





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
)
_a						=   logging.getLogger(__name__)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	np.argmax(SCREAMING_SNAKE_CASE_ ,axis=1							)
	return np.sum(outputs == labels							)





def lowerCAmelCase__(__snake_case							)       ->   List[str]:







	'''simple docstring'''







	with open(SCREAMING_SNAKE_CASE_ ,encoding='''utf_8'''							) as f:
		lowerCamelCase__              =	csv.reader(SCREAMING_SNAKE_CASE_							)
		lowerCamelCase__              =	[]
		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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	lowerCamelCase__              =	[]
	for dataset in encoded_datasets:
		lowerCamelCase__              =	len(SCREAMING_SNAKE_CASE_							)
		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(SCREAMING_SNAKE_CASE_							):
			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(SCREAMING_SNAKE_CASE_							) - 1
			lowerCamelCase__              =	len(SCREAMING_SNAKE_CASE_							) - 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(SCREAMING_SNAKE_CASE_							) for t in all_inputs							)							)
	return tensor_datasets





def lowerCAmelCase__()       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	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=0.0_1							)
	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.'''							)
	lowerCamelCase__              =	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							)

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

	# 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(SCREAMING_SNAKE_CASE_ ,model.config.n_positions							)  # Max size of input for the pre-trained model

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

	lowerCamelCase__              =	TensorDataset(*SCREAMING_SNAKE_CASE_							)
	lowerCamelCase__              =	RandomSampler(SCREAMING_SNAKE_CASE_							)
	lowerCamelCase__              =	DataLoader(SCREAMING_SNAKE_CASE_ ,sampler=SCREAMING_SNAKE_CASE_ ,batch_size=args.train_batch_size							)

	lowerCamelCase__              =	TensorDataset(*SCREAMING_SNAKE_CASE_							)
	lowerCamelCase__              =	SequentialSampler(SCREAMING_SNAKE_CASE_							)
	lowerCamelCase__              =	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:
			lowerCamelCase__              =	args.max_steps
			lowerCamelCase__              =	args.max_steps // (len(SCREAMING_SNAKE_CASE_							) // args.gradient_accumulation_steps) + 1
		else:
			lowerCamelCase__              =	len(SCREAMING_SNAKE_CASE_							) // 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(SCREAMING_SNAKE_CASE_ ,lr=args.learning_rate ,eps=args.adam_epsilon							)
		lowerCamelCase__              =	get_linear_schedule_with_warmup(
		    SCREAMING_SNAKE_CASE_ ,num_warmup_steps=args.warmup_steps ,num_training_steps=SCREAMING_SNAKE_CASE_							)

	if args.do_train:
		lowerCamelCase__              =	0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs							) ,desc='''Epoch'''							):
			lowerCamelCase__              =	0
			lowerCamelCase__              =	0
			lowerCamelCase__              =	tqdm(SCREAMING_SNAKE_CASE_ ,desc='''Training'''							)
			for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
				lowerCamelCase__              =	tuple(t.to(SCREAMING_SNAKE_CASE_							) for t in batch							)
				lowerCamelCase__              =	batch
				lowerCamelCase__              =	model(SCREAMING_SNAKE_CASE_ ,mc_token_ids=SCREAMING_SNAKE_CASE_ ,lm_labels=SCREAMING_SNAKE_CASE_ ,mc_labels=SCREAMING_SNAKE_CASE_							)
				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(SCREAMING_SNAKE_CASE_ ,scheduler.get_lr()[0]							)

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		lowerCamelCase__              =	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`
		lowerCamelCase__              =	os.path.join(args.output_dir ,SCREAMING_SNAKE_CASE_							)
		lowerCamelCase__              =	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
		lowerCamelCase__              =	OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir							)
		lowerCamelCase__              =	OpenAIGPTTokenizer.from_pretrained(args.output_dir							)
		model.to(SCREAMING_SNAKE_CASE_							)

	if args.do_eval:
		model.eval()
		lowerCamelCase__              =	0, 0
		lowerCamelCase__              =	0, 0
		for batch in tqdm(SCREAMING_SNAKE_CASE_ ,desc='''Evaluating'''							):
			lowerCamelCase__              =	tuple(t.to(SCREAMING_SNAKE_CASE_							) for t in batch							)
			lowerCamelCase__              =	batch
			with torch.no_grad():
				lowerCamelCase__              =	model(
				    SCREAMING_SNAKE_CASE_ ,mc_token_ids=SCREAMING_SNAKE_CASE_ ,lm_labels=SCREAMING_SNAKE_CASE_ ,mc_labels=SCREAMING_SNAKE_CASE_							)

			lowerCamelCase__              =	mc_logits.detach().cpu().numpy()
			lowerCamelCase__              =	mc_labels.to('''cpu'''							).numpy()
			lowerCamelCase__              =	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

		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(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 string
from math import logaa





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	document.translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							).replace('''\n''' ,''''''							)
	lowerCamelCase__              =	document_without_punctuation.split(''' '''							)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   tuple[int, int]:







	'''simple docstring'''







	lowerCamelCase__              =	corpus.lower().translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							)  # strip all punctuation and replace it with ''
	lowerCamelCase__              =	corpus_without_punctuation.split('''\n'''							)
	lowerCamelCase__              =	term.lower()
	return (len([doc for doc in docs if term in doc]							), len(__snake_case							))





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False							)       ->   float:







	'''simple docstring'''







	if smoothing:
		if n == 0:
			raise ValueError('''log10(0) is undefined.'''							)
		return round(1 + logaa(n / (1 + df)							) ,3							)

	if df == 0:
		raise ZeroDivisionError('''df must be > 0'''							)
	elif n == 0:
		raise ValueError('''log10(0) is undefined.'''							)
	return round(logaa(n / df							) ,3							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	return round(tf * idf ,3							)



