'''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
)
__snake_case						  =		logging.getLogger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       np.argmax(_SCREAMING_SNAKE_CASE ,	axis=1						)
  return np.sum(outputs == labels						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:




  with open(_SCREAMING_SNAKE_CASE ,	encoding="""utf_8"""						) as f:
    lowercase_     =       csv.reader(_SCREAMING_SNAKE_CASE						)
    lowercase_     =       []
    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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       []
  for dataset in encoded_datasets:
    lowercase_     =       len(_SCREAMING_SNAKE_CASE						)
    lowercase_     =       np.zeros((n_batch, 2, input_len) ,	dtype=np.intaa						)
    lowercase_     =       np.zeros((n_batch, 2) ,	dtype=np.intaa						)
    lowercase_     =       np.full((n_batch, 2, input_len) ,	fill_value=-1_00 ,	dtype=np.intaa						)
    lowercase_     =       np.zeros((n_batch,) ,	dtype=np.intaa						)
    for (
        i,
        (story, conta, conta, mc_label),
    ) in enumerate(_SCREAMING_SNAKE_CASE						):
      lowercase_     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      lowercase_     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      lowercase_     =       with_conta
      lowercase_     =       with_conta
      lowercase_     =       len(_SCREAMING_SNAKE_CASE						) - 1
      lowercase_     =       len(_SCREAMING_SNAKE_CASE						) - 1
      lowercase_     =       with_conta
      lowercase_     =       with_conta
      lowercase_     =       mc_label
    lowercase_     =       (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   A_      (       )			->List[str]:
  lowercase_     =       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=3_74						)

  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_     =       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_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
  lowercase_     =       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_     =       ["""_start_""", """_delimiter_""", """_classify_"""]
  lowercase_     =       OpenAIGPTTokenizer.from_pretrained(args.model_name						)
  tokenizer.add_tokens(_SCREAMING_SNAKE_CASE						)
  lowercase_     =       tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
  lowercase_     =       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_						):
    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_     =       load_rocstories_dataset(args.train_dataset						)
  lowercase_     =       load_rocstories_dataset(args.eval_dataset						)
  lowercase_     =       (train_dataset, eval_dataset)
  lowercase_     =       tokenize_and_encode(_SCREAMING_SNAKE_CASE						)

  # Compute the max input length for the Transformer
  lowercase_     =       model.config.n_positions // 2 - 2
  lowercase_     =       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_     =       min(_SCREAMING_SNAKE_CASE ,	model.config.n_positions						)  # Max size of input for the pre-trained model

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

  lowercase_     =       TensorDataset(*_SCREAMING_SNAKE_CASE						)
  lowercase_     =       RandomSampler(_SCREAMING_SNAKE_CASE						)
  lowercase_     =       DataLoader(_SCREAMING_SNAKE_CASE ,	sampler=_SCREAMING_SNAKE_CASE ,	batch_size=args.train_batch_size						)

  lowercase_     =       TensorDataset(*_SCREAMING_SNAKE_CASE						)
  lowercase_     =       SequentialSampler(_SCREAMING_SNAKE_CASE						)
  lowercase_     =       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_     =       args.max_steps
      lowercase_     =       args.max_steps // (len(_SCREAMING_SNAKE_CASE						) // args.gradient_accumulation_steps) + 1
    else:
      lowercase_     =       len(_SCREAMING_SNAKE_CASE						) // args.gradient_accumulation_steps * args.num_train_epochs

    lowercase_     =       list(model.named_parameters()						)
    lowercase_     =       ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
    lowercase_     =       [
        {
            """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_     =       AdamW(_SCREAMING_SNAKE_CASE ,	lr=args.learning_rate ,	eps=args.adam_epsilon						)
    lowercase_     =       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_     =       0, 0, None
    model.train()
    for _ in trange(int(args.num_train_epochs						) ,	desc="""Epoch"""						):
      lowercase_     =       0
      lowercase_     =       0
      lowercase_     =       tqdm(_SCREAMING_SNAKE_CASE ,	desc="""Training"""						)
      for step, batch in enumerate(_SCREAMING_SNAKE_CASE						):
        lowercase_     =       tuple(t.to(_SCREAMING_SNAKE_CASE						) for t in batch						)
        lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       batch
        lowercase_     =       model(_SCREAMING_SNAKE_CASE ,	mc_token_ids=_SCREAMING_SNAKE_CASE ,	lm_labels=_SCREAMING_SNAKE_CASE ,	mc_labels=_SCREAMING_SNAKE_CASE						)
        lowercase_     =       args.lm_coef * losses[0] + losses[1]
        loss.backward()
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
        tr_loss += loss.item()
        lowercase_     =       (
            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
        )
        nb_tr_steps += 1
        lowercase_     =       """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_     =       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_     =       os.path.join(args.output_dir ,	_SCREAMING_SNAKE_CASE						)
    lowercase_     =       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_     =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
    lowercase_     =       OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
    model.to(_SCREAMING_SNAKE_CASE						)

  if args.do_eval:
    model.eval()
    lowercase_     ,	lowercase_     =       0, 0
    lowercase_     ,	lowercase_     =       0, 0
    for batch in tqdm(_SCREAMING_SNAKE_CASE ,	desc="""Evaluating"""						):
      lowercase_     =       tuple(t.to(_SCREAMING_SNAKE_CASE						) for t in batch						)
      lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       batch
      with torch.no_grad():
        lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       model(
            _SCREAMING_SNAKE_CASE ,	mc_token_ids=_SCREAMING_SNAKE_CASE ,	lm_labels=_SCREAMING_SNAKE_CASE ,	mc_labels=_SCREAMING_SNAKE_CASE						)

      lowercase_     =       mc_logits.detach().cpu().numpy()
      lowercase_     =       mc_labels.to("""cpu"""						).numpy()
      lowercase_     =       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_     =       eval_loss / nb_eval_steps
    lowercase_     =       eval_accuracy / nb_eval_examples
    lowercase_     =       tr_loss / nb_tr_steps if args.do_train else None
    lowercase_     =       {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

    lowercase_     =       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()








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__A							:			List[Any]         =			"python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    require_version(deps[pkg]							,     _SCREAMING_SNAKE_CASE				)





