





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_					(			_lowerCamelCase   ):




   def __init__(self	:str    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]=1024    ,   _UpperCamelCase	:Any=1024    ,   _UpperCamelCase	:Dict=3.6 )->					Tuple:
         __A          =				tokenizer
         __A          =				tokenizer.bos_token_id
         __A          =				dataset
         __A          =				seq_length
         __A          =				seq_length * chars_per_token * num_of_sequences






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





def _a				(	lowerCamelCase:   List[Any] )	->						Dict:







      '''simple docstring'''






      __A          =				{'''streaming''': True}
      __A          =				load_dataset(args.dataset_name					,				split='''train'''					,				**lowerCamelCase )
      __A          =				ConstantLengthDataset(lowerCamelCase					,				lowerCamelCase					,				seq_length=args.seq_length )
      __A          =				DataLoader(lowerCamelCase					,				batch_size=args.batch_size )
      return eval_dataloader



def _a				(	lowerCamelCase:   str )	->						List[str]:







      '''simple docstring'''






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

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


# Setup Accelerator
snake_case__					:							Tuple													=     Accelerator()

# Parse configuration
snake_case__					:							int													=     HfArgumentParser(EvaluationArguments)
snake_case__					:							Union[str, Any]													=     parser.parse_args()
set_seed(args.seed)

# Logging
snake_case__					:							str													=     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__					:							Optional[Any]													=     AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
snake_case__					:							Any													=     create_dataloader(args)

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

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







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












from __future__ import annotations



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






      if index == len(lowerCamelCase ):
            print(lowerCamelCase )
            return

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





