





import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase=1024			, _lowerCAmelCase=1024			, _lowerCAmelCase=3.6					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   tokenizer.bos_token_id
										__SCREAMING_SNAKE_CASE:				Any           =   dataset
										__SCREAMING_SNAKE_CASE:				int           =   seq_length
										__SCREAMING_SNAKE_CASE:				Tuple           =   seq_length * chars_per_token * num_of_sequences






				def __iter__(  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   iter(self.dataset					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   True
										while more_examples:
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   [], 0
																while True:
																						if buffer_len >= self.input_characters:
																												break
																						try:
																												buffer.append(next(_lowerCAmelCase					)['''content''']					)
																												buffer_len += len(buffer[-1]					)
																						except StopIteration:
																												__SCREAMING_SNAKE_CASE:				Tuple           =   False
																												break
																__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer(_lowerCAmelCase			, truncation=_lowerCAmelCase					)['''input_ids''']
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   []
																for tokenized_input in tokenized_inputs:
																						all_token_ids.extend(tokenized_input + [self.concat_token_id]					)
																for i in range(0			, len(_lowerCAmelCase					)			, self.seq_length					):
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   all_token_ids[i : i + self.seq_length]
																						if len(_lowerCAmelCase					) == self.seq_length:
																												yield torch.tensor(_lowerCAmelCase					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   {'''streaming''': True}
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   load_dataset(args.dataset_name       ,			split='''train'''       ,			**UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Any           =   ConstantLengthDataset(UpperCamelCase__       ,			UpperCamelCase__       ,			seq_length=args.seq_length					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   DataLoader(UpperCamelCase__       ,			batch_size=args.batch_size					)
						return eval_dataloader






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[str]					)      ->    Optional[int]:

						"""simple docstring"""





						model.eval()
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   []
						for step, batch in enumerate(UpperCamelCase__					):
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE:				Any           =   model(UpperCamelCase__       ,			labels=UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Dict           =   outputs.loss.repeat(args.batch_size					)
												losses.append(accelerator.gather(UpperCamelCase__					)					)

												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						__SCREAMING_SNAKE_CASE:				int           =   torch.mean(torch.cat(UpperCamelCase__					)					)
						try:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.exp(UpperCamelCase__					)
						except OverflowError:
												__SCREAMING_SNAKE_CASE:				List[str]           =   float('''inf'''					)
						return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase							:       Optional[Any]					 =	Accelerator()

# Parse configuration
lowerCAmelCase							:       Union[str, Any]					 =	HfArgumentParser(EvaluationArguments)
lowerCAmelCase							:       str					 =	parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase							:       str					 =	logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
lowerCAmelCase							:       Union[str, Any]					 =	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase							:       Union[str, Any]					 =	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowerCAmelCase							:       Any					 =	create_dataloader(args)

# Prepare everything with our `accelerator`.
lowerCAmelCase ,	lowerCAmelCase							:       Optional[Any]					 =	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCAmelCase ,	lowerCAmelCase							:       Union[str, Any]					 =	evaluate(args)




logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')










def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float(moles / volume					) * nfactor					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (volume)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



