






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					=	logging.getLogger(__name__)







def       lowercase__	(					A_:						str  ,      A_:						Optional[int]		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				=np.argmax(A_  ,      axis=1		)
       return np.sum(outputs == labels		)







def       lowercase__	(					A_:						Optional[int]		)	->				Dict:







       """simple docstring"""






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







def       lowercase__	(					A_:						Optional[Any]  ,      A_:						Dict  ,      A_:						Optional[Any]  ,      A_:						int  ,      A_:						Dict  ,      A_:						Optional[int]		)	->				Dict:







       """simple docstring"""






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







def       lowercase__	(					)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=argparse.ArgumentParser()
       parser.add_argument("""--model_name"""  ,      type=A_  ,      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=A_  ,      type=A_  ,      required=A_  ,      help="""The output directory where the model predictions and checkpoints will be written."""  ,      )
       parser.add_argument("""--train_dataset"""  ,      type=A_  ,      default=""""""		)
       parser.add_argument("""--eval_dataset"""  ,      type=A_  ,      default=""""""		)
       parser.add_argument("""--seed"""  ,      type=A_  ,      default=42		)
       parser.add_argument("""--num_train_epochs"""  ,      type=A_  ,      default=3		)
       parser.add_argument("""--train_batch_size"""  ,      type=A_  ,      default=8		)
       parser.add_argument("""--eval_batch_size"""  ,      type=A_  ,      default=16		)
       parser.add_argument("""--adam_epsilon"""  ,      default=1e-8  ,      type=A_  ,      help="""Epsilon for Adam optimizer."""		)
       parser.add_argument("""--max_grad_norm"""  ,      type=A_  ,      default=1		)
       parser.add_argument(
           """--max_steps"""  ,      default=-1  ,      type=A_  ,      help=(
               """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
           )  ,      )
       parser.add_argument(
           """--gradient_accumulation_steps"""  ,      type=A_  ,      default=1  ,      help="""Number of updates steps to accumulate before                        performing a backward/update pass."""  ,      )
       parser.add_argument("""--learning_rate"""  ,      type=A_  ,      default=6.25e-5		)
       parser.add_argument("""--warmup_steps"""  ,      default=0  ,      type=A_  ,      help="""Linear warmup over warmup_steps."""		)
       parser.add_argument("""--lr_schedule"""  ,      type=A_  ,      default="""warmup_linear"""		)
       parser.add_argument("""--weight_decay"""  ,      type=A_  ,      default=0.0_1		)
       parser.add_argument("""--lm_coef"""  ,      type=A_  ,      default=0.9		)
       parser.add_argument("""--n_valid"""  ,      type=A_  ,      default=374		)

       parser.add_argument("""--server_ip"""  ,      type=A_  ,      default=""""""  ,      help="""Can be used for distant debugging."""		)
       parser.add_argument("""--server_port"""  ,      type=A_  ,      default=""""""  ,      help="""Can be used for distant debugging."""		)
       __UpperCAmelCase     				=parser.parse_args()
       print(A_		)

       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=A_		)
              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		)

       __UpperCAmelCase     				=torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
       __UpperCAmelCase     				=torch.cuda.device_count()
       logger.info("""device: {}, n_gpu {}""".format(A_  ,      A_		)		)

       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
       __UpperCAmelCase     				=["""_start_""", """_delimiter_""", """_classify_"""]
       __UpperCAmelCase     				=OpenAIGPTTokenizer.from_pretrained(args.model_name		)
       tokenizer.add_tokens(A_		)
       __UpperCAmelCase     				=tokenizer.convert_tokens_to_ids(A_		)
       __UpperCAmelCase     				=OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name		)
       model.resize_token_embeddings(len(A_		)		)
       model.to(A_		)

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

       logger.info("""Encoding dataset..."""		)
       __UpperCAmelCase     				=load_rocstories_dataset(args.train_dataset		)
       __UpperCAmelCase     				=load_rocstories_dataset(args.eval_dataset		)
       __UpperCAmelCase     				=(train_dataset, eval_dataset)
       __UpperCAmelCase     				=tokenize_and_encode(A_		)

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

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

       __UpperCAmelCase     				=TensorDataset(*A_		)
       __UpperCAmelCase     				=RandomSampler(A_		)
       __UpperCAmelCase     				=DataLoader(A_  ,      sampler=A_  ,      batch_size=args.train_batch_size		)

       __UpperCAmelCase     				=TensorDataset(*A_		)
       __UpperCAmelCase     				=SequentialSampler(A_		)
       __UpperCAmelCase     				=DataLoader(A_  ,      sampler=A_  ,      batch_size=args.eval_batch_size		)

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

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

       if args.do_train:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs		)  ,      desc="""Epoch"""		):
                     __UpperCAmelCase     				=0
                     __UpperCAmelCase     				=0
                     __UpperCAmelCase     				=tqdm(A_  ,      desc="""Training"""		)
                     for step, batch in enumerate(A_		):
                            __UpperCAmelCase     				=tuple(t.to(A_		) for t in batch		)
                            __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=batch
                            __UpperCAmelCase     				=model(A_  ,      mc_token_ids=A_  ,      lm_labels=A_  ,      mc_labels=A_		)
                            __UpperCAmelCase     				=args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            __UpperCAmelCase     				=(
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            __UpperCAmelCase     				="""Training loss: {:.2e} lr: {:.2e}""".format(A_  ,      scheduler.get_lr()[0]		)

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

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

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

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

       if args.do_eval:
              model.eval()
              __UpperCAmelCase				, __UpperCAmelCase     				=0, 0
              __UpperCAmelCase				, __UpperCAmelCase     				=0, 0
              for batch in tqdm(A_  ,      desc="""Evaluating"""		):
                     __UpperCAmelCase     				=tuple(t.to(A_		) for t in batch		)
                     __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=batch
                     with torch.no_grad():
                            __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=model(
                                A_  ,      mc_token_ids=A_  ,      lm_labels=A_  ,      mc_labels=A_		)

                     __UpperCAmelCase     				=mc_logits.detach().cpu().numpy()
                     __UpperCAmelCase     				=mc_labels.to("""cpu"""		).numpy()
                     __UpperCAmelCase     				=accuracy(A_  ,      A_		)

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

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

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

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


if __name__ == "__main__":
   main()








def       a_ (       SCREAMING_SNAKE_CASE__   : bytes	):
       '''simple docstring'''





       return "".join([hex(SCREAMING_SNAKE_CASE__	)[2:].zfill(2	).upper() for byte in list(SCREAMING_SNAKE_CASE__	)]	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if (len(SCREAMING_SNAKE_CASE__	) % 2) != 0:
              raise ValueError(
                  'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'	)
       # Check the character set - the standard base16 alphabet
       # is uppercase according to RFC3548 section 6
       if not set(SCREAMING_SNAKE_CASE__	) <= set('0123456789ABCDEF'	):
              raise ValueError(
                  'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'	)
       # For every two hexadecimal digits (= a byte), turn it into an integer.
       # Then, string the result together into bytes, and return it.
       return bytes(int(data[i] + data[i + 1]						,     16	) for i in range(0						,     len(SCREAMING_SNAKE_CASE__	)						,     2	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


