


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






def      a__ (				__lowercase     ,			__lowercase   )	->	int:
				_A					  =			np.argmax(__lowercase     ,			axis=1   )
				return np.sum(outputs == labels   )






def      a__ (				__lowercase   )	->	List[Any]:
				with open(__lowercase     ,			encoding="utf_8"   ) as f:
								_A					  =			csv.reader(__lowercase   )
								_A					  =			[]
								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__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				_A					  =			[]
				for dataset in encoded_datasets:
								_A					  =			len(__lowercase   )
								_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=-100     ,			dtype=np.intaa   )
								_A					  =			np.zeros((n_batch,)     ,			dtype=np.intaa   )
								for (
								    i,
								    (story, conta, conta, mc_label),
								) in enumerate(__lowercase   ):
												_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(__lowercase   ) - 1
												_A					  =			len(__lowercase   ) - 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(__lowercase   ) for t in all_inputs   )   )
				return tensor_datasets






def      a__ (				)	->	List[str]:
				_A					  =			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=6.25E-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."   )
				_A					  =			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   )

				_A					  =			torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
				_A					  =			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
				_A					  =			["_start_", "_delimiter_", "_classify_"]
				_A					  =			OpenAIGPTTokenizer.from_pretrained(args.model_name   )
				tokenizer.add_tokens(__lowercase   )
				_A					  =			tokenizer.convert_tokens_to_ids(__lowercase   )
				_A					  =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
				model.resize_token_embeddings(len(__lowercase   )   )
				model.to(__lowercase   )

				# Load and encode the datasets
				def tokenize_and_encode(__lowercase   ):
								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..."   )
				_A					  =			load_rocstories_dataset(args.train_dataset   )
				_A					  =			load_rocstories_dataset(args.eval_dataset   )
				_A					  =			(train_dataset, eval_dataset)
				_A					  =			tokenize_and_encode(__lowercase   )

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

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

				_A					  =			TensorDataset(*__lowercase   )
				_A					  =			RandomSampler(__lowercase   )
				_A					  =			DataLoader(__lowercase     ,			sampler=__lowercase     ,			batch_size=args.train_batch_size   )

				_A					  =			TensorDataset(*__lowercase   )
				_A					  =			SequentialSampler(__lowercase   )
				_A					  =			DataLoader(__lowercase     ,			sampler=__lowercase     ,			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(__lowercase   ) // args.gradient_accumulation_steps) + 1
								else:
												_A					  =			len(__lowercase   ) // 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(__lowercase     ,			lr=args.learning_rate     ,			eps=args.adam_epsilon   )
								_A					  =			get_linear_schedule_with_warmup(
								    __lowercase     ,			num_warmup_steps=args.warmup_steps     ,			num_training_steps=__lowercase   )

				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(__lowercase     ,			desc="Training"   )
												for step, batch in enumerate(__lowercase   ):
																_A					  =			tuple(t.to(__lowercase   ) for t in batch   )
																_A  ,		_A  ,		_A  ,		_A					  =			batch
																_A					  =			model(__lowercase     ,			mc_token_ids=__lowercase     ,			lm_labels=__lowercase     ,			mc_labels=__lowercase   )
																_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(__lowercase     ,			scheduler.get_lr()[0]   )

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

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

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

								_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(__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()



"""simple docstring"""


import numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()