





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   ):

  """simple docstring"""


  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Optional[Any]=1_0_2_4 ,  lowerCAmelCase_			:		Optional[int]=1_0_2_4 ,  lowerCAmelCase_			:		Optional[int]=3.6					)							->						Optional[int]:
     __lowerCAmelCase							     =     tokenizer
     __lowerCAmelCase							     =     tokenizer.bos_token_id
     __lowerCAmelCase							     =     dataset
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     seq_length * chars_per_token * num_of_sequences






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





def   a_   (			lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     {'streaming': True}
   __lowerCAmelCase							     =     load_dataset(args.dataset_name,     split='train',     **lowerCAmelCase_     )
   __lowerCAmelCase							     =     ConstantLengthDataset(lowerCAmelCase_,     lowerCAmelCase_,     seq_length=args.seq_length     )
   __lowerCAmelCase							     =     DataLoader(lowerCAmelCase_,     batch_size=args.batch_size     )
   return eval_dataloader

def   a_   (			lowerCAmelCase_				:      List[Any]     ):
   model.eval()
   __lowerCAmelCase							     =     []
   for step, batch in enumerate(lowerCAmelCase_     ):
      with torch.no_grad():
         __lowerCAmelCase							     =     model(lowerCAmelCase_,     labels=lowerCAmelCase_     )
      __lowerCAmelCase							     =     outputs.loss.repeat(args.batch_size     )
      losses.append(accelerator.gather(lowerCAmelCase_     )     )

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


# Setup Accelerator
_snake_case  :   List[str]						   =  Accelerator()

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

# Logging
_snake_case  :   Tuple						   =  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  :   List[str]						   =  AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case  :   Union[str, Any]						   =  AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case  :   Union[str, Any]						   =  create_dataloader(args)

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

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




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








class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     __lowerCAmelCase							     =     name
     __lowerCAmelCase							     =     value
     __lowerCAmelCase							     =     weight






  def __repr__(    self			:		Union[str, Any]					)							->						List[str]:
     return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     return self.value






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     return self.name






  def     lowercase		(    self			:		List[Any]					)							->						Tuple:
     return self.weight






  def     lowercase		(    self			:		int					)							->						Dict:
     return self.value / self.weight





def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     []
   for i in range(len(lowerCAmelCase_     )     ):
      menu.append(Things(name[i],     value[i],     weight[i]     )     )
   return menu

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     sorted(lowerCAmelCase_,     key=lowerCAmelCase_,     reverse=lowerCAmelCase_     )
   __lowerCAmelCase							     =     []
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0.0, 0.0
   for i in range(len(lowerCAmelCase_     )     ):
      if (total_cost + items_copy[i].get_weight()) <= max_cost:
         result.append(items_copy[i]     )
         total_cost += items_copy[i].get_weight()
         total_value += items_copy[i].get_value()
   return (result, total_value)

def   a_   (			):
   pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

