

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowercase_             =     NewType('DataClass', Any)
lowercase_             =     NewType('DataClassType', Any)
def   a   (    A__			:     int  )					->   Dict:





      """simple docstring"""



      if isinstance(A__   ,     A__  ):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise ArgumentTypeError(
                F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''  )
def   a   (    A__			:     list  )					->   Callable[[str], Any]:





      """simple docstring"""



      _lowercase			 ={str(A__  ): choice for choice in choices}
      return lambda A__  : str_to_choice.get(A__   ,     A__  )
def   a   (    *,
    A__			:     Union[str, List[str]] = None   ,     A__			:     str = None   ,     A__			:     Any = dataclasses.MISSING   ,     A__			:     Callable[[], Any] = dataclasses.MISSING   ,     A__			:     dict = None   ,     **A__			:     int   ,     )					->   dataclasses.Field:





      """simple docstring"""



      if metadata is None:
            # Important, don't use as default param in function signature because dict is mutable and shared across function calls
            _lowercase			 ={}
      if aliases is not None:
            _lowercase			 =aliases
      if help is not None:
            _lowercase			 =help

      return dataclasses.field(metadata=A__   ,     default=A__   ,     default_factory=A__   ,     **A__  )







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						42




  def __init__(       self							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        if "formatter_class" not in kwargs:
              _lowercase			 =ArgumentDefaultsHelpFormatter
        super().__init__(**lowerCAmelCase      )
        if dataclasses.is_dataclass(lowerCAmelCase      ):
              _lowercase			 =[dataclass_types]
        _lowercase			 =list(lowerCAmelCase      )
        for dtype in self.dataclass_types:
              self._add_dataclass_arguments(lowerCAmelCase      )




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =F'''--{field.name}'''
        _lowercase			 =field.metadata.copy()
        # field.metadata is not used at all by Data Classes,
        # it is provided as a third-party extension mechanism.
        if isinstance(field.type							,     lowerCAmelCase      ):
              raise RuntimeError(
                  'Unresolved type detected, which should have been done with the help of '
                  '`typing.get_type_hints` method by default'      )

        _lowercase			 =kwargs.pop('aliases'							,     []      )
        if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =[aliases]

        _lowercase			 =getattr(field.type							,     '__origin__'							,     field.type      )
        if origin_type is Union or (hasattr(lowerCAmelCase							,     'UnionType'      ) and isinstance(lowerCAmelCase							,     types.UnionType      )):
              if str not in field.type.__args__ and (
                  len(field.type.__args__      ) != 2 or type(lowerCAmelCase      ) not in field.type.__args__
              ):
                    raise ValueError(
                        'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
                        ' the argument parser only supports one type per argument.'
                        F''' Problem encountered in field \'{field.name}\'.'''      )
              if type(lowerCAmelCase      ) not in field.type.__args__:
                    # filter `str` in Union
                    _lowercase			 =field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                    _lowercase			 =getattr(field.type							,     '__origin__'							,     field.type      )
              elif bool not in field.type.__args__:
                    # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                    _lowercase			 =(
                        field.type.__args__[0] if isinstance(lowerCAmelCase							,     field.type.__args__[1]      ) else field.type.__args__[1]
                    )
                    _lowercase			 =getattr(field.type							,     '__origin__'							,     field.type      )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
        _lowercase			 ={}
        if origin_type is Literal or (isinstance(field.type							,     lowerCAmelCase      ) and issubclass(field.type							,     lowerCAmelCase      )):
              if origin_type is Literal:
                    _lowercase			 =field.type.__args__
              else:
                    _lowercase			 =[x.value for x in field.type]

              _lowercase			 =make_choice_type_function(kwargs['choices']      )

              if field.default is not dataclasses.MISSING:
                    _lowercase			 =field.default
              else:
                    _lowercase			 =True
        elif field.type is bool or field.type == Optional[bool]:
              # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
              # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
              _lowercase			 =copy(lowerCAmelCase      )

