

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_			  = logging.getLogger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   str      =       np.argmax(SCREAMING_SNAKE_CASE_	,     axis=1			)
     return np.sum(outputs == labels			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    Dict	,     SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       []
     for dataset in encoded_datasets:
          lowercase__    :   List[str]      =       len(SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Optional[Any]      =       np.zeros((n_batch, 2, input_len)	,     dtype=np.intaa			)
          lowercase__    :   Optional[int]      =       np.zeros((n_batch, 2)	,     dtype=np.intaa			)
          lowercase__    :   str      =       np.full((n_batch, 2, input_len)	,     fill_value=-100	,     dtype=np.intaa			)
          lowercase__    :   Dict      =       np.zeros((n_batch,)	,     dtype=np.intaa			)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   Optional[int]      =       [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__    :   Tuple      =       with_conta
               lowercase__    :   List[Any]      =       with_conta
               lowercase__    :   int      =       len(SCREAMING_SNAKE_CASE_			) - 1
               lowercase__    :   Union[str, Any]      =       len(SCREAMING_SNAKE_CASE_			) - 1
               lowercase__    :   Union[str, Any]      =       with_conta
               lowercase__    :   Any      =       with_conta
               lowercase__    :   Dict      =       mc_label
          lowercase__    :   List[str]      =       (input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_			) for t in all_inputs			)			)
     return tensor_datasets

def 	snake_case__			(    ):



     '''simple docstring'''





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

     parser.add_argument('--server_ip'	,     type=SCREAMING_SNAKE_CASE_	,     default=''	,     help='Can be used for distant debugging.'			)
     parser.add_argument('--server_port'	,     type=SCREAMING_SNAKE_CASE_	,     default=''	,     help='Can be used for distant debugging.'			)
     lowercase__    :   Dict      =       parser.parse_args()
     print(SCREAMING_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=SCREAMING_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__    :   Any      =       torch.cuda.device_count()
     logger.info('device: {}, n_gpu {}'.format(SCREAMING_SNAKE_CASE_	,     SCREAMING_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__    :   Optional[int]      =       OpenAIGPTTokenizer.from_pretrained(args.model_name			)
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Optional[int]      =       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Dict      =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_			)			)
     model.to(SCREAMING_SNAKE_CASE_			)

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

     logger.info('Encoding dataset...'			)
     lowercase__    :   Optional[Any]      =       load_rocstories_dataset(args.train_dataset			)
     lowercase__    :   str      =       load_rocstories_dataset(args.eval_dataset			)
     lowercase__    :   Dict      =       (train_dataset, eval_dataset)
     lowercase__    :   Any      =       tokenize_and_encode(SCREAMING_SNAKE_CASE_			)

     # Compute the max input length for the Transformer
     lowercase__    :   Tuple      =       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__    :   Any      =       min(SCREAMING_SNAKE_CASE_	,     model.config.n_positions			)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     lowercase__    :   int      =       pre_process_datasets(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     *SCREAMING_SNAKE_CASE_			)
     lowercase__      , lowercase__    :   Dict      =       tensor_datasets[0], tensor_datasets[1]

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

     lowercase__    :   Union[str, Any]      =       TensorDataset(*SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Any      =       SequentialSampler(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[Any]      =       DataLoader(SCREAMING_SNAKE_CASE_	,     sampler=SCREAMING_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__    :   List[Any]      =       args.max_steps // (len(SCREAMING_SNAKE_CASE_			) // args.gradient_accumulation_steps) + 1
          else:
               lowercase__    :   List[str]      =       len(SCREAMING_SNAKE_CASE_			) // args.gradient_accumulation_steps * args.num_train_epochs

          lowercase__    :   int      =       list(model.named_parameters()			)
          lowercase__    :   Dict      =       ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          lowercase__    :   int      =       [
              {
                  '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__    :   str      =       AdamW(SCREAMING_SNAKE_CASE_	,     lr=args.learning_rate	,     eps=args.adam_epsilon			)
          lowercase__    :   Dict      =       get_linear_schedule_with_warmup(
              SCREAMING_SNAKE_CASE_	,     num_warmup_steps=args.warmup_steps	,     num_training_steps=SCREAMING_SNAKE_CASE_			)

     if args.do_train:
          lowercase__      , lowercase__      , lowercase__    :   Optional[int]      =       0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs			)	,     desc='Epoch'			):
               lowercase__    :   Optional[int]      =       0
               lowercase__    :   List[Any]      =       0
               lowercase__    :   List[str]      =       tqdm(SCREAMING_SNAKE_CASE_	,     desc='Training'			)
               for step, batch in enumerate(SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       tuple(t.to(SCREAMING_SNAKE_CASE_			) for t in batch			)
                    lowercase__      , lowercase__      , lowercase__      , lowercase__    :   Any      =       batch
                    lowercase__    :   Dict      =       model(SCREAMING_SNAKE_CASE_	,     mc_token_ids=SCREAMING_SNAKE_CASE_	,     lm_labels=SCREAMING_SNAKE_CASE_	,     mc_labels=SCREAMING_SNAKE_CASE_			)
                    lowercase__    :   Union[str, Any]      =       args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    lowercase__    :   List[Any]      =       (
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    lowercase__    :   Dict      =       'Training loss: {:.2e} lr: {:.2e}'.format(SCREAMING_SNAKE_CASE_	,     scheduler.get_lr()[0]			)

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          lowercase__    :   Optional[int]      =       model.module if hasattr(SCREAMING_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[Any]      =       os.path.join(args.output_dir	,     SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Tuple      =       os.path.join(args.output_dir	,     SCREAMING_SNAKE_CASE_			)

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

          # Load a trained model and vocabulary that you have fine-tuned
          lowercase__    :   int      =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
          lowercase__    :   Dict      =       OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
          model.to(SCREAMING_SNAKE_CASE_			)

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

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

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


if __name__ == "__main__":
       main()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """distilbert"""
    __lowerCamelCase						:       List[Any]														=     {
        """hidden_size""": """dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
    }





    def __init__(      self						,   a=3_0522						,   a=512						,   a=False						,   a=6						,   a=12						,   a=768						,   a=4 * 768						,   a=0.1						,   a=0.1						,   a="gelu"						,   a=0.02						,   a=0.1						,   a=0.2						,   a=0						,   **a						,   ):
         lowercase__    :   int      =       vocab_size
         lowercase__    :   int      =       max_position_embeddings
         lowercase__    :   List[str]      =       sinusoidal_pos_embds
         lowercase__    :   Tuple      =       n_layers
         lowercase__    :   Optional[int]      =       n_heads
         lowercase__    :   List[Any]      =       dim
         lowercase__    :   Any      =       hidden_dim
         lowercase__    :   Union[str, Any]      =       dropout
         lowercase__    :   Optional[int]      =       attention_dropout
         lowercase__    :   List[Any]      =       activation
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Dict      =       qa_dropout
         lowercase__    :   Tuple      =       seq_classif_dropout
         super().__init__(**a						,   pad_token_id=a)



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @property
    def       snake_case_				(      self):
         if self.task == "multiple-choice":
              lowercase__    :   List[str]      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__    :   Tuple      =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

