


"""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
)
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.getLogger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		np.argmax(_SCREAMING_SNAKE_CASE				, axis=1				)
							return np.sum(outputs == labels				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							with open(_SCREAMING_SNAKE_CASE				, encoding="""utf_8"""				) as f:
														snake_case_           =		csv.reader(_SCREAMING_SNAKE_CASE				)
														snake_case_           =		[]
														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				)    ->				Union[str, Any]:
							snake_case_           =		[]
							for dataset in encoded_datasets:
														snake_case_           =		len(_SCREAMING_SNAKE_CASE				)
														snake_case_           =		np.zeros((n_batch, 2, input_len)				, dtype=np.intaa				)
														snake_case_           =		np.zeros((n_batch, 2)				, dtype=np.intaa				)
														snake_case_           =		np.full((n_batch, 2, input_len)				, fill_value=-100				, dtype=np.intaa				)
														snake_case_           =		np.zeros((n_batch,)				, dtype=np.intaa				)
														for (
														    i,
														    (story, conta, conta, mc_label),
														) in enumerate(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					snake_case_           =		[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					snake_case_           =		with_conta
																					snake_case_           =		with_conta
																					snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1
																					snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1
																					snake_case_           =		with_conta
																					snake_case_           =		with_conta
																					snake_case_           =		mc_label
														snake_case_           =		(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]:
							snake_case_           =		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=6.2_5E-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."""				)
							snake_case_           =		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				)

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

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

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

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

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

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

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

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

																					snake_case_           =		mc_logits.detach().cpu().numpy()
																					snake_case_           =		mc_labels.to("""cpu"""				).numpy()
																					snake_case_           =		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

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

														snake_case_           =		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()







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



