text: import argparse
  import pickle as pkl 
  from typing import List, Iterator, Dict  
  from collections import namedtuple
  import os
  import overrides
  import tempfile
  
  import numpy as np
  import torch
  from tqdm import tqdm
  import networkx as nx
  import logging
  
  from allennlp.commands.predict import _get_predictor, Predict
  from allennlp.commands import ArgumentParserWithDefaults
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common.checks import check_for_gpu, ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.util import lazy_groups_of
  from allennlp.models.archival import load_archive
  from allennlp.predictors.predictor import Predictor, JsonDict
  from allennlp.data import Instance
  from allennlp.common.util import import_submodules
  
  from miso.data.dataset_readers.decomp_parsing.decomp import DecompGraph
  from miso.data.dataset_readers.decomp_parsing.decomp_with_syntax import DecompGraphWithSyntax
  from miso.metrics.conllu import evaluate_wrapper, UDError
  from miso.commands.predict import _ReturningPredictManager 
  
  logger = logging.getLogger(__name__) 
  
  compute_args = {"gold_file":None, "system_file": None}  
  
  ComputeTup = namedtuple("compute_args", sorted(compute_args))
  
  class ArgNamespace:
      def __init__(self,
                  input_file,
                  batch_size,
                  silent,
                  beam_size,
                  save_pred_path):
          self.input_file = input_file
          self.batch_size = batch_size
          self.silent = silent
          self.beam_size = beam_size
          self.save_pred_path = save_pred_path
  
  
  class ConlluPredict(Subcommand): 
      def add_subparser(self, name: str, parser: argparse._SubParsersAction) -> argparse.ArgumentParser:
          self.name = name 
          description = """Run the specified model against a JSON-lines input file."""
          subparser = parser.add_parser(
              self.name, description=description, help="Use a trained model to make predictions."
          )
  
          subparser.add_argument(
              "archive_file", type=str, help="the archived model to make predictions with"
          )
          subparser.add_argument("input_file", type=str, help="path to or url of the input file")
  
          subparser.add_argument("--output-file", type=str, help="path to output file")
          subparser.add_argument(
              "--weights-file", type=str, help="a path that overrides which weights file to use"
          )
  
          batch_size = subparser.add_mutually_exclusive_group(required=False)
          batch_size.add_argument(
              "--batch-size", type=int, default=1, help="The batch size to use for processing"
          )
  
          subparser.add_argument(
              "--silent", action="store_true", help="do not print output to stdout"
          )
  
          cuda_device = subparser.add_mutually_exclusive_group(required=False)
          cuda_device.add_argument(
              "--cuda-device", type=int, default=-1, help="id of GPU to use (if any)"
          )
  
          subparser.add_argument(
              "--use-dataset-reader",
              action="store_true",
              help="Whether to use the dataset reader of the original model to load Instances. "
              "The validation dataset reader will be used if it exists, otherwise it will "
              "fall back to the train dataset reader. This behavior can be overridden "
              "with the --dataset-reader-choice flag.",
          )
  
          subparser.add_argument(
              "--dataset-reader-choice",
              type=str,
              choices=["train", "validation"],
              default="validation",
              help="Indicates which model dataset reader to use if the --use-dataset-reader "
              "flag is set.",
          )
  
          subparser.add_argument(
              "-o",
              "--overrides",
              type=str,
              default="",
              help="a JSON structure used to override the experiment configuration",
          )
  
          subparser.add_argument(
              "--predictor", type=str, help="optionally specify a specific predictor to use"
          )
          # my options
          subparser.add_argument('--beam-size',
                            type=int,
                            default=1,
                            help="Beam size for seq2seq decoding")
  
          subparser.add_argument("--save-pred-path", type=str, required=False, 
                                  help="optionally specify a path for output pkl") 
  
          subparser.add_argument("--load-path", type=str, required=False, 
                                  help="path to precomuted predicitons") 
          
          subparser.add_argument("--semantics-only", action="store_true", default=False)
  
          subparser.add_argument("--drop-syntax", action="store_true", default=False)
  
          subparser.add_argument("--include-attribute-scores", action="store_true", default=False)
  
          subparser.add_argument("--line-limit", type=int, default=None)
  
