





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        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(      self      :					Any      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					List[str]=10_24      ,				UpperCamelCase      :					int=10_24      ,				UpperCamelCase      :					Any=3.6							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	tokenizer
    _snake_case   :				List[Any]														=	tokenizer.bos_token_id
    _snake_case   :				Optional[int]														=	dataset
    _snake_case   :				Optional[Any]														=	seq_length
    _snake_case   :				Optional[Any]														=	seq_length * chars_per_token * num_of_sequences







  def __iter__(      self      :					Optional[int]							):


    '''simple docstring'''





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




def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]				)->		Tuple:
  _snake_case   :				int														=	{'streaming': True}
  _snake_case   :				Tuple														=	load_dataset(args.dataset_name    ,							split='train'    ,							**lowerCAmelCase				)
  _snake_case   :				List[Any]														=	ConstantLengthDataset(lowerCAmelCase    ,							lowerCAmelCase    ,							seq_length=args.seq_length				)
  _snake_case   :				int														=	DataLoader(lowerCAmelCase    ,							batch_size=args.batch_size				)
  return eval_dataloader


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]				)->		Dict:
  model.eval()
  _snake_case   :				List[Any]														=	[]
  for step, batch in enumerate(lowerCAmelCase				):
    with torch.no_grad():
      _snake_case   :				List[Any]														=	model(lowerCAmelCase    ,							labels=lowerCAmelCase				)
    _snake_case   :				Tuple														=	outputs.loss.repeat(args.batch_size				)
    losses.append(accelerator.gather(lowerCAmelCase				)				)

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  _snake_case   :				Union[str, Any]														=	torch.mean(torch.cat(lowerCAmelCase				)				)
  try:
    _snake_case   :				Any														=	torch.exp(lowerCAmelCase				)
  except OverflowError:
    _snake_case   :				Tuple														=	float('inf'				)
  return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase_						   =    Accelerator()

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

# Logging
lowerCAmelCase_						   =    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_						   =    AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase_						   =    AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowerCAmelCase_						   =    create_dataloader(args)

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

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







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











from functools import reduce

lowerCAmelCase_						   =    (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def     lowerCamelCase_	(      lowerCAmelCase:	str = N				)->		int:
  return max(
      # mypy cannot properly interpret reduce
      int(reduce(lambda lowerCAmelCase    ,							lowerCAmelCase				: str(int(lowerCAmelCase				) * int(lowerCAmelCase				)				)    ,							n[i : i + 13]				)				)
      for i in range(len(lowerCAmelCase				) - 12				)				)


if __name__ == "__main__":
      print(F"""{solution() = }""")




