

'''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
)
__lowerCamelCase									=     logging.getLogger(__name__)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   np.argmax(UpperCAmelCase__,							axis=1					)
       return np.sum(outputs == labels					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Any:
       with open(UpperCAmelCase__,							encoding="""utf_8"""					) as f:
              A_						       =   csv.reader(UpperCAmelCase__					)
              A_						       =   []
              next(UpperCAmelCase__					)  # skip the first line
              for line in tqdm(UpperCAmelCase__					):
                     output.append((""" """.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
       return output
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       A_						       =   []
       for dataset in encoded_datasets:
              A_						       =   len(UpperCAmelCase__					)
              A_						       =   np.zeros((n_batch, 2, input_len),							dtype=np.intaa					)
              A_						       =   np.zeros((n_batch, 2),							dtype=np.intaa					)
              A_						       =   np.full((n_batch, 2, input_len),							fill_value=-1_00,							dtype=np.intaa					)
              A_						       =   np.zeros((n_batch,),							dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(UpperCAmelCase__					):
                     A_						       =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     A_						       =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     A_						       =   with_conta
                     A_						       =   with_conta
                     A_						       =   len(UpperCAmelCase__					) - 1
                     A_						       =   len(UpperCAmelCase__					) - 1
                     A_						       =   with_conta
                     A_						       =   with_conta
                     A_						       =   mc_label
              A_						       =   (input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(UpperCAmelCase__					) for t in all_inputs					)					)
       return tensor_datasets
def    UpperCAmelCase__     (   )      ->      Tuple:
       A_						       =   argparse.ArgumentParser()
       parser.add_argument("""--model_name""",							type=UpperCAmelCase__,							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=UpperCAmelCase__,							type=UpperCAmelCase__,							required=UpperCAmelCase__,							help="""The output directory where the model predictions and checkpoints will be written.""",							)
       parser.add_argument("""--train_dataset""",							type=UpperCAmelCase__,							default=""""""					)
       parser.add_argument("""--eval_dataset""",							type=UpperCAmelCase__,							default=""""""					)
       parser.add_argument("""--seed""",							type=UpperCAmelCase__,							default=42					)
       parser.add_argument("""--num_train_epochs""",							type=UpperCAmelCase__,							default=3					)
       parser.add_argument("""--train_batch_size""",							type=UpperCAmelCase__,							default=8					)
       parser.add_argument("""--eval_batch_size""",							type=UpperCAmelCase__,							default=16					)
       parser.add_argument("""--adam_epsilon""",							default=1e-8,							type=UpperCAmelCase__,							help="""Epsilon for Adam optimizer."""					)
       parser.add_argument("""--max_grad_norm""",							type=UpperCAmelCase__,							default=1					)
       parser.add_argument(
           """--max_steps""",							default=-1,							type=UpperCAmelCase__,							help=(
               """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
           ),							)
       parser.add_argument(
           """--gradient_accumulation_steps""",							type=UpperCAmelCase__,							default=1,							help="""Number of updates steps to accumulate before                        performing a backward/update pass.""",							)
       parser.add_argument("""--learning_rate""",							type=UpperCAmelCase__,							default=6.25e-5					)
       parser.add_argument("""--warmup_steps""",							default=0,							type=UpperCAmelCase__,							help="""Linear warmup over warmup_steps."""					)
       parser.add_argument("""--lr_schedule""",							type=UpperCAmelCase__,							default="""warmup_linear"""					)
       parser.add_argument("""--weight_decay""",							type=UpperCAmelCase__,							default=0.01					)
       parser.add_argument("""--lm_coef""",							type=UpperCAmelCase__,							default=0.9					)
       parser.add_argument("""--n_valid""",							type=UpperCAmelCase__,							default=3_74					)

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

       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=UpperCAmelCase__					)
              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					)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
      main()



'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCamelCase									=     {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase									=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