              # Hack because type=bool in argparse does not behave as we want.
              _lowercase			 =string_to_bool
              if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                    # Default value is False if we have no default when of type bool.
                    _lowercase			 =False if field.default is dataclasses.MISSING else field.default
                    # This is the value that will get picked if we don't include --field_name in any way
                    _lowercase			 =default
                    # This tells argparse we accept 0 or 1 value after --field_name
                    _lowercase			 ='?'
                    # This is the value that will get picked if we do --field_name (without value)
                    _lowercase			 =True
        elif isclass(lowerCAmelCase      ) and issubclass(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =field.type.__args__[0]
              _lowercase			 ='+'
              if field.default_factory is not dataclasses.MISSING:
                    _lowercase			 =field.default_factory()
              elif field.default is dataclasses.MISSING:
                    _lowercase			 =True
        else:
              _lowercase			 =field.type
              if field.default is not dataclasses.MISSING:
                    _lowercase			 =field.default
              elif field.default_factory is not dataclasses.MISSING:
                    _lowercase			 =field.default_factory()
              else:
                    _lowercase			 =True
        parser.add_argument(lowerCAmelCase							,     *lowerCAmelCase							,     **lowerCAmelCase      )

        # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
        # Order is important for arguments with the same destination!
        # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
        # here and we do not need those changes/additional keys.
        if field.default is True and (field.type is bool or field.type == Optional[bool]):
              _lowercase			 =False
              parser.add_argument(F'''--no_{field.name}'''							,     action='store_false'							,     dest=field.name							,     **lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






        if hasattr(lowerCAmelCase							,     '_argument_group_name'      ):
              _lowercase			 =self.add_argument_group(dtype._argument_group_name      )
        else:
              _lowercase			 =self

        try:
              _lowercase			 =get_type_hints(lowerCAmelCase      )
        except NameError:
              raise RuntimeError(
                  F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
                  'removing line of `from __future__ import annotations` which opts in Postponed '
                  'Evaluation of Annotations (PEP 563)'      )
        except TypeError as ex:
              # Remove this block when we drop Python 3.9 support
              if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowerCAmelCase      ):
                    _lowercase			 ='.'.join(map(lowerCAmelCase							,     sys.version_info[:3]      )      )
                    raise RuntimeError(
                        F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
                        'line of `from __future__ import annotations` which opts in union types as '
                        '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
                        'support Python versions that lower than 3.10, you need to use '
                        '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
                        '`X | None`.'      ) from ex
              raise

        for field in dataclasses.fields(lowerCAmelCase      ):
              if not field.init:
                    continue
              _lowercase			 =type_hints[field.name]
              self._parse_dataclass_field(lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase=None							,     lowerCAmelCase=False							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     )						->				Tuple[DataClass, ...]:
        '''simple docstring'''






        if args_file_flag or args_filename or (look_for_args_file and len(sys.argv      )):
              _lowercase			 =[]

              if args_filename:
                    args_files.append(Path(lowerCAmelCase      )      )
              elif look_for_args_file and len(sys.argv      ):
                    args_files.append(Path(sys.argv[0]      ).with_suffix('.args'      )      )

              # args files specified via command line flag should overwrite default args files so we add them last
              if args_file_flag:
                    # Create special parser just to extract the args_file_flag values
                    _lowercase			 =ArgumentParser()
                    args_file_parser.add_argument(lowerCAmelCase							,     type=lowerCAmelCase							,     action='append'      )

                    # Use only remaining args for further parsing (remove the args_file_flag)
                    _lowercase , _lowercase			 =args_file_parser.parse_known_args(args=lowerCAmelCase      )
                    _lowercase			 =vars(lowerCAmelCase      ).get(args_file_flag.lstrip('-'      )							,     lowerCAmelCase      )

                    if cmd_args_file_paths:
                          args_files.extend([Path(lowerCAmelCase      ) for p in cmd_args_file_paths]      )

              _lowercase			 =[]
              for args_file in args_files:
                    if args_file.exists():
                          file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
              _lowercase			 =file_args + args if args is not None else file_args + sys.argv[1:]
        _lowercase , _lowercase			 =self.parse_known_args(args=lowerCAmelCase      )
        _lowercase			 =[]
        for dtype in self.dataclass_types:
              _lowercase			 ={f.name for f in dataclasses.fields(lowerCAmelCase      ) if f.init}
              _lowercase			 ={k: v for k, v in vars(lowerCAmelCase      ).items() if k in keys}
              for k in keys:
                    delattr(lowerCAmelCase							,     lowerCAmelCase      )
              _lowercase			 =dtype(**lowerCAmelCase      )
              outputs.append(lowerCAmelCase      )
        if len(namespace.__dict__      ) > 0:
              # additional namespace.
              outputs.append(lowerCAmelCase      )
        if return_remaining_strings:
              return (*outputs, remaining_args)
        else:
              if remaining_args:
                    raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''      )

              return (*outputs,)




