




"""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
)
UpperCamelCase								=					logging.getLogger(__name__)



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			np.argmax(_lowercase       ,    axis=1      )
     return np.sum(outputs == labels      )



def 							_A       (						lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

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



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			argparse.ArgumentParser()
     parser.add_argument("--model_name"       ,    type=_lowercase       ,    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=_lowercase       ,    type=_lowercase       ,    required=_lowercase       ,    help="The output directory where the model predictions and checkpoints will be written."       ,    )
     parser.add_argument("--train_dataset"       ,    type=_lowercase       ,    default=""      )
     parser.add_argument("--eval_dataset"       ,    type=_lowercase       ,    default=""      )
     parser.add_argument("--seed"       ,    type=_lowercase       ,    default=42      )
     parser.add_argument("--num_train_epochs"       ,    type=_lowercase       ,    default=3      )
     parser.add_argument("--train_batch_size"       ,    type=_lowercase       ,    default=8      )
     parser.add_argument("--eval_batch_size"       ,    type=_lowercase       ,    default=16      )
     parser.add_argument("--adam_epsilon"       ,    default=1E-8       ,    type=_lowercase       ,    help="Epsilon for Adam optimizer."      )
     parser.add_argument("--max_grad_norm"       ,    type=_lowercase       ,    default=1      )
     parser.add_argument(
         "--max_steps"       ,    default=-1       ,    type=_lowercase       ,    help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         )       ,    )
     parser.add_argument(
         "--gradient_accumulation_steps"       ,    type=_lowercase       ,    default=1       ,    help="Number of updates steps to accumulate before                        performing a backward/update pass."       ,    )
     parser.add_argument("--learning_rate"       ,    type=_lowercase       ,    default=6.2_5E-5      )
     parser.add_argument("--warmup_steps"       ,    default=0       ,    type=_lowercase       ,    help="Linear warmup over warmup_steps."      )
     parser.add_argument("--lr_schedule"       ,    type=_lowercase       ,    default="warmup_linear"      )
     parser.add_argument("--weight_decay"       ,    type=_lowercase       ,    default=0.01      )
     parser.add_argument("--lm_coef"       ,    type=_lowercase       ,    default=0.9      )
     parser.add_argument("--n_valid"       ,    type=_lowercase       ,    default=374      )

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

     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=_lowercase      )
          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      )

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

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

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

     logger.info("Encoding dataset..."      )
     lowerCAmelCase__          =			load_rocstories_dataset(args.train_dataset      )
     lowerCAmelCase__          =			load_rocstories_dataset(args.eval_dataset      )
     lowerCAmelCase__          =			(train_dataset, eval_dataset)
     lowerCAmelCase__          =			tokenize_and_encode(_lowercase      )

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

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

     lowerCAmelCase__          =			TensorDataset(*_lowercase      )
     lowerCAmelCase__          =			RandomSampler(_lowercase      )
     lowerCAmelCase__          =			DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.train_batch_size      )

     lowerCAmelCase__          =			TensorDataset(*_lowercase      )
     lowerCAmelCase__          =			SequentialSampler(_lowercase      )
     lowerCAmelCase__          =			DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.eval_batch_size      )

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

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

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

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

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

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

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

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

               lowerCAmelCase__          =			mc_logits.detach().cpu().numpy()
               lowerCAmelCase__          =			mc_labels.to("cpu"      ).numpy()
               lowerCAmelCase__          =			accuracy(_lowercase       ,    _lowercase      )

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

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

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

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


if __name__ == "__main__":
     main()






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase								=					'__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase								=					'Dummy User'
UpperCamelCase								=					'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'

UpperCamelCase								=					'https://hub-ci.huggingface.co'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
UpperCamelCase								=					Path('~/.huggingface/hub_ci_token').expanduser()



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     monkeypatch.setattr("datasets.config.HF_ENDPOINT"       ,    lowerCAmelCase_      )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     HfFolder.save_token(lowerCAmelCase_      )
     yield
     HfFolder.delete_token()



@pytest.fixture(scope="session"      )
def 							_A       (						):
     """simple docstring"""

     return HfApi(endpoint=lowerCAmelCase_      )



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi      ):
     """simple docstring"""

     lowerCAmelCase__          =			HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase_      )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     def _cleanup_repo(lowerCAmelCase_					: Optional[Any]      ):
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )

     return _cleanup_repo



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     @contextmanager
     def _temporary_repo(lowerCAmelCase_					: str      ):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase_      )

     return _temporary_repo



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data/text_data.txt"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_img_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_img_data_
