
'''simple docstring'''





from itertools import permutations



def        lowerCamelCase_  (   __UpperCamelCase = 5     ):
				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
				A_       	=  list(range(gon_side + 1 ,						0 ,						-1     )     )
				A_       	=  list(range(gon_side + 2 ,						gon_side * 2 + 1     )     )

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

				A_       	=  F"Magic {gon_side}-gon ring is impossible"
				raise ValueError(__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  [0] * (gon_side * 3)
				A_       	=  perm[0:3]
				perm.append(perm[1]     )

				A_       	=  1 if gon_side < 5 else 2

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

				return result



def        lowerCamelCase_  (   __UpperCamelCase     ):
				if len(__UpperCamelCase     ) % 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

				A_       	=  sum(numbers[:3]     )

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


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

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 					__lowercase		(					A							,       A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	StableDiffusionModelEditingPipeline
					__magic_name__						:							List[str]     	=	TEXT_TO_IMAGE_PARAMS
					__magic_name__						:							Any     	=	TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__						:							Optional[Any]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__						:							Tuple     	=	TEXT_TO_IMAGE_IMAGE_PARAMS


					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									torch.manual_seed(0						)
									A_       	=  UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,       cross_attention_dim=3_2       ,       )
									A_       	=  DDIMScheduler()
									torch.manual_seed(0						)
									A_       	=  AutoencoderKL(
									    block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,       latent_channels=4       ,       )
									torch.manual_seed(0						)
									A_       	=  CLIPTextConfig(
									    bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1E-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_0_0_0       ,       )
									A_       	=  CLIPTextModel(a__						)
									A_       	=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

									A_       	=  {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=0						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    '''height''': None,
									    '''width''': None,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  '''french fries'''
									A_       	=  sd_pipe(**a__       ,       negative_prompt=a__						)
									A_       	=  output.images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''						)
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  PNDMScheduler()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(a__						):
													A_       	=  sd_pipe(**a__						).images


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=5E-3						)


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)






@slow
@require_torch_gpu
class 					__lowercase		(					unittest.TestCase       ):


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





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def        lowerCAmelCase_    (	self       ,       a__=0						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(a__       ,       safety_checker=a__						)
									pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									A_       	=  np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

									assert np.abs(expected_slice - image_slice						).max() < 1E-2

									# make sure image changes after editing
									pipe.edit_model('''A pack of roses'''       ,       '''A pack of blue roses'''						)

									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice						).max() > 1E-1


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  DDIMScheduler.from_pretrained(a__       ,       subfolder='''scheduler'''						)
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(
									    a__       ,       scheduler=a__       ,       safety_checker=a__						)
									A_       	=  pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						)

									A_       	=  torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9