





# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def    __lowerCamelCase					(							__a   :List[str]    ,     __a   :List[Any]    ,     __a   :Union[str, Any]    ,     __a   :List[Any]    )		->				Dict:






		"""simple docstring"""

		A__        =   multiprocessing.Manager()
		A__        =   manager.list()

		A__        =   multiprocessing.Process(target=__a    ,     args=(check_program, result, timeout)    )
		p.start()
		p.join(timeout=timeout + 1    )
		if p.is_alive():
				p.kill()

		if not result:
				result.append("""timed out"""    )

		return {
		    "task_id": task_id,
		    "passed": result[0] == "passed",
		    "result": result[0],
		    "completion_id": completion_id,
		}







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Any    ,     __a   :List[Any]    )		->				Union[str, Any]:






		"""simple docstring"""

		with create_tempdir():
				# These system calls are needed when cleaning up tempdir.
				import os
				import shutil

				A__        =   shutil.rmtree
				A__        =   os.rmdir
				A__        =   os.chdir

				# Disable functionalities that can make destructive changes to the test.
				reliability_guard()

				# Run program.
				try:
						A__        =   {}
						with swallow_io():
								with time_limit(__a    ):
										exec(__a    ,     __a    )
						result.append("""passed"""    )
				except TimeoutException:
						result.append("""timed out"""    )
				except BaseException as e:
						result.append(F'failed: {e}'    )

				# Needed for cleaning up.
				A__        =   rmtree
				A__        =   rmdir
				A__        =   chdir







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :List[str]    )		->				Dict:






		"""simple docstring"""

		def signal_handler(__a   :List[Any]    ,     __a   :Optional[Any]    ):
				raise TimeoutException("""Timed out!"""    )

		signal.setitimer(signal.ITIMER_REAL    ,     __a    )
		signal.signal(signal.SIGALRM    ,     __a    )
		try:
				yield
		finally:
				signal.setitimer(signal.ITIMER_REAL    ,     0    )







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   WriteOnlyStringIO()
		with contextlib.redirect_stdout(__a    ):
				with contextlib.redirect_stderr(__a    ):
						with redirect_stdin(__a    ):
								yield







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Dict:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as dirname:
				with chdir(__a    ):
						yield dirname


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(io.StringIO    ):
			'''simple docstring'''




			def    a_       (			self   :    Any							,	*__lowerCAmelCase   :    List[str]							,	**__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Optional[int]   )	->	str:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Any   )	->	int:







					"""simple docstring"""






					raise OSError







			def    a_       (			self   :    str							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Union[str, Any]   )	->	int:







					"""simple docstring"""






					return False


class   A			(contextlib._RedirectStream    ):  # type: ignore
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    '''stdin'''







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :Union[str, Any]    )		->				List[str]:






		"""simple docstring"""

		if root == ".":
				yield
				return
		A__        =   os.getcwd()
		os.chdir(__a    )
		try:
				yield
		except BaseException as exc:
				raise exc
		finally:
				os.chdir(__a    )







def    __lowerCamelCase					(							__a   :Union[str, Any]=None    )		->				Dict:






		"""simple docstring"""

		if maximum_memory_bytes is not None:
				import resource

				resource.setrlimit(resource.RLIMIT_AS    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				resource.setrlimit(resource.RLIMIT_DATA    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				if not platform.uname().system == "Darwin":
						resource.setrlimit(resource.RLIMIT_STACK    ,     (maximum_memory_bytes, maximum_memory_bytes)    )

		faulthandler.disable()

		import builtins

		A__        =   None
		A__        =   None

		import os

		A__        =   """1"""

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None

		import shutil

		A__        =   None
		A__        =   None
		A__        =   None

		import subprocess

		A__        =   None  # type: ignore

		A__        =   None

		import sys

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None










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    :					Tuple =      logging.getLogger(__name__)







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[str]    )		->				Tuple:






		"""simple docstring"""

		A__        =   np.argmax(__a    ,     axis=1    )
		return np.sum(outputs == labels    )







def    __lowerCamelCase					(							__a   :Tuple    )		->				Dict:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[Any]    ,     __a   :Dict    ,     __a   :Optional[Any]    ,     __a   :Optional[Any]    ,     __a   :int    )		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   []
		for dataset in encoded_datasets:
				A__        =   len(__a    )
				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=-1_0_0    ,     dtype=np.intaa    )
				A__        =   np.zeros((n_batch,)    ,     dtype=np.intaa    )
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(__a    ):
						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(__a    ) - 1
						A__        =   len(__a    ) - 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(__a    ) for t in all_inputs    )    )
		return tensor_datasets







def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   argparse.ArgumentParser()
		parser.add_argument("""--model_name"""    ,     type=__a    ,     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=__a    ,     type=__a    ,     required=__a    ,     help="""The output directory where the model predictions and checkpoints will be written."""    ,     )
		parser.add_argument("""--train_dataset"""    ,     type=__a    ,     default=""""""    )
		parser.add_argument("""--eval_dataset"""    ,     type=__a    ,     default=""""""    )
		parser.add_argument("""--seed"""    ,     type=__a    ,     default=4_2    )
		parser.add_argument("""--num_train_epochs"""    ,     type=__a    ,     default=3    )
		parser.add_argument("""--train_batch_size"""    ,     type=__a    ,     default=8    )
		parser.add_argument("""--eval_batch_size"""    ,     type=__a    ,     default=1_6    )
		parser.add_argument("""--adam_epsilon"""    ,     default=1E-8    ,     type=__a    ,     help="""Epsilon for Adam optimizer."""    )
		parser.add_argument("""--max_grad_norm"""    ,     type=__a    ,     default=1    )
		parser.add_argument(
		    """--max_steps"""    ,     default=-1    ,     type=__a    ,     help=(
		        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
		    )    ,     )
		parser.add_argument(
		    """--gradient_accumulation_steps"""    ,     type=__a    ,     default=1    ,     help="""Number of updates steps to accumulate before                        performing a backward/update pass."""    ,     )
		parser.add_argument("""--learning_rate"""    ,     type=__a    ,     default=6.25E-5    )
		parser.add_argument("""--warmup_steps"""    ,     default=0    ,     type=__a    ,     help="""Linear warmup over warmup_steps."""    )
		parser.add_argument("""--lr_schedule"""    ,     type=__a    ,     default="""warmup_linear"""    )
		parser.add_argument("""--weight_decay"""    ,     type=__a    ,     default=0.01    )
		parser.add_argument("""--lm_coef"""    ,     type=__a    ,     default=0.9    )
		parser.add_argument("""--n_valid"""    ,     type=__a    ,     default=3_7_4    )

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

		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=__a    )
				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(__a    ,     __a    )    )

		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(__a    )
		A__        =   tokenizer.convert_tokens_to_ids(__a    )
		A__        =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
		model.resize_token_embeddings(len(__a    )    )
		model.to(__a    )

		# Load and encode the datasets
		def tokenize_and_encode(__a   :Tuple    ):
				if isinstance(__a    ,     __a    ):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__a    )    )
				elif isinstance(__a    ,     __a    ):
						return obj
				return [tokenize_and_encode(__a    ) 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(__a    )

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

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

		A__        =   TensorDataset(*__a    )
		A__        =   RandomSampler(__a    )
		A__        =   DataLoader(__a    ,     sampler=__a    ,     batch_size=args.train_batch_size    )

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

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

    # Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				A__        =   model.module if hasattr(__a    ,     """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    ,     __a    )
				A__        =   os.path.join(args.output_dir    ,     __a    )

				torch.save(model_to_save.state_dict()    ,     __a    )
				model_to_save.config.to_json_file(__a    )
				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(__a    )

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

						A__        =   mc_logits.detach().cpu().numpy()
						A__        =   mc_labels.to("""cpu"""    ).numpy()
						A__        =   accuracy(__a    ,     __a    )

						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(__a    ,     """w"""    ) as writer:
						logger.info("""***** Eval results *****"""    )
						for key in sorted(result.keys()    ):
								logger.info("""  %s = %s"""    ,     __a    ,     str(result[key]    )    )
								writer.write("""%s = %s\n""" % (key, str(result[key]    ))    )


if __name__ == "__main__":
					main()



