"""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  :  Optional[int]=				logging.getLogger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Any						)							->    str:

	'''simple docstring'''





	__snake_case    :							Dict   	=				np.argmax(__UpperCamelCase    ,							axis=1						)
	return np.sum(outputs == labels						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Optional[Any]:

	'''simple docstring'''





	with open(__UpperCamelCase    ,							encoding='utf_8'						) as f:
		__snake_case    :							List[Any]   	=				csv.reader(__UpperCamelCase						)
		__snake_case    :							Any   	=				[]
		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_					:       Union[str, Any]    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       int						)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							List[Any]   	=				[]
	for dataset in encoded_datasets:
		__snake_case    :							str   	=				len(__UpperCamelCase						)
		__snake_case    :							str   	=				np.zeros((n_batch, 2, input_len)    ,							dtype=np.intaa						)
		__snake_case    :							Tuple   	=				np.zeros((n_batch, 2)    ,							dtype=np.intaa						)
		__snake_case    :							Optional[int]   	=				np.full((n_batch, 2, input_len)    ,							fill_value=-1_00    ,							dtype=np.intaa						)
		__snake_case    :							Union[str, Any]   	=				np.zeros((n_batch,)    ,							dtype=np.intaa						)
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(__UpperCamelCase						):
			__snake_case    :							Optional[int]   	=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__snake_case    :							int   	=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__snake_case    :							Dict   	=				with_conta
			__snake_case    :							List[Any]   	=				with_conta
			__snake_case    :							str   	=				len(__UpperCamelCase						) - 1
			__snake_case    :							Optional[Any]   	=				len(__UpperCamelCase						) - 1
			__snake_case    :							List[Any]   	=				with_conta
			__snake_case    :							Any   	=				with_conta
			__snake_case    :							Optional[int]   	=				mc_label
		__snake_case    :							Optional[int]   	=				(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       (					)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Dict   	=				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=6.25E-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_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    :							int   	=				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    :							Tuple   	=				torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
	__snake_case    :							int   	=				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    :							List[Any]   	=				['_start_', '_delimiter_', '_classify_']
	__snake_case    :							Any   	=				OpenAIGPTTokenizer.from_pretrained(args.model_name						)
	tokenizer.add_tokens(__UpperCamelCase						)
	__snake_case    :							Optional[Any]   	=				tokenizer.convert_tokens_to_ids(__UpperCamelCase						)
	__snake_case    :							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_					:       List[str]						):
		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    :							int   	=				load_rocstories_dataset(args.train_dataset						)
	__snake_case    :							List[str]   	=				load_rocstories_dataset(args.eval_dataset						)
	__snake_case    :							str   	=				(train_dataset, eval_dataset)
	__snake_case    :							int   	=				tokenize_and_encode(__UpperCamelCase						)

	# Compute the max input length for the Transformer
	__snake_case    :							Dict   	=				model.config.n_positions // 2 - 2
	__snake_case    :							Tuple   	=				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    :							List[Any]   	=				min(__UpperCamelCase    ,							model.config.n_positions						)  # Max size of input for the pre-trained model

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

	__snake_case    :							List[str]   	=				TensorDataset(*__UpperCamelCase						)
	__snake_case    :							str   	=				RandomSampler(__UpperCamelCase						)
	__snake_case    :							Optional[int]   	=				DataLoader(__UpperCamelCase    ,							sampler=__UpperCamelCase    ,							batch_size=args.train_batch_size						)

	__snake_case    :							int   	=				TensorDataset(*__UpperCamelCase						)
	__snake_case    :							int   	=				SequentialSampler(__UpperCamelCase						)
	__snake_case    :							Optional[Any]   	=				DataLoader(__UpperCamelCase    ,							sampler=__UpperCamelCase    ,							batch_size=args.eval_batch_size						)

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			__snake_case    :							Optional[int]   	=				args.max_steps
			__snake_case    :							Optional[int]   	=				args.max_steps // (len(__UpperCamelCase						) // args.gradient_accumulation_steps) + 1
		else:
			__snake_case    :							Union[str, Any]   	=				len(__UpperCamelCase						) // args.gradient_accumulation_steps * args.num_train_epochs

