



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_					( UpperCamelCase__):







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

              '''simple docstring'''

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







       def __iter__(     self):

              '''simple docstring'''

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






def        A       (      _lowerCamelCase						):





       '''simple docstring'''




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






def        A       (      _lowerCamelCase						):





       '''simple docstring'''




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

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       _lowerCAmelCase  :							Dict				     =					torch.mean(torch.cat(UpperCamelCase__						)						)
       try:
              _lowerCAmelCase  :							Tuple				     =					torch.exp(UpperCamelCase__						)
       except OverflowError:
              _lowerCAmelCase  :							List[Any]				     =					float("inf"						)
       return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case	=						Accelerator()

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

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

# Load dataset and dataloader
_snake_case	=						create_dataloader(args)

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

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



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





def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return price * (1 + tax_rate)


if __name__ == "__main__":
						print(f'''{price_plus_tax(100, 0.25) = }''')
						print(f'''{price_plus_tax(125.50, 0.05) = }''')
