



"""simple docstring"""
import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCAmelCase       :Optional[Any]	=   'sshleifer/bart-tiny-random'
_lowerCAmelCase       :str	=   'patrickvonplaten/t5-tiny-random'
@require_torch
class 				_UpperCAmelCase				(					unittest.TestCase ):





     '''simple docstring'''



     @cached_property
     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         return AutoConfig.from_pretrained(__UpperCAmelCase						)





     def 					__lowerCAmelCase      (   self						)				->   List[Any]:
         _UpperCAmelCase				,							*_UpperCAmelCase				:    str											=       create_student_by_copying_alternating_layers(__UpperCAmelCase			,      tempfile.mkdtemp()			,      e=1			,      d=1						)
         self.assertEqual(student.config.num_hidden_layers			,      1						)





     def 					__lowerCAmelCase      (   self						)				->   str:
         _UpperCAmelCase				,							*_UpperCAmelCase				:    Optional[int]											=       create_student_by_copying_alternating_layers(__UpperCAmelCase			,      tempfile.mkdtemp()			,      e=1			,      d=__UpperCAmelCase						)





     def 					__lowerCAmelCase      (   self						)				->   int:
         _UpperCAmelCase				,							*_UpperCAmelCase				:    Any											=       create_student_by_copying_alternating_layers(__UpperCAmelCase			,      tempfile.mkdtemp()			,      e=1			,      d=__UpperCAmelCase						)
         self.assertEqual(student.config.encoder_layers			,      1						)
         self.assertEqual(student.config.decoder_layers			,      self.teacher_config.encoder_layers						)





     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         _UpperCAmelCase				,							*_UpperCAmelCase				:    Tuple											=       create_student_by_copying_alternating_layers(__UpperCAmelCase			,      tempfile.mkdtemp()			,      e=1			,      d=1						)
         self.assertEqual(student.config.encoder_layers			,      1						)
         self.assertEqual(student.config.decoder_layers			,      1						)





     def 					__lowerCAmelCase      (   self						)				->   Optional[int]:
         with self.assertRaises(__UpperCAmelCase						):
             create_student_by_copying_alternating_layers(__UpperCAmelCase			,      tempfile.mkdtemp()			,      e=__UpperCAmelCase			,      d=__UpperCAmelCase						)







from __future__ import annotations

from math import ceil, floor, sqrt





def 		a__      (		_UpperCamelCase      :			int = 2_00_00_00				):
   __lowerCamelCase    				=     [0]
   __lowerCamelCase    				=     42

   for idx in range(1			,ceil(sqrt(target * 2				) * 1.1				)				):
      triangle_numbers.append(triangle_numbers[-1] + idx				)

   # we want this to be as close as possible to target
   __lowerCamelCase    				=     0
   # the area corresponding to the grid that gives the product closest to target
   __lowerCamelCase    				=     0
   # an estimate of b, using the quadratic formula
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_floor
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_ceil
   __lowerCamelCase    				=     42

   for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,1				):
      __lowerCamelCase    				=     (-1 + sqrt(1 + 8 * target / triangle_a				)) / 2
      __lowerCamelCase    				=     floor(_UpperCamelCase				)
      __lowerCamelCase    				=     ceil(_UpperCamelCase				)
      __lowerCamelCase    				=     triangle_numbers[b_floor]
      __lowerCamelCase    				=     triangle_numbers[b_ceil]

      if abs(target - triangle_b_first_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_first_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_floor

      if abs(target - triangle_b_second_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_second_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_ceil

   return area


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






