


from itertools import permutations






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE = 5   )->    int:




      """simple docstring"""

      if gon_side < 3 or gon_side > 5:
            raise ValueError('''gon_side must be in the range [3, 5]'''   )

      # Since it's 16, we know 10 is on the outer ring
      # Put the big numbers at the end so that they are never the first number
      snake_case_			= list(range(gon_side + 1   ,   0   ,   -1   )   )
      snake_case_			= list(range(gon_side + 2   ,   gon_side * 2 + 1   )   )

      for perm in permutations(small_numbers + big_numbers   ):
            snake_case_			= generate_gon_ring(SCREAMING_SNAKE_CASE   ,   list(SCREAMING_SNAKE_CASE   )   )
            if is_magic_gon(SCREAMING_SNAKE_CASE   ):
                  return int(''''''.join(str(SCREAMING_SNAKE_CASE   ) for n in numbers   )   )

      snake_case_			= f'''Magic {gon_side}-gon ring is impossible'''
      raise ValueError(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    list[int]:




      """simple docstring"""

      snake_case_			= [0] * (gon_side * 3)
      snake_case_			= perm[0:3]
      perm.append(perm[1]   )

      snake_case_			= 1 if gon_side < 5 else 2

      for i in range(1   ,   len(SCREAMING_SNAKE_CASE   ) // 3 + magic_number   ):
            snake_case_			= perm[2 * i + 1]
            snake_case_			= result[3 * i - 1]
            snake_case_			= perm[2 * i + 2]

      return result






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    bool:




      """simple docstring"""

      if len(SCREAMING_SNAKE_CASE   ) % 3 != 0:
            raise ValueError('''a gon ring should have a length that is a multiple of 3'''   )

      if min(numbers[::3]   ) != numbers[0]:
            return False

      snake_case_			= sum(numbers[:3]   )

      return all(sum(numbers[i : i + 3]   ) == total for i in range(3   ,   len(SCREAMING_SNAKE_CASE   )   ,   3   )   )


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



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )