

"""simple docstring"""




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      __lowercase			(    SCREAMING_SNAKE_CASE_					):

  def __init__(  self		:			Optional[Any]					,A		:			List[Any]					,A		:			List[Any]					,A		:			int=1_024					,A		:			List[Any]=1_024					,A		:			str=3.6    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					tokenizer
   UpperCAmelCase__					:  int					   =					tokenizer.bos_token_id
   UpperCAmelCase__					:  List[Any]					   =					dataset
   UpperCAmelCase__					:  Tuple					   =					seq_length
   UpperCAmelCase__					:  Any					   =					seq_length * chars_per_token * num_of_sequences




  def __iter__(  self		:			Dict    ):






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







def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					{'''streaming''': True}
 UpperCAmelCase__					:  List[str]					   =					load_dataset(args.dataset_name							,    split="""train"""							,    **__UpperCamelCase      )
 UpperCAmelCase__					:  List[str]					   =					ConstantLengthDataset(__UpperCamelCase							,    __UpperCamelCase							,    seq_length=args.seq_length      )
 UpperCAmelCase__					:  int					   =					DataLoader(__UpperCamelCase							,    batch_size=args.batch_size      )
 return eval_dataloader
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





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

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


# Setup Accelerator
__UpperCAmelCase =				Accelerator()

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

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

# Load dataset and dataloader
__UpperCAmelCase =				create_dataloader(args)

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

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




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








"""simple docstring"""




def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					[]
 if len(__UpperCamelCase      ) == 1:
  return [nums.copy()]
 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Tuple					   =					nums.pop(0      )
  UpperCAmelCase__					:  List[str]					   =					permute(__UpperCamelCase      )
  for perm in permutations:
   perm.append(__UpperCamelCase      )
  result.extend(__UpperCamelCase      )
  nums.append(__UpperCamelCase      )
 return result
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 def backtrack(__UpperCamelCase      ):
  if start == len(__UpperCamelCase      ) - 1:
   output.append(nums[:]      )
  else:
   for i in range(__UpperCamelCase							,    len(__UpperCamelCase      )      ):
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					nums[i], nums[start]
    backtrack(start + 1      )
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					nums[i], nums[start]  # backtrack

 UpperCAmelCase__					:  List[str]					   =					[]
 backtrack(0      )
 return output


if __name__ == "__main__":
      import doctest

      # use res to print the data in permute2 function
      __UpperCAmelCase =				permutea([1, 2, 3])
      print(res)
      doctest.testmod()





