
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			:     Optional[Any]           =   logging.getLogger(__name__)

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       np.argmax(A_,     axis=1					)
  return np.sum(outputs == labels					)

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_,     A_,     A_,     A_,     A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       []
  for dataset in encoded_datasets:
    __magic_name__				     =       len(A_					)
    __magic_name__				     =       np.zeros((n_batch, 2, input_len),     dtype=np.intaa					)
    __magic_name__				     =       np.zeros((n_batch, 2),     dtype=np.intaa					)
    __magic_name__				     =       np.full((n_batch, 2, input_len),     fill_value=-100,     dtype=np.intaa					)
    __magic_name__				     =       np.zeros((n_batch,),     dtype=np.intaa					)
    for (
        i,
        (story, conta, conta, mc_label),
    ) in enumerate(A_					):
      __magic_name__				     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      __magic_name__				     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      __magic_name__				     =       with_conta
      __magic_name__				     =       with_conta
      __magic_name__				     =       len(A_					) - 1
      __magic_name__				     =       len(A_					) - 1
      __magic_name__				     =       with_conta
      __magic_name__				     =       with_conta
      __magic_name__				     =       mc_label
    __magic_name__				     =       (input_ids, mc_token_ids, lm_labels, mc_labels)
    tensor_datasets.append(tuple(torch.tensor(A_					) for t in all_inputs					)					)
  return tensor_datasets

def a__    (       ):






  '''simple docstring'''





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

  parser.add_argument("""--server_ip""",     type=A_,     default="""""",     help="""Can be used for distant debugging."""					)
  parser.add_argument("""--server_port""",     type=A_,     default="""""",     help="""Can be used for distant debugging."""					)
  __magic_name__				     =       parser.parse_args()
  print(A_					)

  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=A_					)
    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					)

  __magic_name__				     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
  __magic_name__				     =       torch.cuda.device_count()
  logger.info("""device: {}, n_gpu {}""".format(A_,     A_					)					)

  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
  __magic_name__				     =       ["""_start_""", """_delimiter_""", """_classify_"""]
  __magic_name__				     =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
  tokenizer.add_tokens(A_					)
  __magic_name__				     =       tokenizer.convert_tokens_to_ids(A_					)
  __magic_name__				     =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
  model.resize_token_embeddings(len(A_					)					)
  model.to(A_					)

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

  logger.info("""Encoding dataset..."""					)
  __magic_name__				     =       load_rocstories_dataset(args.train_dataset					)
  __magic_name__				     =       load_rocstories_dataset(args.eval_dataset					)
  __magic_name__				     =       (train_dataset, eval_dataset)
  __magic_name__				     =       tokenize_and_encode(A_					)

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

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

  __magic_name__				     =       TensorDataset(*A_					)
  __magic_name__				     =       RandomSampler(A_					)
  __magic_name__				     =       DataLoader(A_,     sampler=A_,     batch_size=args.train_batch_size					)

  __magic_name__				     =       TensorDataset(*A_					)
  __magic_name__				     =       SequentialSampler(A_					)
  __magic_name__				     =       DataLoader(A_,     sampler=A_,     batch_size=args.eval_batch_size					)

  # Prepare optimizer
  if args.do_train:
    if args.max_steps > 0:
      __magic_name__				     =       args.max_steps
      __magic_name__				     =       args.max_steps // (len(A_					) // args.gradient_accumulation_steps) + 1
    else:
      __magic_name__				     =       len(A_					) // args.gradient_accumulation_steps * args.num_train_epochs

    __magic_name__				     =       list(model.named_parameters()					)
    __magic_name__				     =       ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
    __magic_name__				     =       [
        {
            """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},
    ]
    __magic_name__				     =       AdamW(A_,     lr=args.learning_rate,     eps=args.adam_epsilon					)
    __magic_name__				     =       get_linear_schedule_with_warmup(
        A_,     num_warmup_steps=args.warmup_steps,     num_training_steps=A_					)

  if args.do_train:
    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       0, 0, None
    model.train()
    for _ in trange(int(args.num_train_epochs					),     desc="""Epoch"""					):
      __magic_name__				     =       0
      __magic_name__				     =       0
      __magic_name__				     =       tqdm(A_,     desc="""Training"""					)
      for step, batch in enumerate(A_					):
        __magic_name__				     =       tuple(t.to(A_					) for t in batch					)
        __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       batch
        __magic_name__				     =       model(A_,     mc_token_ids=A_,     lm_labels=A_,     mc_labels=A_					)
        __magic_name__				     =       args.lm_coef * losses[0] + losses[1]
        loss.backward()
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
        tr_loss += loss.item()
        __magic_name__				     =       (
            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
        )
        nb_tr_steps += 1
        __magic_name__				     =       """Training loss: {:.2e} lr: {:.2e}""".format(A_,     scheduler.get_lr()[0]					)

    # Save a trained model
  if args.do_train:
    # Save a trained model, configuration and tokenizer
    __magic_name__				     =       model.module if hasattr(A_,     """module"""					) else model  # Only save the model itself

    # If we save using the predefined names, we can load using `from_pretrained`
    __magic_name__				     =       os.path.join(args.output_dir,     A_					)
    __magic_name__				     =       os.path.join(args.output_dir,     A_					)

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

    # Load a trained model and vocabulary that you have fine-tuned
    __magic_name__				     =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
    __magic_name__				     =       OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
    model.to(A_					)

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

      __magic_name__				     =       mc_logits.detach().cpu().numpy()
      __magic_name__				     =       mc_labels.to("""cpu"""					).numpy()
      __magic_name__				     =       accuracy(A_,     A_					)

      eval_loss += mc_loss.mean().item()
      eval_accuracy += tmp_eval_accuracy

      nb_eval_examples += input_ids.size(0					)
      nb_eval_steps += 1

    __magic_name__				     =       eval_loss / nb_eval_steps
    __magic_name__				     =       eval_accuracy / nb_eval_examples
    __magic_name__				     =       tr_loss / nb_tr_steps if args.do_train else None
    __magic_name__				     =       {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

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


if __name__ == "__main__":
    main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

    __lowerCAmelCase			:     Any           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

