


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
)
__lowerCAmelCase		       =							logging.getLogger(__name__)




def    snake_case_     (  snake_case			,							snake_case     )  ->				Any:
       lowercase__:   Optional[Any]  			=   np.argmax(snake_case			,							axis=1     )
       return np.sum(outputs == labels     )




def    snake_case_     (  snake_case     )  ->				Optional[Any]:
       with open(snake_case			,							encoding='utf_8'     ) as f:
              lowercase__:   Any  			=   csv.reader(snake_case     )
              lowercase__:   Optional[int]  			=   []
              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    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Optional[int]:
       lowercase__:   Tuple  			=   []
       for dataset in encoded_datasets:
              lowercase__:   Dict  			=   len(snake_case     )
              lowercase__:   Union[str, Any]  			=   np.zeros((n_batch, 2, input_len)			,							dtype=np.intaa     )
              lowercase__:   str  			=   np.zeros((n_batch, 2)			,							dtype=np.intaa     )
              lowercase__:   Optional[Any]  			=   np.full((n_batch, 2, input_len)			,							fill_value=-1_00			,							dtype=np.intaa     )
              lowercase__:   Dict  			=   np.zeros((n_batch,)			,							dtype=np.intaa     )
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:   Optional[int]  			=   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:   Optional[Any]  			=   with_conta
                     lowercase__:   Union[str, Any]  			=   with_conta
                     lowercase__:   Dict  			=   len(snake_case     ) - 1
                     lowercase__:   Dict  			=   len(snake_case     ) - 1
                     lowercase__:   Optional[Any]  			=   with_conta
                     lowercase__:   Any  			=   with_conta
                     lowercase__:   Any  			=   mc_label
              lowercase__:   str  			=   (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    snake_case_     (  )  ->				List[str]:
       lowercase__:   Optional[int]  			=   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=0.0_1     )
       parser.add_argument('--lm_coef'			,							type=snake_case			,							default=0.9     )
       parser.add_argument('--n_valid'			,							type=snake_case			,							default=3_74     )

       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__:   Optional[int]  			=   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__:   List[Any]  			=   torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Tuple  			=   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__:   Optional[int]  			=   ['_start_', '_delimiter_', '_classify_']
       lowercase__:   Union[str, Any]  			=   OpenAIGPTTokenizer.from_pretrained(args.model_name     )
       tokenizer.add_tokens(snake_case     )
       lowercase__:   int  			=   tokenizer.convert_tokens_to_ids(snake_case     )
       lowercase__:   Optional[int]  			=   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     ):
              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__:   Optional[int]  			=   load_rocstories_dataset(args.train_dataset     )
       lowercase__:   Optional[Any]  			=   load_rocstories_dataset(args.eval_dataset     )
       lowercase__:   str  			=   (train_dataset, eval_dataset)
       lowercase__:   int  			=   tokenize_and_encode(snake_case     )

       # Compute the max input length for the Transformer
       lowercase__:   int  			=   model.config.n_positions // 2 - 2
       lowercase__:   Tuple  			=   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__:   Dict  			=   min(snake_case			,							model.config.n_positions     )  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       lowercase__:   List[str]  			=   pre_process_datasets(snake_case			,							snake_case			,							snake_case			,							*snake_case     )
       lowercase__			,						lowercase__:   Union[str, Any]  			=   tensor_datasets[0], tensor_datasets[1]

       lowercase__:   List[str]  			=   TensorDataset(*snake_case     )
       lowercase__:   List[Any]  			=   RandomSampler(snake_case     )
       lowercase__:   Tuple  			=   DataLoader(snake_case			,							sampler=snake_case			,							batch_size=args.train_batch_size     )

       lowercase__:   Union[str, Any]  			=   TensorDataset(*snake_case     )
       lowercase__:   List[str]  			=   SequentialSampler(snake_case     )
       lowercase__:   List[Any]  			=   DataLoader(snake_case			,							sampler=snake_case			,							batch_size=args.eval_batch_size     )

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase__:   int  			=   args.max_steps
                     lowercase__:   int  			=   args.max_steps // (len(snake_case     ) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:   Any  			=   len(snake_case     ) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:   str  			=   list(model.named_parameters()     )
              lowercase__:   str  			=   ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              lowercase__:   Optional[Any]  			=   [
                  {
                      '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__:   Union[str, Any]  			=   AdamW(snake_case			,							lr=args.learning_rate			,							eps=args.adam_epsilon     )
              lowercase__:   List[Any]  			=   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__:   int  			=   0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs     )			,							desc='Epoch'     ):
                     lowercase__:   int  			=   0
                     lowercase__:   Optional[int]  			=   0
                     lowercase__:   int  			=   tqdm(snake_case			,							desc='Training'     )
                     for step, batch in enumerate(snake_case     ):
                            lowercase__:   List[str]  			=   tuple(t.to(snake_case     ) for t in batch     )
                            lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   int  			=   batch
                            lowercase__:   Optional[Any]  			=   model(snake_case			,							mc_token_ids=snake_case			,							lm_labels=snake_case			,							mc_labels=snake_case     )
                            lowercase__:   Tuple  			=   args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowercase__:   Any  			=   (
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowercase__:   Tuple  			=   '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__:   Dict  			=   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__:   List[str]  			=   os.path.join(args.output_dir			,							snake_case     )
              lowercase__:   Union[str, Any]  			=   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__:   Optional[int]  			=   OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
              lowercase__:   Optional[Any]  			=   OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
              model.to(snake_case     )