          subparser.add_argument("--json-output-file", type=str, required=False,
                                  help="optionally specify a path to output json dict") 
  
          subparser.add_argument("--oracle", action = "store_true") 
  
          subparser.set_defaults(func=_construct_and_predict)
  
          return subparser
  
  def _construct_and_predict(args: argparse.Namespace) -> None:
      predictor = _get_predictor(args)
      args.predictor = predictor
      ConlluPredictWrapper.from_params(args).predict_and_compute()
  
  
  class ConlluPredictWrapper:
      """
      Reads, predicts, and scores syntactic conllu score 
      """
      def __init__(self,
                  predictor = None,
                  input_file = "dev",
                  batch_size = 32,
                  silent = True,
                  beam_size = 5, 
                  save_pred_path = None,
                  load_path = None,
                  semantics_only = False,
                  drop_syntax = True,
                  line_limit = None,
                  include_attribute_scores = False,
                  oracle = False,
                  output_file = None):
  
          self.load_path = load_path
          if self.load_path is not None:
              # don't use a predictor if provided with pre-computed graphs
              pass
          else:
              # otherwise we need to predict 
              self.predictor = predictor
              if predictor is not None:
                  self.pred_args = ArgNamespace(input_file, batch_size, silent, beam_size, save_pred_path)
  
          self.semantics_only = semantics_only
          self.drop_syntax = drop_syntax
          self.line_limit = line_limit
          self.include_attribute_scores = include_attribute_scores
          self.oracle = oracle
          self.output_file = output_file
          
          if self.pred_args.input_file not in ["train", "dev", "test"] and \
          "original_only" not in self.pred_args.input_file and \
          "altered_only" not in self.pred_args.input_file and \
          "en-ud" not in self.pred_args.input_file: 
              if os.path.exists(self.pred_args.input_file):
                  # ud only case
                  # make compatible with allenNLP 
                  self.pred_args.input_file += "/*"
   
          self.manager = _ReturningPredictManager(self.predictor,
                                      self.pred_args.input_file,
                                      None,
                                      self.pred_args.batch_size,
                                      not self.pred_args.silent,
                                      True,
                                      self.pred_args.beam_size,
                                      line_limit = self.line_limit,
                                      oracle = self.oracle,
                                      json_output_file = None)  
  
      @staticmethod
      def conllu_dict_to_str(conllu_dict, id, text):
  
          #text = " ".join([row["form"] for row in conllu_dict])
  
          conllu_str = f"# sent_id = train-s{id}\n" +\
                       f"# text = {text}\n" + \
                       f"# org_sent_id = {id}\n"
          colnames = ["ID", "form", "lemma", "upos", "xpos", "feats", "head", "deprel", "deps", "misc"]
          for row in conllu_dict:
              vals = [row[cn] for cn in colnames]
              conllu_str += "\t".join(vals) + "\n"
          #conllu_str += '\n' 
          return conllu_str
  
      def predict_and_compute(self):
          assert(self.predictor is not None)
          input_instances, output_graphs = self.manager.run()
     
          # ignore everything except conllu graph 
          if len(output_graphs) > 0 and type(output_graphs[0]) == tuple:
              output_graphs = [x[-1] for x in output_graphs]
  
          input_sents = [inst.fields['syn_tokens_str'].metadata for inst in input_instances]
  
          las_scores = []
          uas_scores = []
          mlas_scores = []
          blex_scores = []
          
          all_strs = []
          for i in range(len(input_instances)):
              all_strs.append(output_graphs[i])
  
          with open(self.output_file, "w") as pred_file:
              pred_file.write("\n".join(all_strs))
  
          logger.info(f"succesfully wrote {len(all_strs)} to {self.output_file}") 
   
      @classmethod
      def from_params(cls, args):
          return cls(predictor=args.predictor,
                     input_file = args.input_file,
                     batch_size = args.batch_size,
                     silent = args.silent,
                     beam_size = args.beam_size, 
                     save_pred_path = args.save_pred_path,
                     load_path = args.load_path,
                     semantics_only = args.semantics_only,
                     drop_syntax = args.drop_syntax,
                     line_limit = args.line_limit,
                     include_attribute_scores = args.include_attribute_scores,
                     oracle = args.oracle,
                     output_file = args.output_file
                     )
  
  