		__snake_case    :							List[Any]   	=				list(model.named_parameters()						)
		__snake_case    :							Optional[Any]   	=				['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		__snake_case    :							List[Any]   	=				[
		    {
		        '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    :							Dict   	=				AdamW(__UpperCamelCase    ,							lr=args.learning_rate    ,							eps=args.adam_epsilon						)
		__snake_case    :							Any   	=				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    :							str   	=				0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs						)    ,							desc='Epoch'						):
			__snake_case    :							Union[str, Any]   	=				0
			__snake_case    :							List[Any]   	=				0
			__snake_case    :							int   	=				tqdm(__UpperCamelCase    ,							desc='Training'						)
			for step, batch in enumerate(__UpperCamelCase						):
				__snake_case    :							List[str]   	=				tuple(t.to(__UpperCamelCase						) for t in batch						)
				__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							List[Any]   	=				batch
				__snake_case    :							Any   	=				model(__UpperCamelCase    ,							mc_token_ids=__UpperCamelCase    ,							lm_labels=__UpperCamelCase    ,							mc_labels=__UpperCamelCase						)
				__snake_case    :							List[Any]   	=				args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				__snake_case    :							int   	=				(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				__snake_case    :							Optional[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
		__snake_case    :							str   	=				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    :							str   	=				os.path.join(args.output_dir    ,							__UpperCamelCase						)
		__snake_case    :							Optional[int]   	=				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    :							List[Any]   	=				OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
		__snake_case    :							Tuple   	=				OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
		model.to(__UpperCamelCase						)

	if args.do_eval:
		model.eval()
		__snake_case	,       __snake_case    :							Any   	=				0, 0
		__snake_case	,       __snake_case    :							Tuple   	=				0, 0
		for batch in tqdm(__UpperCamelCase    ,							desc='Evaluating'						):
			__snake_case    :							Tuple   	=				tuple(t.to(__UpperCamelCase						) for t in batch						)
			__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Dict   	=				batch
			with torch.no_grad():
				__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Optional[int]   	=				model(
				    __UpperCamelCase    ,							mc_token_ids=__UpperCamelCase    ,							lm_labels=__UpperCamelCase    ,							mc_labels=__UpperCamelCase						)

			__snake_case    :							Union[str, Any]   	=				mc_logits.detach().cpu().numpy()
			__snake_case    :							Dict   	=				mc_labels.to('cpu'						).numpy()
			__snake_case    :							Any   	=				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    :							int   	=				eval_loss / nb_eval_steps
		__snake_case    :							Optional[Any]   	=				eval_accuracy / nb_eval_examples
		__snake_case    :							Union[str, Any]   	=				tr_loss / nb_tr_steps if args.do_train else None
		__snake_case    :							Optional[int]   	=				{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

		__snake_case    :							List[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()



"""simple docstring"""







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 	UpperCamelCase    :



				def __init__(self       :   Tuple			,						_A       :   List[Any]			,						_A       :   int=sys.maxsize)					->			int:
					__snake_case    :							Dict   	=				'bilinear'
					__snake_case    :							int   	=				max_size
					__snake_case    :							Any   	=				short_edge_length






				def __call__(self       :   Optional[Any]			,						_A       :   Any)					->			Dict:
					__snake_case    :							Any   	=				[]
					for img in imgs:
						__snake_case	,       __snake_case    :							int   	=				img.shape[:2]
						# later: provide list and randomly choose index for resize
						__snake_case    :							Optional[Any]   	=				np.random.randint(self.short_edge_length[0]			,						self.short_edge_length[1] + 1)
						if size == 0:
							return img
						__snake_case    :							int   	=				size * 1.0 / min(_A			,						_A)
						if h < w:
							__snake_case	,       __snake_case    :							str   	=				size, scale * w
						else:
							__snake_case	,       __snake_case    :							List[str]   	=				scale * h, size
						if max(_A			,						_A) > self.max_size:
							__snake_case    :							str   	=				self.max_size * 1.0 / max(_A			,						_A)
							__snake_case    :							Any   	=				newh * scale
							__snake_case    :							Any   	=				neww * scale
						__snake_case    :							Optional[int]   	=				int(neww + 0.5)
						__snake_case    :							str   	=				int(newh + 0.5)

