text: """This script is a hack which hijacks sys.argv when
  the arguments are passed by wandb server. It converts
  them into the format understood by the local scripts
  and runs the entrypoint for all local scripts"""
  
  import sys
  from copy import deepcopy
  import re
  from typing import List, Iterable, Dict, Union, Optional, Callable, Tuple, Any
  import json
  import functools
  import argparse
  from collections import OrderedDict
  import wandb
  from allennlp.run import run as allennlp_run
  from pathlib import Path
  import logging
  import os
  logger = logging.getLogger(__name__)
  
  
  class Fixes(object):
      def _replace(self, number_str_match):  # type: ignore
  
          return ": " + number_str_match.group(1)  # return just the number
  
      def __call__(self, s):  # type: ignore
  
          res_copy = re.sub(r": \"([0-9.]+)\"", self._replace, s)
  
          return res_copy
  
  
  def match_all(inp: Iterable[str],
                pattern: str = r"^--\S+=\S+$") -> Dict[str, str]:
      key_value = {}
  
      for k_v in inp:
          m = re.match(pattern, k_v)
  
          if m is None:
              raise ValueError("{} not in --key=value form".format(k_v))
          k, v = m.group(0)[2:].split('=')
          key_value[k] = v
  
      return key_value
  
  
  def filter_level0(
          pairs: Dict[str, str]) -> Dict[str, Union[str, Dict[str, str]]]:
      """ Filter out key, value pairs for form parent.child: value"""
      result = {}
  
      for k, v in pairs.items():
          key_parts = k.split('.')
  
          if len(key_parts) > 1:  # has form parent.children
              parent = key_parts[0]  # create the new key
              subkey = '.'.join(key_parts[1:])
              parent_dict: Dict[str, str] = result.get(parent,
                                                       {})  # type: ignore
              parent_dict[subkey] = v
              result[parent] = parent_dict  # type: ignore
          else:
              result[k] = v
  
      return result  # type: ignore
  
  
  def filter_drop_empty_values(pairs: Dict) -> Dict:
      res = {}
  
      for k, v in pairs.items():
          if v:
              res[k] = v
  
      return res
  
  
  def extract_program_and_non_kwargs(everything: List[str],
                                     allowed_pattern: str = r"^[^-=]+$"
                                     ) -> Tuple[str, str]:
      words = everything
      regex = re.compile(allowed_pattern)
      filter(lambda w: not regex.match(w), words)
      prog_and_non_kwargs = []
      kwargs = []
  
      for word in words:
          if regex.fullmatch(word):
              prog_and_non_kwargs.append(word)
          else:
              kwargs.append(word)
  
      return (prog_and_non_kwargs), (kwargs)
  
  
  def process_argv_args(argv,
                        fixers: Optional[List[Callable[[str], str]]] = None,
                        filters: Optional[List[Callable[[Dict], Dict]]] = None):
  
      if fixers is None:
          fixers = [Fixes()]
  
      if filters is None:
          filters = [filter_level0]
      args = deepcopy(argv)
      # first argument should be prog_name
      prog, args = extract_program_and_non_kwargs(args)
      # expected pattern
      key_value_pairs = match_all(args)
      required_form = functools.reduce(lambda res, f: f(res), filters,
                                       key_value_pairs)
      required_string_form = ' '.join([
          "--{} {}".format(k, json.dumps(v))
  
          if isinstance(v, dict) else "--{} {}".format(k, v)
  
          for k, v in required_form.items()
      ])
      fixed = functools.reduce(lambda res, f: f(res), fixers,
                               required_string_form)
  
      return ' '.join([prog, fixed])
  
  
  def create_dynamic_parser(
          args: Optional[List[str]] = None,
          known_args_parser: Optional[argparse.ArgumentParser] = None):
  
      if known_args_parser is None:
          known_args_parser = argparse.ArgumentParser()
  
      if known_args_parser is not None:
          known_args, unknown_args = known_args_parser.parse_known_args(args)
  
      for unknown_arg in unknown_args:
          # expect them to be like --name=value
          try:
              name, value = unknown_arg.split('=')
          except Exception as e:
              raise ValueError(
                  "{} no in --key=value form".format(unknown_arg)) from e
  
          if name.startswith(("-", "--")):
              known_args_parser.add_argument(name)
          else:
              raise ValueError("{} no in --key=value form".format(unknown_arg))
  
