





import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
snake_case 				=       logging.getLogger(__name__)

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Union[str, Any]     )      ->						Optional[Any]:
  _lowercase							= np.argmax(snake_case__			,							axis=1     )
  return np.sum(outputs == labels     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						Union[str, Any]:
  with open(snake_case__			,							encoding='utf_8'     ) as f:
    _lowercase							= csv.reader(snake_case__     )
    _lowercase							= []
    next(snake_case__     )  # skip the first line
    for line in tqdm(snake_case__     ):
      output.append((' '.join(line[1:5]     ), line[5], line[6], int(line[-1]     ) - 1)     )
  return output

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Dict			,							snake_case__  :Any			,							snake_case__  :List[str]			,							snake_case__  :int			,							snake_case__  :Optional[int]     )      ->						Any:
  _lowercase							= []
  for dataset in encoded_datasets:
    _lowercase							= len(snake_case__     )
    _lowercase							= np.zeros((n_batch, 2, input_len)			,							dtype=np.intaa     )
    _lowercase							= np.zeros((n_batch, 2)			,							dtype=np.intaa     )
    _lowercase							= np.full((n_batch, 2, input_len)			,							fill_value=-100			,							dtype=np.intaa     )
    _lowercase							= np.zeros((n_batch,)			,							dtype=np.intaa     )
    for (
        i,
        (story, conta, conta, mc_label),
    ) in enumerate(snake_case__     ):
      _lowercase							= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      _lowercase							= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      _lowercase							= with_conta
      _lowercase							= with_conta
      _lowercase							= len(snake_case__     ) - 1
      _lowercase							= len(snake_case__     ) - 1
      _lowercase							= with_conta
      _lowercase							= with_conta
      _lowercase							= mc_label
    _lowercase							= (input_ids, mc_token_ids, lm_labels, mc_labels)
    tensor_datasets.append(tuple(torch.tensor(snake_case__     ) for t in all_inputs     )     )
  return tensor_datasets

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Tuple:
  _lowercase							= argparse.ArgumentParser()
  parser.add_argument('--model_name'			,							type=snake_case__			,							default='openai-gpt'			,							help='pretrained model name'     )
  parser.add_argument('--do_train'			,							action='store_true'			,							help='Whether to run training.'     )
  parser.add_argument('--do_eval'			,							action='store_true'			,							help='Whether to run eval on the dev set.'     )
  parser.add_argument(
      '--output_dir'			,							default=snake_case__			,							type=snake_case__			,							required=snake_case__			,							help='The output directory where the model predictions and checkpoints will be written.'			,							)
  parser.add_argument('--train_dataset'			,							type=snake_case__			,							default=''     )
  parser.add_argument('--eval_dataset'			,							type=snake_case__			,							default=''     )
  parser.add_argument('--seed'			,							type=snake_case__			,							default=42     )
  parser.add_argument('--num_train_epochs'			,							type=snake_case__			,							default=3     )
  parser.add_argument('--train_batch_size'			,							type=snake_case__			,							default=8     )
  parser.add_argument('--eval_batch_size'			,							type=snake_case__			,							default=16     )
  parser.add_argument('--adam_epsilon'			,							default=1E-8			,							type=snake_case__			,							help='Epsilon for Adam optimizer.'     )
  parser.add_argument('--max_grad_norm'			,							type=snake_case__			,							default=1     )
  parser.add_argument(
      '--max_steps'			,							default=-1			,							type=snake_case__			,							help=(
          'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
      )			,							)
  parser.add_argument(
      '--gradient_accumulation_steps'			,							type=snake_case__			,							default=1			,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'			,							)
  parser.add_argument('--learning_rate'			,							type=snake_case__			,							default=6.25E-5     )
  parser.add_argument('--warmup_steps'			,							default=0			,							type=snake_case__			,							help='Linear warmup over warmup_steps.'     )
  parser.add_argument('--lr_schedule'			,							type=snake_case__			,							default='warmup_linear'     )
  parser.add_argument('--weight_decay'			,							type=snake_case__			,							default=0.01     )
  parser.add_argument('--lm_coef'			,							type=snake_case__			,							default=0.9     )
  parser.add_argument('--n_valid'			,							type=snake_case__			,							default=374     )

  parser.add_argument('--server_ip'			,							type=snake_case__			,							default=''			,							help='Can be used for distant debugging.'     )
  parser.add_argument('--server_port'			,							type=snake_case__			,							default=''			,							help='Can be used for distant debugging.'     )
  _lowercase							= parser.parse_args()
  print(snake_case__     )

