






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
     import torch






@require_torch
@require_optimum
@slow
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       a		  =      '''hf-internal-testing/tiny-random-t5'''
       a		  =      AutoTokenizer.from_pretrained(__UpperCAmelCase  )
       a		  =      AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase  )

       a		  =      tokenizer('''This is me''' ,					return_tensors='''pt'''  )

       a		  =      model.to_bettertransformer()

       self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

       a		  =      model.generate(**__UpperCAmelCase  )

       a		  =      model.reverse_bettertransformer()

       self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(__UpperCAmelCase  )

        a		  =      AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase  )

        self.assertFalse(
            any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()  )  )

        a		  =      model_reloaded.generate(**__UpperCAmelCase  )
        self.assertTrue(torch.allclose(__UpperCAmelCase ,					__UpperCAmelCase  )  )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


       a		  =      '''hf-internal-testing/tiny-random-t5'''
       a		  =      AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase  )

       a		  =      model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
        with self.assertRaises(__UpperCAmelCase  ):
         model.save_pretrained(__UpperCAmelCase  )

        a		  =      model.reverse_bettertransformer()
        model.save_pretrained(__UpperCAmelCase  )










import math
def 						_a    (      a :int )							->			list:
 a		  =      [True] * n
 a		  =      False
 a		  =      False
 a		  =      True

 for i in range(3	,							int(n**0.5 + 1 )	,							2 ):
  a		  =      i * 2
  while index < n:
   a		  =      False
   a		  =      index + i

 a		  =      [2]

 for i in range(3	,							a	,							2 ):
  if is_prime[i]:
   primes.append(a )

 return primes
def 						_a    (      a :int = 999_966_663_333 )							->			int:
 a		  =      math.floor(math.sqrt(a ) ) + 100
 a		  =      prime_sieve(a )

 a		  =      0
 a		  =      0
 a		  =      primes[prime_index]

 while (last_prime**2) <= limit:
  a		  =      primes[prime_index + 1]

  a		  =      last_prime**2
  a		  =      next_prime**2

  # Get numbers divisible by lps(current)
  a		  =      lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  a		  =      upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  a		  =      0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  a		  =      next_prime
  prime_index += 1

 return matches_sum


if __name__ == "__main__":
     print(solution())


