


'''simple docstring'''





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__					(					a_   ):





			'''simple docstring'''

			def __init__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			Dict=1_024			,    lowerCAmelCase_							:			List[Any]=1_024			,    lowerCAmelCase_							:			Dict=3.6	)      ->		Union[str, Any]:
						__A=	tokenizer
						__A=	tokenizer.bos_token_id
						__A=	dataset
						__A=	seq_length
						__A=	seq_length * chars_per_token * num_of_sequences

			def __iter__(  self							:			Any	)      ->		Optional[Any]:
						__A=	iter(self.dataset	)
						__A=	True
						while more_examples:
									__A,	__A=	[], 0
									while True:
												if buffer_len >= self.input_characters:
															break
												try:
															buffer.append(next(lowerCAmelCase_	)['content']	)
															buffer_len += len(buffer[-1]	)
												except StopIteration:
															__A=	False
															break
									__A=	tokenizer(lowerCAmelCase_			,    truncation=lowerCAmelCase_	)['input_ids']
									__A=	[]
									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	):
												__A=	all_token_ids[i : i + self.seq_length]
												if len(lowerCAmelCase_	) == self.seq_length:
															yield torch.tensor(lowerCAmelCase_	)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict ):






			"""simple docstring"""




			__A=	{'streaming': True}
			__A=	load_dataset(args.dataset_name,split='train',**_SCREAMING_SNAKE_CASE )
			__A=	ConstantLengthDataset(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,seq_length=args.seq_length )
			__A=	DataLoader(_SCREAMING_SNAKE_CASE,batch_size=args.batch_size )
			return eval_dataloader






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




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

						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			__A=	torch.mean(torch.cat(_SCREAMING_SNAKE_CASE ) )
			try:
						__A=	torch.exp(_SCREAMING_SNAKE_CASE )
			except OverflowError:
						__A=	float('inf' )
			return loss.item(), perplexity.item()


# Setup Accelerator
UpperCAmelCase__  			=				Accelerator()

# Parse configuration
UpperCAmelCase__  			=				HfArgumentParser(EvaluationArguments)
UpperCAmelCase__  			=				parser.parse_args()
set_seed(args.seed)

# Logging
UpperCAmelCase__  			=				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
UpperCAmelCase__  			=				AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase__  			=				AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase__  			=				create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCAmelCase__			,  UpperCAmelCase__  			=				accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
UpperCAmelCase__			,  UpperCAmelCase__  			=				evaluate(args)






logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase__  			=				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