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				Tuple[DataClass, ...]:
        '''simple docstring'''






        _lowercase			 =set(args.keys()      )
        _lowercase			 =[]
        for dtype in self.dataclass_types:
              _lowercase			 ={f.name for f in dataclasses.fields(lowerCAmelCase      ) if f.init}
              _lowercase			 ={k: v for k, v in args.items() if k in keys}
              unused_keys.difference_update(inputs.keys()      )
              _lowercase			 =dtype(**lowerCAmelCase      )
              outputs.append(lowerCAmelCase      )
        if not allow_extra_keys and unused_keys:
              raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(lowerCAmelCase      )}'''      )
        return tuple(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				Tuple[DataClass, ...]:
        '''simple docstring'''






        with open(Path(lowerCAmelCase      )							,     encoding='utf-8'      ) as open_json_file:
              _lowercase			 =json.loads(open_json_file.read()      )
        _lowercase			 =self.parse_dict(lowerCAmelCase							,     allow_extra_keys=lowerCAmelCase      )
        return tuple(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				Tuple[DataClass, ...]:
        '''simple docstring'''






        _lowercase			 =self.parse_dict(yaml.safe_load(Path(lowerCAmelCase      ).read_text()      )							,     allow_extra_keys=lowerCAmelCase      )
        return tuple(lowerCAmelCase      )





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def   a   (    A__			:     Optional[int]=32   ,     A__			:     str=10   ,     A__			:     Tuple=100   ,     A__			:     Union[str, Any]=1026   ,     A__			:     Tuple=True   ,     A__			:     Dict="data/tokenized_stories_train_wikitext103.jbl"   ,     A__			:     Dict="igf_context_pairs.jbl"   ,     )					->   Optional[int]:





      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      _lowercase , _lowercase			 =generate_datasets(
          A__   ,     A__   ,     number=A__   ,     min_len=1026   ,     trim=A__  )
      # keeps model same across runs
      set_seed(4  )
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      _lowercase			 =load_gpta('gpt2'  ).to(A__  )
      print('computing perplexity on objective set'  )
      _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  ).item()
      print('perplexity on objective set:'   ,     A__  )

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__  )

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()
def   a   (    A__			:     str   ,     A__			:     str=15   ,     A__			:     Optional[int]=128   ,     A__			:     Dict=100   ,     A__			:     Optional[Any]="igf_model.pt"   ,     )					->   int:





      """simple docstring"""



      set_seed(42  )

      # Load pre-trained model
      _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      _lowercase			 =SecondaryLearner(A__  )

      # Train secondary learner
      _lowercase			 =train_secondary_learner(
          A__   ,     A__   ,     max_epochs=A__   ,     batch_size=A__   ,     eval_freq=100   ,     igf_model_path=A__   ,     )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     str   ,     A__			:     Union[str, Any]=32   ,     A__			:     List[str]=1000   ,     A__			:     int=16   ,     A__			:     List[str]=1.0   ,     A__			:     Tuple=recopy_gpta   ,     A__			:     Any=None   ,     A__			:     Tuple=10   ,     A__			:     Tuple="gpt2_finetuned.pt"   ,     )					->   str:





      """simple docstring"""



      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      _lowercase			 =RandomSampler(A__  )
      _lowercase			 =DataLoader(A__   ,     sampler=A__  )

      _lowercase			 =max_steps // (len(A__  )) + 1
      _lowercase			 =0
      _lowercase			 =torch.zeros((1, context_len)   ,     dtype=torch.long   ,     device=A__  )
      _lowercase , _lowercase , _lowercase			 =recopy_model(A__   ,     A__   ,     A__  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(A__  )
            secondary_learner.eval()
      _lowercase			 =[]
      _lowercase			 =0

      _lowercase			 =[]
      _lowercase			 =[]

      # Compute the performance of the transformer model at the beginning
      _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
      test_perps.append(A__  )
      print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
      for epoch in range(int(A__  )  ):
            for step, example in enumerate(A__  ):
                  torch.cuda.empty_cache()
                  _lowercase			 =random.randint(0   ,     example.size(2  ) - context_len - 1  )
                  _lowercase			 =example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  _lowercase			 =model(A__   ,     labels=A__  )
                  _lowercase			 =True

                  if secondary_learner is not None:
                        _lowercase			 =secondary_learner.forward(
                            torch.tensor(A__   ,     dtype=torch.long   ,     device=A__  ).unsqueeze(0  )  )[0].item()
                        observed_qs.append(float(A__  )  )

