





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__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    Any						,lowerCamelCase__						:    Any=1024						,lowerCamelCase__						:    Optional[Any]=1024						,lowerCamelCase__						:    int=3.6       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						tokenizer
           SCREAMING_SNAKE_CASE		=						tokenizer.bos_token_id
           SCREAMING_SNAKE_CASE		=						dataset
           SCREAMING_SNAKE_CASE		=						seq_length
           SCREAMING_SNAKE_CASE		=						seq_length * chars_per_token * num_of_sequences






     def __iter__(							self						:    List[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


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



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						{"""streaming""": True}
      SCREAMING_SNAKE_CASE		=						load_dataset(args.dataset_name ,						split="""train""" ,						**_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						ConstantLengthDataset(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						seq_length=args.seq_length	)
      SCREAMING_SNAKE_CASE		=						DataLoader(_SCREAMING_SNAKE_CASE ,						batch_size=args.batch_size	)
      return eval_dataloader



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      model.eval()
      SCREAMING_SNAKE_CASE		=						[]
      for step, batch in enumerate(_SCREAMING_SNAKE_CASE	):
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE ,						labels=_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						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		=						torch.mean(torch.cat(_SCREAMING_SNAKE_CASE	)	)
      try:
            SCREAMING_SNAKE_CASE		=						torch.exp(_SCREAMING_SNAKE_CASE	)
      except OverflowError:
            SCREAMING_SNAKE_CASE		=						float("""inf"""	)
      return loss.item(), perplexity.item()


# Setup Accelerator
SCREAMING_SNAKE_CASE_						   =       Accelerator()

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

# Logging
SCREAMING_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
SCREAMING_SNAKE_CASE_						   =       AutoModelForCausalLM.from_pretrained(args.model_ckpt)
SCREAMING_SNAKE_CASE_						   =       AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE_						   =       create_dataloader(args)

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

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



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









import math
import unittest



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      assert isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) and (
          number >= 0
      ), "'number' must been an int and positive"

      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(math.sqrt(_SCREAMING_SNAKE_CASE	) + 1	) ,						6	):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Tuple:




           '''simple docstring'''


           self.assertTrue(is_prime(2       )       )
           self.assertTrue(is_prime(3       )       )
           self.assertTrue(is_prime(5       )       )
           self.assertTrue(is_prime(7       )       )
           self.assertTrue(is_prime(11       )       )
           self.assertTrue(is_prime(13       )       )
           self.assertTrue(is_prime(17       )       )
           self.assertTrue(is_prime(19       )       )
           self.assertTrue(is_prime(23       )       )
           self.assertTrue(is_prime(29       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           with self.assertRaises(lowerCamelCase__       ):
                 is_prime(-19       )
           self.assertFalse(
               is_prime(0       )						,"""Zero doesn't have any positive factors, primes must have exactly two."""						,)
           self.assertFalse(
               is_prime(1       )						,"""One only has 1 positive factor, primes must have exactly two."""						,)
           self.assertFalse(is_prime(2 * 2       )       )
           self.assertFalse(is_prime(2 * 3       )       )
           self.assertFalse(is_prime(3 * 3       )       )
           self.assertFalse(is_prime(3 * 5       )       )
           self.assertFalse(is_prime(3 * 5 * 7       )       )


if __name__ == "__main__":
      unittest.main()