  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=snake_case__     )
    ptvsd.wait_for_attach()

  random.seed(args.seed     )
  np.random.seed(args.seed     )
  torch.manual_seed(args.seed     )
  torch.cuda.manual_seed_all(args.seed     )

  _lowercase							= torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
  _lowercase							= torch.cuda.device_count()
  logger.info('device: {}, n_gpu {}'.format(snake_case__			,							snake_case__     )     )

  if not args.do_train and not args.do_eval:
    raise ValueError('At least one of `do_train` or `do_eval` must be True.'     )

  if not os.path.exists(args.output_dir     ):
    os.makedirs(args.output_dir     )

  # Load tokenizer and model
  # This loading functions also add new tokens and embeddings called `special tokens`
  # These new embeddings will be fine-tuned on the RocStories dataset
  _lowercase							= ['_start_', '_delimiter_', '_classify_']
  _lowercase							= OpenAIGPTTokenizer.from_pretrained(args.model_name     )
  tokenizer.add_tokens(snake_case__     )
  _lowercase							= tokenizer.convert_tokens_to_ids(snake_case__     )
  _lowercase							= OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name     )
  model.resize_token_embeddings(len(snake_case__     )     )
  model.to(snake_case__     )

  # Load and encode the datasets
  def tokenize_and_encode(snake_case__  :Tuple     ):
    if isinstance(snake_case__			,							snake_case__     ):
      return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case__     )     )
    elif isinstance(snake_case__			,							snake_case__     ):
      return obj
    return [tokenize_and_encode(snake_case__     ) for o in obj]

  logger.info('Encoding dataset...'     )
  _lowercase							= load_rocstories_dataset(args.train_dataset     )
  _lowercase							= load_rocstories_dataset(args.eval_dataset     )
  _lowercase							= (train_dataset, eval_dataset)
  _lowercase							= tokenize_and_encode(snake_case__     )

  # Compute the max input length for the Transformer
  _lowercase							= model.config.n_positions // 2 - 2
  _lowercase							= max(
      len(story[:max_length]     ) + max(len(conta[:max_length]     )			,							len(conta[:max_length]     )     ) + 3
      for dataset in encoded_datasets
      for story, conta, conta, _ in dataset     )
  _lowercase							= min(snake_case__			,							model.config.n_positions     )  # Max size of input for the pre-trained model

  # Prepare inputs tensors and dataloaders
  _lowercase							= pre_process_datasets(snake_case__			,							snake_case__			,							snake_case__			,							*snake_case__     )
  _lowercase						,		_lowercase							= tensor_datasets[0], tensor_datasets[1]

  _lowercase							= TensorDataset(*snake_case__     )
  _lowercase							= RandomSampler(snake_case__     )
  _lowercase							= DataLoader(snake_case__			,							sampler=snake_case__			,							batch_size=args.train_batch_size     )

  _lowercase							= TensorDataset(*snake_case__     )
  _lowercase							= SequentialSampler(snake_case__     )
  _lowercase							= DataLoader(snake_case__			,							sampler=snake_case__			,							batch_size=args.eval_batch_size     )

  # Prepare optimizer
  if args.do_train:
    if args.max_steps > 0:
      _lowercase							= args.max_steps
      _lowercase							= args.max_steps // (len(snake_case__     ) // args.gradient_accumulation_steps) + 1
    else:
      _lowercase							= len(snake_case__     ) // args.gradient_accumulation_steps * args.num_train_epochs