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 10:
                              _lowercase			 =-1
                        if predicted_q < threshold:
                              _lowercase			 =False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        _lowercase			 =outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        _lowercase			 =0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()   ,     3.0  )
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
                              test_perps.append(A__  )

                              print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 60:
                        break
            if max_steps > 0 and global_step > 60:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()   ,     A__  )
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model
def   a   (    )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The input data dir. Should contain data files for WikiText.'   ,     )
      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(
          '--data_file'   ,     type=A__   ,     default=A__   ,     help=(
              'A jbl file containing tokenized data which can be split as objective dataset, '
              'train_dataset and test_dataset.'
          )   ,     )

      parser.add_argument(
          '--igf_data_file'   ,     type=A__   ,     default=A__   ,     help='A jbl file containing the context and information gain pairs to train secondary learner.'   ,     )

      parser.add_argument(
          '--output_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The output directory where the final fine-tuned model is stored.'   ,     )

      parser.add_argument(
          '--tokenizer_name'   ,     default=A__   ,     type=A__   ,     help='Pretrained tokenizer name or path if not the same as model_name'   ,     )
      parser.add_argument('--seed'   ,     type=A__   ,     default=A__   ,     help='A seed for reproducible training.'  )

      parser.add_argument(
          '--context_len'   ,     default=32   ,     type=A__   ,     help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )   ,     )

      parser.add_argument(
          '--size_objective_set'   ,     default=100   ,     type=A__   ,     help='number of articles that are long enough to be used as our objective set'   ,     )
      parser.add_argument(
          '--eval_freq'   ,     default=100   ,     type=A__   ,     help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'   ,     default=1000   ,     type=A__   ,     help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'   ,     default=128   ,     type=A__   ,     help='batch size of training data for secondary learner'   ,     )

      parser.add_argument(
          '--batch_size'   ,     default=16   ,     type=A__   ,     help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'   ,     default=10   ,     type=A__   ,     help=(
              'decay the selectivity of our secondary learner filter from'
              '1 standard deviation above average to 1 below average after 10 batches'
          )   ,     )

      parser.add_argument(
          '--number'   ,     default=100   ,     type=A__   ,     help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'   ,     default=1026   ,     type=A__   ,     help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'   ,     default=15   ,     type=A__   ,     help='number of epochs to train secondary learner'  )

      parser.add_argument('--trim'   ,     default=A__   ,     type=A__   ,     help='truncate the example if it exceeds context length'  )

      parser.add_argument(
          '--threshold'   ,     default=1.0   ,     type=A__   ,     help=(
              'The threshold value used by secondary learner to filter the train_data and allow only'
              ' informative data as input to the model'
          )   ,     )

      parser.add_argument('--finetuned_model_name'   ,     default='gpt2_finetuned.pt'   ,     type=A__   ,     help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'   ,     default=A__   ,     type=A__   ,     help='Reset the model to the original pretrained GPT-2 weights after each iteration'   ,     )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=32   ,     max_steps=10   ,     size_objective_set=100   ,     min_len=1026   ,     trim=A__   ,     data_file='data/tokenized_stories_train_wikitext103.jbl'   ,     igf_data_file='igf_context_pairs.jbl'   ,     )

      # Load train data for secondary learner
      _lowercase			 =joblib.load('data/IGF_values.jbl'  )

      # Train secondary learner
      _lowercase			 =training_secondary_learner(
          A__   ,     secondary_learner_max_epochs=15   ,     secondary_learner_batch_size=128   ,     eval_freq=100   ,     igf_model_path='igf_model.pt'   ,     )

      # load pretrained gpt2 model
      _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(42  )

      # Generate train and test data to train and evaluate gpt2 model
      _lowercase , _lowercase			 =generate_datasets(
          context_len=32   ,     file='data/tokenized_stories_train_wikitext103.jbl'   ,     number=100   ,     min_len=1026   ,     trim=A__  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          A__   ,     A__   ,     A__   ,     context_len=32   ,     max_steps=1000   ,     batch_size=16   ,     threshold=1.0   ,     recopy_model=A__   ,     secondary_learner=A__   ,     eval_interval=10   ,     finetuned_model_name='gpt2_finetuned.pt'   ,     )


if __name__ == "__main__":
  main()


