






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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict,  lowerCamelCase						:    List[str]=1_024,  lowerCamelCase						:    str=1_024,  lowerCamelCase						:    Dict=3.6  ):

								'''simple docstring'''
								lowercase__    =			tokenizer
								lowercase__    =			tokenizer.bos_token_id
								lowercase__    =			dataset
								lowercase__    =			seq_length
								lowercase__    =			seq_length * chars_per_token * num_of_sequences




				def __iter__(     self						:    Tuple  ):

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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			{'''streaming''': True}
				lowercase__    =			load_dataset(args.dataset_name		,   split='''train'''		,   **lowerCamelCase_				)
				lowercase__    =			ConstantLengthDataset(lowerCamelCase_		,   lowerCamelCase_		,   seq_length=args.seq_length				)
				lowercase__    =			DataLoader(lowerCamelCase_		,   batch_size=args.batch_size				)
				return eval_dataloader






def   a			(						lowerCamelCase_				):






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

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


# Setup Accelerator
A__	:			List[Any]			   = Accelerator()

# Parse configuration
A__	:			List[Any]			   = HfArgumentParser(EvaluationArguments)
A__	:			Tuple			   = parser.parse_args()
set_seed(args.seed)

# Logging
A__	:			int			   = 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
A__	:			Any			   = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A__	:			Optional[Any]			   = AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A__	:			Optional[Any]			   = create_dataloader(args)

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

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




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












from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




