


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							str       					=			'markuplm'
 def __init__(       self    ,			lowerCAmelCase__=30_522    ,			lowerCAmelCase__=768    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=3_072    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=512    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=1E-12    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=256    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=216    ,			lowerCAmelCase__=1_001    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=50    ,			lowerCAmelCase__="absolute"    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__    ,			) ->    List[Any]:




        '''simple docstring'''
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			bos_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
        lowercase__:       List[str]        =       vocab_size
        lowercase__:       str        =       hidden_size
        lowercase__:       Optional[int]        =       num_hidden_layers
        lowercase__:       List[Any]        =       num_attention_heads
        lowercase__:       Any        =       hidden_act
        lowercase__:       int        =       intermediate_size
        lowercase__:       List[Any]        =       hidden_dropout_prob
        lowercase__:       Any        =       attention_probs_dropout_prob
        lowercase__:       Dict        =       max_position_embeddings
        lowercase__:       List[Any]        =       type_vocab_size
        lowercase__:       int        =       initializer_range
        lowercase__:       int        =       layer_norm_eps
        lowercase__:       Union[str, Any]        =       position_embedding_type
        lowercase__:       Dict        =       use_cache
        lowercase__:       Optional[int]        =       classifier_dropout
        # additional properties
        lowercase__:       Any        =       max_depth
        lowercase__:       List[Any]        =       max_xpath_tag_unit_embeddings
        lowercase__:       int        =       max_xpath_subs_unit_embeddings
        lowercase__:       Union[str, Any]        =       tag_pad_id
        lowercase__:       Any        =       subs_pad_id
        lowercase__:       Optional[Any]        =       xpath_unit_hidden_size






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




def        snake_case_					(  snake_case					)  ->    Dict:
       with open(snake_case      ,							encoding='utf_8'					) as f:
              lowercase__:       str        =       csv.reader(snake_case					)
              lowercase__:       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					)  ->    Tuple:
       lowercase__:       List[Any]        =       []
       for dataset in encoded_datasets:
              lowercase__:       Dict        =       len(snake_case					)
              lowercase__:       int        =       np.zeros((n_batch, 2, input_len)      ,							dtype=np.intaa					)
              lowercase__:       int        =       np.zeros((n_batch, 2)      ,							dtype=np.intaa					)
              lowercase__:       Optional[int]        =       np.full((n_batch, 2, input_len)      ,							fill_value=-1_00      ,							dtype=np.intaa					)
              lowercase__:       Optional[Any]        =       np.zeros((n_batch,)      ,							dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case					):
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       Union[str, Any]        =       with_conta
                     lowercase__:       List[Any]        =       with_conta
                     lowercase__:       Any        =       len(snake_case					) - 1
                     lowercase__:       Dict        =       len(snake_case					) - 1
                     lowercase__:       Optional[Any]        =       with_conta
                     lowercase__:       Tuple        =       with_conta
                     lowercase__:       int        =       mc_label
              lowercase__:       Any        =       (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_					(  )  ->    Union[str, Any]:
       lowercase__:       Optional[Any]        =       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__:       List[str]        =       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__:       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__:       Any        =       ['_start_', '_delimiter_', '_classify_']
       lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       lowercase__:       int        =       tokenizer.convert_tokens_to_ids(snake_case					)
       lowercase__:       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__:       Dict        =       load_rocstories_dataset(args.train_dataset					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.eval_dataset					)
       lowercase__:       str        =       (train_dataset, eval_dataset)
       lowercase__:       Any        =       tokenize_and_encode(snake_case					)

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

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

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

       lowercase__:       str        =       TensorDataset(*snake_case					)
       lowercase__:       str        =       SequentialSampler(snake_case					)
       lowercase__:       Optional[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__:       Union[str, Any]        =       args.max_steps
                     lowercase__:       Tuple        =       args.max_steps // (len(snake_case					) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:       Optional[Any]        =       len(snake_case					) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:       str        =       list(model.named_parameters()					)
              lowercase__:       Any        =       ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              lowercase__:       str        =       [
                  {
                      '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__:       Tuple        =       AdamW(snake_case      ,							lr=args.learning_rate      ,							eps=args.adam_epsilon					)
              lowercase__:       Tuple        =       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__:       str        =       0
                     lowercase__:       Optional[Any]        =       0
                     lowercase__:       List[Any]        =       tqdm(snake_case      ,							desc='Training'					)
                     for step, batch in enumerate(snake_case					):
                            lowercase__:       Union[str, Any]        =       tuple(t.to(snake_case					) for t in batch					)
                            lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       List[Any]        =       batch
                            lowercase__:       List[str]        =       model(snake_case      ,							mc_token_ids=snake_case      ,							lm_labels=snake_case      ,							mc_labels=snake_case					)
                            lowercase__:       Optional[Any]        =       args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowercase__:       Union[str, 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__:       Any        =       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__:       Tuple        =       os.path.join(args.output_dir      ,							snake_case					)
              lowercase__:       List[str]        =       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[Any]        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
              lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
              model.to(snake_case					)

       if args.do_eval:
              model.eval()
              lowercase__      ,						lowercase__:       Optional[Any]        =       0, 0
              lowercase__      ,						lowercase__:       List[Any]        =       0, 0
              for batch in tqdm(snake_case      ,							desc='Evaluating'					):
                     lowercase__:       str        =       tuple(t.to(snake_case					) for t in batch					)
                     lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       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__:       Dict        =       mc_logits.detach().cpu().numpy()
                     lowercase__:       Tuple        =       mc_labels.to('cpu'					).numpy()
                     lowercase__:       Dict        =       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__:       Optional[int]        =       eval_accuracy / nb_eval_examples
              lowercase__:       int        =       tr_loss / nb_tr_steps if args.do_train else None
              lowercase__:       Optional[Any]        =       {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

              lowercase__:       Dict        =       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()


