






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__      :	str       	=      logging.getLogger(__name__)






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     np.argmax(_lowercase    ,      axis=1)
   return np.sum(outputs == labels)






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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     []
   for dataset in encoded_datasets:
      UpperCamelCase_									=     len(_lowercase)
      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(_lowercase):
         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(_lowercase) - 1
         UpperCamelCase_									=     len(_lowercase) - 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(_lowercase) for t in all_inputs))
   return tensor_datasets






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

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

   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=_lowercase)
      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(_lowercase    ,      _lowercase))

   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(_lowercase)
   UpperCamelCase_									=     tokenizer.convert_tokens_to_ids(_lowercase)
   UpperCamelCase_									=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
   model.resize_token_embeddings(len(_lowercase))
   model.to(_lowercase)

   # Load and encode the datasets
   def tokenize_and_encode(__lowercase):
      if isinstance(_lowercase    ,      _lowercase):
         return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_lowercase))
      elif isinstance(_lowercase    ,      _lowercase):
         return obj
      return [tokenize_and_encode(_lowercase) 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(_lowercase)

   # 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(_lowercase    ,      model.config.n_positions)  # Max size of input for the pre-trained model

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

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

   UpperCamelCase_									=     TensorDataset(*_lowercase)
   UpperCamelCase_									=     SequentialSampler(_lowercase)
   UpperCamelCase_									=     DataLoader(_lowercase    ,      sampler=_lowercase    ,      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(_lowercase) // args.gradient_accumulation_steps) + 1
      else:
         UpperCamelCase_									=     len(_lowercase) // 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(_lowercase    ,      lr=args.learning_rate    ,      eps=args.adam_epsilon)
      UpperCamelCase_									=     get_linear_schedule_with_warmup(
          _lowercase    ,      num_warmup_steps=args.warmup_steps    ,      num_training_steps=_lowercase)

   if args.do_train:
      UpperCamelCase_									=     0, 0, None
      model.train()
      for _ in trange(int(args.num_train_epochs)    ,      desc='Epoch'):
         UpperCamelCase_									=     0
         UpperCamelCase_									=     0
         UpperCamelCase_									=     tqdm(_lowercase    ,      desc='Training')
         for step, batch in enumerate(_lowercase):
            UpperCamelCase_									=     tuple(t.to(_lowercase) for t in batch)
            UpperCamelCase_									=     batch
            UpperCamelCase_									=     model(_lowercase    ,      mc_token_ids=_lowercase    ,      lm_labels=_lowercase    ,      mc_labels=_lowercase)
            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(_lowercase    ,      scheduler.get_lr()[0])

    # Save a trained model
   if args.do_train:
      # Save a trained model, configuration and tokenizer
      UpperCamelCase_									=     model.module if hasattr(_lowercase    ,      '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    ,      _lowercase)
      UpperCamelCase_									=     os.path.join(args.output_dir    ,      _lowercase)

      torch.save(model_to_save.state_dict()    ,      _lowercase)
      model_to_save.config.to_json_file(_lowercase)
      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(_lowercase)

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

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

         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(_lowercase    ,      'w') as writer:
         logger.info('***** Eval results *****')
         for key in sorted(result.keys()):
            logger.info('  %s = %s'    ,      _lowercase    ,      str(result[key]))
            writer.write('%s = %s\n' % (key, str(result[key])))


if __name__ == "__main__":
 main()













import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     parent




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {}






def     _snake_case    ():
   UpperCamelCase_									=     '<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

   UpperCamelCase_									=     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]


@require_bsa
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   MarkupLMFeatureExtractor if is_bsa_available() else None




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     MarkupLMFeatureExtractionTester(self )




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     return self.feature_extract_tester.prepare_feat_extract_dict()




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Initialize feature_extractor
     UpperCamelCase_									=     self.feature_extraction_class()

     # Test not batched input
     UpperCamelCase_									=     get_html_strings()[0]
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
     UpperCamelCase_									=     [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
     # fmt: on

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )

     # Test batched
     UpperCamelCase_									=     get_html_strings()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     expected_nodes + [['My First Heading', 'My first paragraph.']]
     UpperCamelCase_									=     expected_xpaths + [['/html/body/h1', '/html/body/p']]

     self.assertEqual(len(encoding.nodes )		,		2 )
     self.assertEqual(len(encoding.xpaths )		,		2 )

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )





