




'''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       :						int								=		logging.getLogger(__name__)


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   Tuple						)      ->      Dict:
			__snake_case			=		np.argmax(_UpperCAmelCase		,				axis=1						)
			return np.sum(outputs == labels						)


def __UpperCAmelCase	(   _UpperCAmelCase		:   str						)      ->      Dict:
			with open(_UpperCAmelCase		,				encoding="utf_8"						) as f:
						__snake_case			=		csv.reader(_UpperCAmelCase						)
						__snake_case			=		[]
						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 __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   str		,				_UpperCAmelCase		:   Optional[Any]						)      ->      List[Any]:
			__snake_case			=		[]
			for dataset in encoded_datasets:
						__snake_case			=		len(_UpperCAmelCase						)
						__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=-1_00		,				dtype=np.intaa						)
						__snake_case			=		np.zeros((n_batch,)		,				dtype=np.intaa						)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(_UpperCAmelCase						):
									__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(_UpperCAmelCase						) - 1
									__snake_case			=		len(_UpperCAmelCase						) - 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(_UpperCAmelCase						) for t in all_inputs						)						)
			return tensor_datasets


def __UpperCAmelCase	(   )      ->      Optional[int]:
			__snake_case			=		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=42						)
			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=16						)
			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=6.2_5E-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_74						)

			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."						)
			__snake_case			=		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						)

			__snake_case			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"						)
			__snake_case			=		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
			__snake_case			=		["_start_", "_delimiter_", "_classify_"]
			__snake_case			=		OpenAIGPTTokenizer.from_pretrained(args.model_name						)
			tokenizer.add_tokens(_UpperCAmelCase						)
			__snake_case			=		tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
			__snake_case			=		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		:   Optional[Any]						):
						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..."						)
			__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(_UpperCAmelCase						)

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

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

			__snake_case			=		TensorDataset(*_UpperCAmelCase						)
			__snake_case			=		RandomSampler(_UpperCAmelCase						)
			__snake_case			=		DataLoader(_UpperCAmelCase		,				sampler=_UpperCAmelCase		,				batch_size=args.train_batch_size						)

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

			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(_UpperCAmelCase		,				desc="Training"						)
									for step, batch in enumerate(_UpperCAmelCase						):
												__snake_case			=		tuple(t.to(_UpperCAmelCase						) for t in batch						)
												__snake_case		,     __snake_case		,     __snake_case		,     __snake_case			=		batch
												__snake_case			=		model(_UpperCAmelCase		,				mc_token_ids=_UpperCAmelCase		,				lm_labels=_UpperCAmelCase		,				mc_labels=_UpperCAmelCase						)
												__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(_UpperCAmelCase		,				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(_UpperCAmelCase		,				"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		,				_UpperCAmelCase						)
						__snake_case			=		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
						__snake_case			=		OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
						__snake_case			=		OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
						model.to(_UpperCAmelCase						)

			if args.do_eval:
						model.eval()
						__snake_case		,     __snake_case			=		0, 0
						__snake_case		,     __snake_case			=		0, 0
						for batch in tqdm(_UpperCAmelCase		,				desc="Evaluating"						):
									__snake_case			=		tuple(t.to(_UpperCAmelCase						) 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(
												    _UpperCAmelCase		,				mc_token_ids=_UpperCAmelCase		,				lm_labels=_UpperCAmelCase		,				mc_labels=_UpperCAmelCase						)

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

						__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(_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()









'''simple docstring'''






from __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()