



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       _SCREAMING_SNAKE_CASE	(_UpperCAmelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							List[str]=1_0_2_4     ,  UpperCamelCase :							Optional[int]=1_0_2_4     ,  UpperCamelCase :							Dict=3.6				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	tokenizer
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer.bos_token_id
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	dataset
      __SCREAMING_SNAKE_CASE       :    int       =	seq_length
      __SCREAMING_SNAKE_CASE       :    Any       =	seq_length * chars_per_token * num_of_sequences






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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict     ):



 """simple docstring"""





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

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


# Setup Accelerator
_lowerCamelCase						    =  Accelerator()

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

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

# Load dataset and dataloader
_lowerCamelCase						    =  create_dataloader(args)

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

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






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











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5		,	int(sqrt(__lowerCamelCase     ) + 1     )		,	6     ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 10001     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Dict       =	1
 while count != nth and number < 3:
  number += 1
  if is_prime(__lowerCamelCase     ):
   count += 1
 while count != nth:
  number += 2
  if is_prime(__lowerCamelCase     ):
   count += 1
 return number


if __name__ == "__main__":
      print(f'''{solution() = }''')






