

'''simple docstring'''


def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    float:
 if mass < 0:
  raise ValueError('The mass of a body cannot be negative'  )
 return 0.5 * mass * abs(UpperCamelCase  ) * abs(UpperCamelCase  )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)









'''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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Any       	=					np.argmax(UpperCamelCase   ,axis=1  )
	return np.sum(outputs == labels  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	with open(UpperCamelCase   ,encoding='utf_8'  ) as f:
		UpperCAmelCase_		:      int       	=					csv.reader(UpperCamelCase  )
		UpperCAmelCase_		:      Tuple       	=					[]
		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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      str       	=					[]
	for dataset in encoded_datasets:
		UpperCAmelCase_		:      str       	=					len(UpperCamelCase  )
		UpperCAmelCase_		:      str       	=					np.zeros((n_batch, 2, input_len)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch, 2)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      Any       	=					np.full((n_batch, 2, input_len)   ,fill_value=-1_0_0   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch,)   ,dtype=np.intaa  )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      List[Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Tuple       	=					with_conta
			UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      int       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Optional[int]       	=					mc_label
		UpperCAmelCase_		:      Tuple       	=					(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 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Union[str, Any]       	=					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=4_2  )
	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=1_6  )
	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_7_4  )

	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.'  )
	UpperCAmelCase_		:      Optional[Any]       	=					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  )

	UpperCAmelCase_		:      Union[str, Any]       	=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
	UpperCAmelCase_		:      Dict       	=					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
	UpperCAmelCase_		:      Optional[int]       	=					['_start_', '_delimiter_', '_classify_']
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTTokenizer.from_pretrained(args.model_name  )
	tokenizer.add_tokens(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.convert_tokens_to_ids(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					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...'  )
	UpperCAmelCase_		:      List[str]       	=					load_rocstories_dataset(args.train_dataset  )
	UpperCAmelCase_		:      Optional[Any]       	=					load_rocstories_dataset(args.eval_dataset  )
	UpperCAmelCase_		:      Dict       	=					(train_dataset, eval_dataset)
	UpperCAmelCase_		:      Union[str, Any]       	=					tokenize_and_encode(UpperCamelCase  )

	# Compute the max input length for the Transformer
	UpperCAmelCase_		:      Dict       	=					model.config.n_positions // 2 - 2
	UpperCAmelCase_		:      int       	=					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  )
	UpperCAmelCase_		:      Dict       	=					min(UpperCamelCase   ,model.config.n_positions  )  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	UpperCAmelCase_		:      Optional[int]       	=					pre_process_datasets(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,*UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					tensor_datasets[0], tensor_datasets[1]

	UpperCAmelCase_		:      Optional[int]       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					RandomSampler(UpperCamelCase  )
	UpperCAmelCase_		:      int       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.train_batch_size  )

	UpperCAmelCase_		:      int       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					SequentialSampler(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.eval_batch_size  )

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			UpperCAmelCase_		:      List[str]       	=					args.max_steps
			UpperCAmelCase_		:      List[str]       	=					args.max_steps // (len(UpperCamelCase  ) // args.gradient_accumulation_steps) + 1
		else:
			UpperCAmelCase_		:      List[str]       	=					len(UpperCamelCase  ) // args.gradient_accumulation_steps * args.num_train_epochs

		UpperCAmelCase_		:      Any       	=					list(model.named_parameters()  )
		UpperCAmelCase_		:      Any       	=					['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		UpperCAmelCase_		:      Dict       	=					[
		    {
		        '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},
		]
		UpperCAmelCase_		:      Any       	=					AdamW(UpperCamelCase   ,lr=args.learning_rate   ,eps=args.adam_epsilon  )
		UpperCAmelCase_		:      List[str]       	=					get_linear_schedule_with_warmup(
		    UpperCamelCase   ,num_warmup_steps=args.warmup_steps   ,num_training_steps=UpperCamelCase  )

	if args.do_train:
		UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs  )   ,desc='Epoch'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					0
			UpperCAmelCase_		:      Any       	=					0
			UpperCAmelCase_		:      List[Any]       	=					tqdm(UpperCamelCase   ,desc='Training'  )
			for step, batch in enumerate(UpperCamelCase  ):
				UpperCAmelCase_		:      Optional[Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					batch
				UpperCAmelCase_		:      Dict       	=					model(UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )
				UpperCAmelCase_		:      int       	=					args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				UpperCAmelCase_		:      Dict       	=					(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				UpperCAmelCase_		:      List[Any]       	=					'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
		UpperCAmelCase_		:      int       	=					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`
		UpperCAmelCase_		:      int       	=					os.path.join(args.output_dir   ,UpperCamelCase  )
		UpperCAmelCase_		:      Any       	=					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
		UpperCAmelCase_		:      int       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
		UpperCAmelCase_		:      Dict       	=					OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
		model.to(UpperCamelCase  )

	if args.do_eval:
		model.eval()
		UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					0, 0
		UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					0, 0
		for batch in tqdm(UpperCamelCase   ,desc='Evaluating'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
			UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					batch
			with torch.no_grad():
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					model(
				    UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					mc_logits.detach().cpu().numpy()
			UpperCAmelCase_		:      int       	=					mc_labels.to('cpu'  ).numpy()
			UpperCAmelCase_		:      Optional[int]       	=					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

		UpperCAmelCase_		:      str       	=					eval_loss / nb_eval_steps
		UpperCAmelCase_		:      str       	=					eval_accuracy / nb_eval_examples
		UpperCAmelCase_		:      List[Any]       	=					tr_loss / nb_tr_steps if args.do_train else None
		UpperCAmelCase_		:      Dict       	=					{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

		UpperCAmelCase_		:      Union[str, Any]       	=					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()






