






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			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A							,  _A=1024							,  _A=1024							,  _A=3.6) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				tokenizer
     _UpperCAmelCase				:      Union[str, Any]			  =				tokenizer.bos_token_id
     _UpperCAmelCase				:      Dict			  =				dataset
     _UpperCAmelCase				:      Dict			  =				seq_length
     _UpperCAmelCase				:      Any			  =				seq_length * chars_per_token * num_of_sequences


 def __iter__(	self) ->		Union[str, Any]:

     """simple docstring"""



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



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

def      _lowerCamelCase	(						__A    :							str )      ->					str:
    model.eval()
    _UpperCAmelCase				:      List[Any]			  =				[]
    for step, batch in enumerate(__A ):
        with torch.no_grad():
            _UpperCAmelCase				:      str			  =				model(__A		,		labels=__A )
        _UpperCAmelCase				:      str			  =				outputs.loss.repeat(args.batch_size )
        losses.append(accelerator.gather(__A ) )

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


# Setup Accelerator
SCREAMING_SNAKE_CASE 							=						Accelerator()

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

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

# Load dataset and dataloader
SCREAMING_SNAKE_CASE 							=						create_dataloader(args)

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

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


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












def      _lowerCamelCase	(						__A    :							int )      ->					bool:
    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      Tuple			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )
    if number < 0:
        return False
    _UpperCAmelCase				:      List[str]			  =				number * number
    while number > 0:
        if number % 10 != number_square % 10:
            return False
        number //= 10
        number_square //= 10
    return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()