      return known_args_parser
  
  
  def apply_filters_on_dict(args_dict: Dict, filters):
      filtered = functools.reduce(lambda res, f: f(res), filters, args_dict)
  
      return filtered
  
  
  class PatterReformatter(object):
      def __init__(self, pattern=("--{}", "{}")):
          self.pattern = pattern
  
      def __call__(self, k, v):
          if isinstance(v, dict):
              res = (self.pattern[0].format(k), self.pattern[1].format(
                  json.dumps(v)))
  
          else:
              res = (self.pattern[0].format(k), self.pattern[1].format(v))
  
          return res
  
  
  class PatterReformatterWithNameExceptions(PatterReformatter):
      def __init__(self,
                   pattern: Tuple[str, str] = ("--{}", "{}"),
                   exceptions: Dict[str, Callable] = None) -> None:
  
          if exceptions is None:
              raise ValueError("Use PatterReformatter instead")
          super().__init__(pattern)  # type: ignore
          self.exceptions = exceptions
  
      def __call__(self, k: Any, v: Any) -> Tuple[str, str]:
  
          if k in self.exceptions:
              return (self.exceptions[k])(k, v)
          else:
              return super().__call__(k, v)
  
  
  def reformat_as_required(args_dict: Dict,
                           reformater=None,
                           fixers=None,
                           reorder=None) -> List[str]:
  
      if reformater is None:
          reformater = PatterReformatter()
  
      if fixers is None:
          fixers = [Fixes()]
  
      if reorder is not None:
          if isinstance(reorder, list):
              args_dict = functools.reduce(lambda res, f: f(res), reorder,
                                           args_dict)
          else:
              args_dict = reorder(args_dict)
      # required_string_form = [reformater(k, v) for k, v in args_dict.items()]
      required_string_form = []
  
      for k, v in args_dict.items():
          temp = reformater(k, v)
          required_string_form.append(temp)
      fixed = [
          functools.reduce(lambda res, f: f(res), fixers, v)
  
          for key_value_or_just_value_tuple in required_string_form
  
          for v in key_value_or_just_value_tuple
      ]
  
      return fixed
  
  
  def init_wandb():
      pass
  
  
  def sort_like(inp, ref):
      len_ref = len(ref)
  
      return sorted(inp, key=lambda v: ref.index(v) if v in ref else len_ref)
  
  
  def sort_dict_like(inp_dict, ref_list):
      len_ref = len(ref_list)
      sorted_t = []
  
      for k in sorted(
              inp_dict,
              key=lambda v: ref_list.index(v) if v in ref_list else len_ref):
          sorted_t.append((k, inp_dict[k]))
  
      return OrderedDict(sorted_t)
  
  
  class WandbToAllenNLPTranslater(object):
      def __init__(self,
                   args: Optional[Dict[Any, Any]] = None,
                   pos_args: Optional[List[str]] = None,
                   expected_wandb_args: Optional[Dict[str, str]] = None,
                   order: Optional[List[str]] = None,
                   fixed_kwargs_args: Optional[List[Tuple[str, Any]]] = None,
                   filters: List[Callable] = None,
                   fixers: List[Callable] = None,
                   keep_prog_name=True):
          self.parser = argparse.ArgumentParser()
          self.keep_prog_name = keep_prog_name
  
          if fixed_kwargs_args is None:
              self.fixed_kwargs_args = {}
          else:
              self.fixed_kwargs_args = fixed_kwargs_args
  
          if args is not None:
              for arg_name, props in args.items():
                  self.parser.add_argument('--' + arg_name, **props)
  
          if filters is None:
              self.filters = [filter_level0, filter_drop_empty_values]
          else:
              self.filters = filters
  
          if fixers is None:
              self.fixers = [Fixes()]
          else:
              self.fixers = fixers
  
          if order is not None:
              self.reorder = lambda arr: sort_dict_like(arr, pos_args)
          else:
              self.reorder = None
  
          if pos_args is not None:
  
              def create_pos(k, v):
                  return (v, )
  
              self.reformater = PatterReformatterWithNameExceptions(
                  exceptions={name: create_pos
                              for name in pos_args})
          else:
              self.reformater = PatterReformatter()
          self.expected_wandb_args = expected_wandb_args
          self.target_args: Optional[List[str]] = None
          self.wandb_args: Optional[Dict[str, Any]] = None
  
