





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
)
__A							:			Optional[Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							np.argmax(_SCREAMING_SNAKE_CASE							,     axis=1				)
    return np.sum(outputs == labels				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    with open(_SCREAMING_SNAKE_CASE							,     encoding='utf_8'				) as f:
        _A     =							csv.reader(_SCREAMING_SNAKE_CASE				)
        _A     =							[]
        next(_SCREAMING_SNAKE_CASE				)  # skip the first line
        for line in tqdm(_SCREAMING_SNAKE_CASE				):
            output.append((' '.join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
    return output





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							[]
    for dataset in encoded_datasets:
        _A     =							len(_SCREAMING_SNAKE_CASE				)
        _A     =							np.zeros((n_batch, 2, input_len)							,     dtype=np.intaa				)
        _A     =							np.zeros((n_batch, 2)							,     dtype=np.intaa				)
        _A     =							np.full((n_batch, 2, input_len)							,     fill_value=-100							,     dtype=np.intaa				)
        _A     =							np.zeros((n_batch,)							,     dtype=np.intaa				)
        for (
            i,
            (story, conta, conta, mc_label),
        ) in enumerate(_SCREAMING_SNAKE_CASE				):
            _A     =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            _A     =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            _A     =							with_conta
            _A     =							with_conta
            _A     =							len(_SCREAMING_SNAKE_CASE				) - 1
            _A     =							len(_SCREAMING_SNAKE_CASE				) - 1
            _A     =							with_conta
            _A     =							with_conta
            _A     =							mc_label
        _A     =							(input_ids, mc_token_ids, lm_labels, mc_labels)
        tensor_datasets.append(tuple(torch.tensor(_SCREAMING_SNAKE_CASE				) for t in all_inputs				)				)
    return tensor_datasets





def 				__lowerCAmelCase(       )							-> List[str]:




    """simple docstring"""




    _A     =							argparse.ArgumentParser()
    parser.add_argument('--model_name'							,     type=_SCREAMING_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=_SCREAMING_SNAKE_CASE							,     type=_SCREAMING_SNAKE_CASE							,     required=_SCREAMING_SNAKE_CASE							,     help='The output directory where the model predictions and checkpoints will be written.'							,     )
    parser.add_argument('--train_dataset'							,     type=_SCREAMING_SNAKE_CASE							,     default=''				)
    parser.add_argument('--eval_dataset'							,     type=_SCREAMING_SNAKE_CASE							,     default=''				)
    parser.add_argument('--seed'							,     type=_SCREAMING_SNAKE_CASE							,     default=42				)
    parser.add_argument('--num_train_epochs'							,     type=_SCREAMING_SNAKE_CASE							,     default=3				)
    parser.add_argument('--train_batch_size'							,     type=_SCREAMING_SNAKE_CASE							,     default=8				)
    parser.add_argument('--eval_batch_size'							,     type=_SCREAMING_SNAKE_CASE							,     default=16				)
    parser.add_argument('--adam_epsilon'							,     default=1e-8							,     type=_SCREAMING_SNAKE_CASE							,     help='Epsilon for Adam optimizer.'				)
    parser.add_argument('--max_grad_norm'							,     type=_SCREAMING_SNAKE_CASE							,     default=1				)
    parser.add_argument(
        '--max_steps'							,     default=-1							,     type=_SCREAMING_SNAKE_CASE							,     help=(
            'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
        )							,     )
    parser.add_argument(
        '--gradient_accumulation_steps'							,     type=_SCREAMING_SNAKE_CASE							,     default=1							,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'							,     )
    parser.add_argument('--learning_rate'							,     type=_SCREAMING_SNAKE_CASE							,     default=6.25e-5				)
    parser.add_argument('--warmup_steps'							,     default=0							,     type=_SCREAMING_SNAKE_CASE							,     help='Linear warmup over warmup_steps.'				)
    parser.add_argument('--lr_schedule'							,     type=_SCREAMING_SNAKE_CASE							,     default='warmup_linear'				)
    parser.add_argument('--weight_decay'							,     type=_SCREAMING_SNAKE_CASE							,     default=0.01				)
    parser.add_argument('--lm_coef'							,     type=_SCREAMING_SNAKE_CASE							,     default=0.9				)
    parser.add_argument('--n_valid'							,     type=_SCREAMING_SNAKE_CASE							,     default=374				)

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

    _A     =							torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
    _A     =							torch.cuda.device_count()
    logger.info('device: {}, n_gpu {}'.format(_SCREAMING_SNAKE_CASE							,     _SCREAMING_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
    _A     =							['_start_', '_delimiter_', '_classify_']
    _A     =							OpenAIGPTTokenizer.from_pretrained(args.model_name				)
    tokenizer.add_tokens(_SCREAMING_SNAKE_CASE				)
    _A     =							tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
    _A     =							OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
    model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE				)				)
    model.to(_SCREAMING_SNAKE_CASE				)

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

    logger.info('Encoding dataset...'				)
    _A     =							load_rocstories_dataset(args.train_dataset				)
    _A     =							load_rocstories_dataset(args.eval_dataset				)
    _A     =							(train_dataset, eval_dataset)
    _A     =							tokenize_and_encode(_SCREAMING_SNAKE_CASE				)

    # Compute the max input length for the Transformer
    _A     =							model.config.n_positions // 2 - 2
    _A     =							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				)
    _A     =							min(_SCREAMING_SNAKE_CASE							,     model.config.n_positions				)  # Max size of input for the pre-trained model

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

    _A     =							TensorDataset(*_SCREAMING_SNAKE_CASE				)
    _A     =							RandomSampler(_SCREAMING_SNAKE_CASE				)
    _A     =							DataLoader(_SCREAMING_SNAKE_CASE							,     sampler=_SCREAMING_SNAKE_CASE							,     batch_size=args.train_batch_size				)

    _A     =							TensorDataset(*_SCREAMING_SNAKE_CASE				)
    _A     =							SequentialSampler(_SCREAMING_SNAKE_CASE				)
    _A     =							DataLoader(_SCREAMING_SNAKE_CASE							,     sampler=_SCREAMING_SNAKE_CASE							,     batch_size=args.eval_batch_size				)

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

        _A     =							list(model.named_parameters()				)
        _A     =							['bias', 'LayerNorm.bias', 'LayerNorm.weight']
        _A     =							[
            {
                '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},
        ]
        _A     =							AdamW(_SCREAMING_SNAKE_CASE							,     lr=args.learning_rate							,     eps=args.adam_epsilon				)
        _A     =							get_linear_schedule_with_warmup(
            _SCREAMING_SNAKE_CASE							,     num_warmup_steps=args.warmup_steps							,     num_training_steps=_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

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

            _A     =							mc_logits.detach().cpu().numpy()
            _A     =							mc_labels.to('cpu'				).numpy()
            _A     =							accuracy(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

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

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

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

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


if __name__ == "__main__":
 main()












import numpy as np





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> np.array:




    """simple docstring"""




    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





