
'''simple docstring'''







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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Tuple			=   """hf-internal-testing/tiny-random-t5"""
     _a				:	Any			=   AutoTokenizer.from_pretrained(lowerCamelCase__       )
     _a				:	Optional[int]			=   AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )

     _a				:	List[str]			=   tokenizer('This is me'							,return_tensors='pt'       )

     _a				:	Any			=   model.to_bettertransformer()

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

     _a				:	int			=   model.generate(**lowerCamelCase__       )

     _a				:	str			=   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(lowerCamelCase__       )

        _a				:	List[Any]			=   AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )

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

        _a				:	str			=   model_reloaded.generate(**lowerCamelCase__       )
        self.assertTrue(torch.allclose(lowerCamelCase__							,lowerCamelCase__       )       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   """hf-internal-testing/tiny-random-t5"""
     _a				:	List[Any]			=   AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )

     _a				:	Optional[Any]			=   model.to_bettertransformer()

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

        _a				:	str			=   model.reverse_bettertransformer()
        model.save_pretrained(lowerCamelCase__       )








def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


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


