


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

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Tuple				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= np.argmax(_A					,   axis=1				)
	return np.sum(outputs == labels				)

def 			__UpperCamelCase    (    _A    :				List[Any]				)    ->      List[Any]:




	"""simple docstring"""





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




	"""simple docstring"""





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




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 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=42				)
	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=16				)
	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_74				)

	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.'				)
	lowerCAmelCase					:    Dict      							= 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				)

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

	# Compute the max input length for the Transformer
	lowerCAmelCase					:    List[Any]      							= model.config.n_positions // 2 - 2
	lowerCAmelCase					:    Optional[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				)
	lowerCAmelCase					:    int      							= min(_A					,   model.config.n_positions				)  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	lowerCAmelCase					:    Optional[Any]      							= pre_process_datasets(_A					,   _A					,   _A					,   *_A				)
	lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= tensor_datasets[0], tensor_datasets[1]

	lowerCAmelCase					:    int      							= TensorDataset(*_A				)
	lowerCAmelCase					:    Tuple      							= RandomSampler(_A				)
	lowerCAmelCase					:    Optional[int]      							= DataLoader(_A					,   sampler=_A					,   batch_size=args.train_batch_size				)

	lowerCAmelCase					:    Any      							= TensorDataset(*_A				)
	lowerCAmelCase					:    Union[str, Any]      							= SequentialSampler(_A				)
	lowerCAmelCase					:    Optional[Any]      							= DataLoader(_A					,   sampler=_A					,   batch_size=args.eval_batch_size				)

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

		lowerCAmelCase					:    Optional[int]      							= list(model.named_parameters()				)
		lowerCAmelCase					:    Any      							= ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		lowerCAmelCase					:    Tuple      							= [
		    {
		        '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},
		]
		lowerCAmelCase					:    Dict      							= AdamW(_A					,   lr=args.learning_rate					,   eps=args.adam_epsilon				)
		lowerCAmelCase					:    Union[str, Any]      							= get_linear_schedule_with_warmup(
		    _A					,   num_warmup_steps=args.warmup_steps					,   num_training_steps=_A				)

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

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

			lowerCAmelCase					:    Tuple      							= mc_logits.detach().cpu().numpy()
			lowerCAmelCase					:    Any      							= mc_labels.to('cpu'				).numpy()
			lowerCAmelCase					:    Any      							= 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

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

		lowerCAmelCase					:    Optional[int]      							= 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()




'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Tuple		     =	{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Union[str, Any]			      = """open-llama"""



	def __init__(					self ,					snake_case__=10_0000 ,					snake_case__=4096 ,					snake_case__=1_1008 ,					snake_case__=32 ,					snake_case__=32 ,					snake_case__="silu" ,					snake_case__=2048 ,					snake_case__=0.0_2 ,					snake_case__=1e-6 ,					snake_case__=True ,					snake_case__=0 ,					snake_case__=1 ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		lowerCAmelCase					:    Tuple      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= max_position_embeddings
		lowerCAmelCase					:    List[Any]      							= hidden_size
		lowerCAmelCase					:    List[Any]      							= intermediate_size
		lowerCAmelCase					:    Tuple      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Union[str, Any]      							= initializer_range
		lowerCAmelCase					:    str      							= rms_norm_eps
		lowerCAmelCase					:    Optional[int]      							= use_cache
		lowerCAmelCase					:    Dict      							= kwargs.pop(
		    'use_memorry_efficient_attention' ,					snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= use_stable_embedding
		lowerCAmelCase					:    Tuple      							= shared_input_output_embedding
		lowerCAmelCase					:    Tuple      							= rope_scaling
		self._rope_scaling_validation()

		super().__init__(
		    pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					tie_word_embeddings=snake_case__ ,					**snake_case__ ,					)



	def 				lowercase     (					self    ):
		if self.rope_scaling is None:
			return

		if not isinstance(self.rope_scaling ,					snake_case__    ) or len(self.rope_scaling    ) != 2:
			raise ValueError(
			    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
			    f"got {self.rope_scaling}"    )
		lowerCAmelCase					:    List[Any]      							= self.rope_scaling.get('type' ,					snake_case__    )
		lowerCAmelCase					:    List[str]      							= self.rope_scaling.get('factor' ,					snake_case__    )
		if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
			raise ValueError(
			    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"    )
		if rope_scaling_factor is None or not isinstance(snake_case__ ,					snake_case__    ) or rope_scaling_factor <= 1.0:
			raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"    )