      def translate(self, args: Optional[List[str]] = None) -> List[str]:
          parsed_args = create_dynamic_parser(
              known_args_parser=self.parser).parse_args(args)
          vars_dict = vars(parsed_args)
          # pop wandb args
          logger.debug("self.expected_wandb_args:{}".format(
              self.expected_wandb_args))
  
          if self.expected_wandb_args is not None:
              logger.debug("entered if")
              logger.debug("expected_wandb_args:{}".format(
                  self.expected_wandb_args))
              self.wandb_args = {
                  arg: vars_dict.pop(arg, default)
  
                  for arg, default in self.expected_wandb_args.items()
              }
          logger.debug("wandb_args: {}".format(self.wandb_args))
  
          transformed = apply_filters_on_dict(vars_dict, self.filters)
  
          reformated = reformat_as_required(
              transformed,
              reformater=self.reformater,
              fixers=self.fixers,
              reorder=self.reorder)
  
          # add fixed args
          fixed_args: List[str] = []
  
          for k, v in self.fixed_kwargs_args:
              fixed_args += ["--{}".format(k), "{}".format(v)]
          self.target_args = reformated + fixed_args
  
          return self.target_args
  
      def translate_and_replace(self, args: Optional[List[str]] = None):
          translated_list = self.translate(args)
  
          if self.keep_prog_name:
              prog = [sys.argv[0]]
          else:
              prog = []
  
          sys.argv = prog + translated_list
  
          return sys.argv
  
  
  def init_wandb():
      run = wandb.init()
      # add serialization dir if training
  
      if sys.argv[1] == 'train':
          sys.argv.append('--serialization-dir {}'.format(run.dir))
  
  
  def process_wandb_arg_value(arg: str, value: Any):
      if arg == 'tags':
          return value.split(",")
  
      if arg == 'tensorboard':
          if value:
              wandb.tensorboard.patch(save=True, tensorboardX=False)
  
          return None
  
  
  def setup_wandb(expected: Dict[str, Any], pos_args: List[str],
                  order: List[str], fixed_kwargs_args: List[Tuple[str, str]],
                  expected_wandb_args: Dict[str, Any]):
  
      # init wandb
      run = wandb.init()
      serialization_dir = Path('/tmp', run.id) /  'allennlp_serialization_dir'
      fixed_kwargs_args.append(('serialization-dir', str(serialization_dir)))
      translater = WandbToAllenNLPTranslater(
          expected,
          pos_args,
          expected_wandb_args=expected_wandb_args,
          order=order,
          fixed_kwargs_args=fixed_kwargs_args)
      translater.translate_and_replace()
      # use the wandb args on run
  
      for wandb_arg, value in translater.wandb_args.items():
          processed_val = process_wandb_arg_value(wandb_arg, value)
          logger.debug("setting {} on run object to {}".format(
              wandb_arg, processed_val))
          #setattr(run, wandb_arg, processed_val)
      # logger.debug("wandb.run object's {} attribute is {}".format(
      #        wandb_arg, getattr(wandb.run, wandb_arg)))
      #logger.debug("type of run: {}".format(type(run)))
      #logger.debug("dir of run: {}".format(dir(run)))
  
      return run
  
  
  def run():
      # arguments which SHOULD be present
      expected = {'local_config_file': dict(type=str)}
  
      # All the arguments from wandb server will be
      # named arguments. Following specifies which of these
      # to convert to positional arguments while passing them to allennlp
      # specify the arguments which are to be converted
      # example below will create:
      # $ allennlp <subcommand> <local_config_file> ...
      # concretely:
      # $ allennlp train configs/lstm_nli.jsonnet
      pos_args = ['subcommand', 'local_config_file']
  
      # if arguments have to follow a strict order, specify it here
      # Most likely, positions arguments should be required to follow a specific order
      order = ['subcommand', 'local_config_file', 'serialization-dir']
  
      # These are arguments which will not be sent by the wandb server
      # but allennlp always needs these
      fixed_kwargs_args = [('include-package', 'models'),
                           ('include-package', 'wandb_allennlp')]
      expected_wandb_args = {'tags': 'unspecified', 'tensorboard': False}
      setup_wandb(expected, pos_args, order, fixed_kwargs_args,
                  expected_wandb_args)
      allennlp_run()
  