





'''simple docstring'''




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
)
lowercase__   :				List[str]      =   logging.getLogger(__name__)






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			List[str]      )					->					List[Any]:






     """simple docstring"""




     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return np.sum(outputs == labels      )






def 	_lowerCAmelCase      (  __snake_case  :			Union[str, Any]      )					->					Any:






     """simple docstring"""




     with open(__snake_case			,							encoding='utf_8'      ) as f:
          __A						:			Any								=	csv.reader(__snake_case      )
          __A						:			Union[str, Any]								=	[]
          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 	_lowerCAmelCase      (  __snake_case  :			List[Any]			,							__snake_case  :			str			,							__snake_case  :			Tuple			,							__snake_case  :			Optional[Any]			,							__snake_case  :			Optional[int]			,							__snake_case  :			Optional[Any]      )					->					Union[str, Any]:






     """simple docstring"""




     __A						:			List[str]								=	[]
     for dataset in encoded_datasets:
          __A						:			Optional[Any]								=	len(__snake_case      )
          __A						:			str								=	np.zeros((n_batch, 2, input_len)			,							dtype=np.intaa      )
          __A						:			Optional[Any]								=	np.zeros((n_batch, 2)			,							dtype=np.intaa      )
          __A						:			int								=	np.full((n_batch, 2, input_len)			,							fill_value=-1_00			,							dtype=np.intaa      )
          __A						:			str								=	np.zeros((n_batch,)			,							dtype=np.intaa      )
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(__snake_case      ):
               __A						:			Tuple								=	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __A						:			Union[str, Any]								=	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __A						:			int								=	with_conta
               __A						:			List[Any]								=	with_conta
               __A						:			List[str]								=	len(__snake_case      ) - 1
               __A						:			Any								=	len(__snake_case      ) - 1
               __A						:			str								=	with_conta
               __A						:			str								=	with_conta
               __A						:			Optional[Any]								=	mc_label
          __A						:			Dict								=	(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 	_lowerCAmelCase      (  )					->					int:






     """simple docstring"""




     __A						:			Tuple								=	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=6.2_5e-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=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.'      )
     __A						:			Tuple								=	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      )

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

     # Compute the max input length for the Transformer
     __A						:			int								=	model.config.n_positions // 2 - 2
     __A						:			Optional[Any]								=	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						:			Union[str, Any]								=	min(__snake_case			,							model.config.n_positions      )  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     __A						:			int								=	pre_process_datasets(__snake_case			,							__snake_case			,							__snake_case			,							*__snake_case      )
     __A						:			int								=	tensor_datasets[0], tensor_datasets[1]

     __A						:			List[Any]								=	TensorDataset(*__snake_case      )
     __A						:			int								=	RandomSampler(__snake_case      )
     __A						:			Optional[int]								=	DataLoader(__snake_case			,							sampler=__snake_case			,							batch_size=args.train_batch_size      )

     __A						:			Dict								=	TensorDataset(*__snake_case      )
     __A						:			str								=	SequentialSampler(__snake_case      )
     __A						:			int								=	DataLoader(__snake_case			,							sampler=__snake_case			,							batch_size=args.eval_batch_size      )

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               __A						:			Dict								=	args.max_steps
               __A						:			List[str]								=	args.max_steps // (len(__snake_case      ) // args.gradient_accumulation_steps) + 1
          else:
               __A						:			List[str]								=	len(__snake_case      ) // args.gradient_accumulation_steps * args.num_train_epochs

          __A						:			int								=	list(model.named_parameters()      )
          __A						:			Union[str, Any]								=	['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          __A						:			Dict								=	[
              {
                  '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						:			List[str]								=	AdamW(__snake_case			,							lr=args.learning_rate			,							eps=args.adam_epsilon      )
          __A						:			Any								=	get_linear_schedule_with_warmup(
              __snake_case			,							num_warmup_steps=args.warmup_steps			,							num_training_steps=__snake_case      )

     if args.do_train:
          __A						:			Union[str, Any]								=	0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs      )			,							desc='Epoch'      ):
               __A						:			Any								=	0
               __A						:			Optional[Any]								=	0
               __A						:			Tuple								=	tqdm(__snake_case			,							desc='Training'      )
               for step, batch in enumerate(__snake_case      ):
                    __A						:			List[str]								=	tuple(t.to(__snake_case      ) for t in batch      )
                    __A						:			Union[str, Any]								=	batch
                    __A						:			Union[str, Any]								=	model(__snake_case			,							mc_token_ids=__snake_case			,							lm_labels=__snake_case			,							mc_labels=__snake_case      )
                    __A						:			Dict								=	args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    __A						:			Optional[int]								=	(
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    __A						:			List[str]								=	'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
          __A						:			List[str]								=	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`
          __A						:			List[Any]								=	os.path.join(args.output_dir			,							__snake_case      )
          __A						:			Dict								=	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
          __A						:			List[str]								=	OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir      )
          __A						:			Any								=	OpenAIGPTTokenizer.from_pretrained(args.output_dir      )
          model.to(__snake_case      )

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

               __A						:			int								=	mc_logits.detach().cpu().numpy()
               __A						:			Dict								=	mc_labels.to('cpu'      ).numpy()
               __A						:			Tuple								=	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

          __A						:			Union[str, Any]								=	eval_loss / nb_eval_steps
          __A						:			Dict								=	eval_accuracy / nb_eval_examples
          __A						:			Tuple								=	tr_loss / nb_tr_steps if args.do_train else None
          __A						:			Any								=	{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

          __A						:			Optional[int]								=	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()






'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase		      =	'''AutoImageProcessor'''
     lowerCAmelCase		      =	'''AutoTokenizer'''


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			Tuple								=	self.image_processor


     def __call__(						self						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     **_UpperCAmelCase):

          '''simple docstring'''






          if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

          if text is not None:
               __A						:			Any								=	self.tokenizer(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if images is not None:
               __A						:			Tuple								=	self.image_processor(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if text is not None and images is not None:
               __A						:			Optional[int]								=	image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**_UpperCAmelCase)						,     tensor_type=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return ["input_ids", "attention_mask", "pixel_values"]