						if img.dtype == np.uinta:
							__snake_case    :							str   	=				Image.fromarray(_A)
							__snake_case    :							str   	=				pil_image.resize((neww, newh)			,						PILImageResampling.BILINEAR)
							__snake_case    :							Tuple   	=				np.asarray(_A)
						else:
							__snake_case    :							str   	=				img.permute(2			,						0			,						1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
							__snake_case    :							Dict   	=				nn.functional.interpolate(
							    _A			,						(newh, neww)			,						mode=self.interp_method			,						align_corners=_A).squeeze(0)
						img_augs.append(_A)

					return img_augs







class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   List[str])					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,						cfg.INPUT.MAX_SIZE_TEST)
					__snake_case    :							Union[str, Any]   	=				cfg.INPUT.FORMAT
					__snake_case    :							List[Any]   	=				cfg.SIZE_DIVISIBILITY
					__snake_case    :							Tuple   	=				cfg.PAD_VALUE
					__snake_case    :							int   	=				cfg.INPUT.MAX_SIZE_TEST
					__snake_case    :							List[Any]   	=				cfg.MODEL.DEVICE
					__snake_case    :							Tuple   	=				torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,						1			,						1)
					__snake_case    :							Any   	=				torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,						1			,						1)
					__snake_case    :							Union[str, Any]   	=				lambda _A: (x - self.pixel_mean) / self.pixel_std



				def       _lowercase  (self       :   Tuple			,						_A       :   str)					->			int:
					__snake_case    :							str   	=				tuple(max(_A) for s in zip(*[img.shape for img in images]))
					__snake_case    :							int   	=				[im.shape[-2:] for im in images]
					__snake_case    :							int   	=				[
					    nn.functional.pad(
					        _A			,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,						value=self.pad_value			,						)
					    for size, im in zip(_A			,						_A)
					]

					return torch.stack(_A), torch.tensor(_A)






				def __call__(self       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Union[str, Any]=False)					->			Optional[int]:
					with torch.no_grad():
						if not isinstance(_A			,						_A):
							__snake_case    :							str   	=				[images]
						if single_image:
							assert len(_A) == 1
						for i in range(len(_A)):
							if isinstance(images[i]			,						torch.Tensor):
								images.insert(_A			,						images.pop(_A).to(self.device).float())
							elif not isinstance(images[i]			,						torch.Tensor):
								images.insert(
								    _A			,						torch.as_tensor(img_tensorize(images.pop(_A)			,						input_format=self.input_format))
								    .to(self.device)
								    .float()			,						)
            # resize smallest edge
						__snake_case    :							Any   	=				torch.tensor([im.shape[:2] for im in images])
						__snake_case    :							Tuple   	=				self.aug(_A)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__snake_case    :							List[str]   	=				[self.normalizer(_A) for x in images]
						# now pad them to do the following operations
						__snake_case	,       __snake_case    :							List[Any]   	=				self.pad(_A)
						# Normalize

						if self.size_divisibility > 0:
							raise NotImplementedError()
						# pad
						__snake_case    :							int   	=				torch.true_divide(_A			,						_A)
						if single_image:
							return images[0], sizes[0], scales_yx[0]
						else:
							return images, sizes, scales_yx







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Any						)							->    int:

	'''simple docstring'''





	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple[int, int]						)							->    Union[str, Any]:

	'''simple docstring'''





	assert torch.isfinite(UpperCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
	__snake_case	,       __snake_case    :							Optional[int]   	=				box_size
	tensor[:, 0].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 1].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 2].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 3].clamp_(min=0    ,							max=UpperCAmelCase_						)