    _lowercase							= list(model.named_parameters()     )
    _lowercase							= ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    _lowercase							= [
        {
            'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay     )],
            'weight_decay': args.weight_decay,
        },
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay     )], 'weight_decay': 0.0},
    ]
    _lowercase							= AdamW(snake_case__			,							lr=args.learning_rate			,							eps=args.adam_epsilon     )
    _lowercase							= get_linear_schedule_with_warmup(
        snake_case__			,							num_warmup_steps=args.warmup_steps			,							num_training_steps=snake_case__     )

  if args.do_train:
    _lowercase						,		_lowercase						,		_lowercase							= 0, 0, None
    model.train()
    for _ in trange(int(args.num_train_epochs     )			,							desc='Epoch'     ):
      _lowercase							= 0
      _lowercase							= 0
      _lowercase							= tqdm(snake_case__			,							desc='Training'     )
      for step, batch in enumerate(snake_case__     ):
        _lowercase							= tuple(t.to(snake_case__     ) for t in batch     )
        _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= batch
        _lowercase							= model(snake_case__			,							mc_token_ids=snake_case__			,							lm_labels=snake_case__			,							mc_labels=snake_case__     )
        _lowercase							= args.lm_coef * losses[0] + losses[1]
        loss.backward()
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
        tr_loss += loss.item()
        _lowercase							= (
            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
        )
        nb_tr_steps += 1
        _lowercase							= 'Training loss: {:.2e} lr: {:.2e}'.format(snake_case__			,							scheduler.get_lr()[0]     )

    # Save a trained model
  if args.do_train:
    # Save a trained model, configuration and tokenizer
    _lowercase							= model.module if hasattr(snake_case__			,							'module'     ) else model  # Only save the model itself

    # If we save using the predefined names, we can load using `from_pretrained`
    _lowercase							= os.path.join(args.output_dir			,							snake_case__     )
    _lowercase							= os.path.join(args.output_dir			,							snake_case__     )

    torch.save(model_to_save.state_dict()			,							snake_case__     )
    model_to_save.config.to_json_file(snake_case__     )
    tokenizer.save_vocabulary(args.output_dir     )

    # Load a trained model and vocabulary that you have fine-tuned
    _lowercase							= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
    _lowercase							= OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
    model.to(snake_case__     )

  if args.do_eval:
    model.eval()
    _lowercase						,		_lowercase							= 0, 0
    _lowercase						,		_lowercase							= 0, 0
    for batch in tqdm(snake_case__			,							desc='Evaluating'     ):
      _lowercase							= tuple(t.to(snake_case__     ) for t in batch     )
      _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= batch
      with torch.no_grad():
        _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= model(
            snake_case__			,							mc_token_ids=snake_case__			,							lm_labels=snake_case__			,							mc_labels=snake_case__     )

      _lowercase							= mc_logits.detach().cpu().numpy()
      _lowercase							= mc_labels.to('cpu'     ).numpy()
      _lowercase							= accuracy(snake_case__			,							snake_case__     )

      eval_loss += mc_loss.mean().item()
      eval_accuracy += tmp_eval_accuracy

      nb_eval_examples += input_ids.size(0     )
      nb_eval_steps += 1

    _lowercase							= eval_loss / nb_eval_steps
    _lowercase							= eval_accuracy / nb_eval_examples
    _lowercase							= tr_loss / nb_tr_steps if args.do_train else None
    _lowercase							= {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

    _lowercase							= os.path.join(args.output_dir			,							'eval_results.txt'     )
    with open(snake_case__			,							'w'     ) as writer:
      logger.info('***** Eval results *****'     )
      for key in sorted(result.keys()     ):
        logger.info('  %s = %s'			,							snake_case__			,							str(result[key]     )     )
        writer.write('%s = %s\n' % (key, str(result[key]     ))     )


if __name__ == "__main__":
      main()






from itertools import count

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int = 50     )      ->						int:
  _lowercase							= [1] * min_block_length

  for n in count(snake_case__     ):
    fill_count_functions.append(1     )

    for block_length in range(snake_case__			,							n + 1     ):
      for block_start in range(n - block_length     ):
        fill_count_functions[n] += fill_count_functions[
            n - block_start - block_length - 1
        ]

      fill_count_functions[n] += 1

    if fill_count_functions[n] > 100_0000:
      break

  return n


if __name__ == "__main__":
      print(F"""{solution() = }""")