,
  )
  
  from scripts.utils import add_data_args
  
  
  def main(args):
  
      for file in tqdm.tqdm(glob.glob(args.fasta_path + '*.fasta')):
          # Gather input sequences
          with open(file, "r") as fp:
              lines = [l.strip() for l in fp.readlines()]
          tags, seqs = lines[::2], lines[1::2]
          tags = [l[1:] for l in tags]
      
          for tag, seq in zip(tags, seqs):
              print(tag)
              outfile = tag + '.npy'
              outdir_single = os.path.join(args.output_dir, 'single')
              outdir_pair = os.path.join(args.output_dir, 'pair')
              outpath_single = os.path.join(outdir_single, outfile)
              outpath_pair = os.path.join(outdir_pair, outfile)
          
              if not os.path.isfile(outpath_single): # or not os.path.isfile(outpath_pair):
                  try:
                      config = model_config(args.model_name)
                      model = AlphaFold(config)
                      model = model.eval()
                      import_jax_weights_(model, args.param_path, version=args.model_name)
                  
                      model = model.to(args.model_device)
                      print('model on device')
                      template_featurizer = templates.TemplateHitFeaturizer(
                          mmcif_dir=args.template_mmcif_dir,
                          max_template_date=args.max_template_date,
                          max_hits=config.data.predict.max_templates,
                          kalign_binary_path=args.kalign_binary_path,
                          release_dates_path=args.release_dates_path,
                          obsolete_pdbs_path=args.obsolete_pdbs_path
                      )
                  
                      use_small_bfd = (args.bfd_database_path is None)
                  
                      data_processor = data_pipeline.DataPipeline(
                          template_featurizer=template_featurizer,
                      )
                  
                      random_seed = args.data_random_seed
                      if random_seed is None:
                          random_seed = random.randrange(sys.maxsize)
                      feature_processor = feature_pipeline.FeaturePipeline(config.data)
                      
                      Path(args.output_dir).mkdir(parents=True, exist_ok=True)
                      Path(outdir_single).mkdir(parents=True, exist_ok=True)
                      Path(outdir_pair).mkdir(parents=True, exist_ok=True)
                      
                      if(args.use_precomputed_alignments is None):
                          alignment_dir = os.path.join(args.output_dir, "alignments")
                      else:
                          alignment_dir = args.use_precomputed_alignments
                  
                      fasta_path = os.path.join(args.output_dir, f"{tag}_tmp.fasta")
                      with open(fasta_path, "w") as fp:
                          fp.write(f">{tag}\n{seq}")
              
                      logging.info("Generating features...")
                      local_alignment_dir = os.path.join(alignment_dir, tag)
      
                      if(args.use_precomputed_alignments is None):
                          if not os.path.exists(local_alignment_dir):
                              os.makedirs(local_alignment_dir)
                          
                          alignment_runner = data_pipeline.AlignmentRunner(
                              jackhmmer_binary_path=args.jackhmmer_binary_path,
                              hhblits_binary_path=args.hhblits_binary_path,
                              hhsearch_binary_path=args.hhsearch_binary_path,
                              uniref90_database_path=args.uniref90_database_path,
                              mgnify_database_path=args.mgnify_database_path,
                              bfd_database_path=args.bfd_database_path,
                              uniclust30_database_path=args.uniclust30_database_path,
                              pdb70_database_path=args.pdb70_database_path,
                              use_small_bfd=use_small_bfd,
                              no_cpus=args.cpus,
                          )
                          alignment_runner.run(
                              fasta_path, local_alignment_dir
                          )
      
                      feature_dict = data_processor.process_fasta(
                          fasta_path=fasta_path, alignment_dir=local_alignment_dir
                      )
              
                      # Remove temporary FASTA file
                      os.remove(fasta_path)
                  
                      processed_feature_dict = feature_processor.process_features(
                          feature_dict, mode='predict',
                      )
                  
                      logging.info("Executing model...")
                      batch = processed_feature_dict
                      with torch.no_grad():
                          batch = {
                              k:torch.as_tensor(v, device=args.model_device)
                              for k,v in batch.items()
                          }
                      
                          t = time.perf_counter()
                          out = model(batch)
                          logging.info(f"Inference time: {time.perf_counter() - t}")
                          
                          # Save embeddings
                          np.save(outpath_single, out['single'].cpu().detach().numpy())
                          #np.save(outpath_pair, out['pair'].cpu().detach().numpy())
                  
                  except RuntimeError:
                      print(tag, 'CUDA OOM')
                      continue
  
                  except FileNotFoundError:
                      print(tag, 'missing MSA')
                      continue
                  
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "fasta_path", type=str,
      )
      parser.add_argument(
          "template_mmcif_dir", type=str,
      )
      parser.add_argument(
          "--use_precomputed_alignments", type=str, default=None,
          help="""Path to alignment directory. If provided, alignment computation 
                  is skipped and database path arguments are ignored."""
      )
      parser.add_argument(
          "--output_dir", type=str, default=os.getcwd(),
          help="""Name of the directory in which to output the prediction.""",
      )
      parser.add_argument(
          "--model_device", type=str, default="cpu",
          help="""Name of the device on which to run the model. Any valid torch
               device name is accepted (e.g. "cpu", "cuda:0")"""
      )
      parser.add_argument(
          "--model_name", type=str, default="model_1",
          help="""Name of a model config. Choose one of model_{1-5} or 
               model_{1-5}_ptm, as defined on the AlphaFold GitHub."""
      )
      parser.add_argument(
          "--param_path", type=str, default=None,
          help="""Path to model parameters. If None, parameters are selected
               automatically according to the model name from 
               openfold/resources/params"""
      )
      parser.add_argument(
          "--cpus", type=int, default=4,
          help="""Number of CPUs with which to run alignment tools"""
      )
      parser.add_argument(
          '--preset', type=str, default='full_dbs',
          choices=('reduced_dbs', 'full_dbs')
      )
      parser.add_argument(
          '--data_random_seed', type=str, default=None
      )
      add_data_args(parser)
      args = parser.parse_args()
  
      if(args.param_path is None):
          args.param_path = os.path.join(
              "openfold", "resources", "params", 
              "params_" + args.model_name + ".npz"
          )
  
      if(args.model_device == "cpu" and torch.cuda.is_available()):
          logging.warning(
              """The model is being run on CPU. Consider specifying 
              --model_device for better performance"""
          )
  
      main(args)
  