       if args.do_eval:
              model.eval()
              lowercase__			,						lowercase__:   Union[str, Any]  			=   0, 0
              lowercase__			,						lowercase__:   Optional[int]  			=   0, 0
              for batch in tqdm(snake_case			,							desc='Evaluating'     ):
                     lowercase__:   int  			=   tuple(t.to(snake_case     ) for t in batch     )
                     lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   int  			=   batch
                     with torch.no_grad():
                            lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   Any  			=   model(
                                snake_case			,							mc_token_ids=snake_case			,							lm_labels=snake_case			,							mc_labels=snake_case     )

                     lowercase__:   Union[str, Any]  			=   mc_logits.detach().cpu().numpy()
                     lowercase__:   Optional[int]  			=   mc_labels.to('cpu'     ).numpy()
                     lowercase__:   int  			=   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__:   Optional[int]  			=   eval_loss / nb_eval_steps
              lowercase__:   List[str]  			=   eval_accuracy / nb_eval_examples
              lowercase__:   Optional[Any]  			=   tr_loss / nb_tr_steps if args.do_train else None
              lowercase__:   Any  			=   {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

              lowercase__:   Union[str, Any]  			=   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()






import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   Any  			=   params

        lowercase__:   List[Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   np.array([len(lowerCAmelCase__ ) for t in data] )

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()
 def __getitem__(   self				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        return (self.token_ids[index], self.lengths[index])
 def __len__(   self )					->				List[Any]:




        '''simple docstring'''
        return len(self.lengths )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        assert len(self.token_ids ) == len(self.lengths )
        assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   self.params.max_model_input_size
        lowercase__:   Dict  			=   self.lengths > max_len
        logger.info(F'Splitting {sum(lowerCAmelCase__ )} too long sequences.' )

        def divide_chunks(lowerCAmelCase__				, lowerCAmelCase__ ):
               return [l[i : i + n] for i in range(0				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )]

        lowercase__:   str  			=   []
        lowercase__:   List[str]  			=   []
        if self.params.mlm:
               lowercase__			,						lowercase__:   str  			=   self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
               lowercase__			,						lowercase__:   int  			=   self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

        for seq_, len_ in zip(self.token_ids				, self.lengths ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                      new_tok_ids.append(seq_ )
                      new_lengths.append(len_ )
               else:
                      lowercase__:   Optional[Any]  			=   []
                      for sub_s in divide_chunks(seq_				, max_len - 2 ):
                             if sub_s[0] != cls_id:
                                    lowercase__:   int  			=   np.insert(lowerCAmelCase__				, 0				, lowerCAmelCase__ )
                             if sub_s[-1] != sep_id:
                                    lowercase__:   Union[str, Any]  			=   np.insert(lowerCAmelCase__				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )
                             assert len(lowerCAmelCase__ ) <= max_len
                             assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                             sub_seqs.append(lowerCAmelCase__ )

                      new_tok_ids.extend(lowerCAmelCase__ )
                      new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )

        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Dict  			=   len(self )
        lowercase__:   Union[str, Any]  			=   self.lengths > 11
        lowercase__:   List[Any]  			=   self.token_ids[indices]
        lowercase__:   Optional[Any]  			=   self.lengths[indices]
        lowercase__:   List[str]  			=   len(self )
        logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        if "unk_token" not in self.params.special_tok_ids:
               return
        else:
               lowercase__:   List[str]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   str  			=   len(self )
        lowercase__:   Tuple  			=   np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
        lowercase__:   Any  			=   (unk_occs / self.lengths) < 0.5
        lowercase__:   Tuple  			=   self.token_ids[indices]
        lowercase__:   str  			=   self.lengths[indices]
        lowercase__:   Dict  			=   len(self )
        logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        if not self.params.is_master:
               return
        logger.info(F'{len(self )} sequences' )
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   [t[0] for t in batch]
        lowercase__:   Dict  			=   [t[1] for t in batch]
        assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )

        # Max for paddings
        lowercase__:   List[Any]  			=   max(lowerCAmelCase__ )

        # Pad token ids
        if self.params.mlm:
               lowercase__:   Dict  			=   self.params.special_tok_ids['pad_token']
        else:
               lowercase__:   Optional[Any]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   int  			=   [list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
        assert len(tk_ ) == len(lowerCAmelCase__ )
        assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )

        lowercase__:   Tuple  			=   torch.tensor(tk_ )  # (bs, max_seq_len_)
        lowercase__:   Optional[Any]  			=   torch.tensor(lowerCAmelCase__ )  # (bs)
        return tk_t, lg_t


