"""simple docstring"""




import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class     UpperCamelCase__(			_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase ):
						lowerCAmelCase__  :						int													=		StableUnCLIPPipeline
						lowerCAmelCase__  :						Union[str, Any]													=		TEXT_TO_IMAGE_PARAMS
						lowerCAmelCase__  :						Any													=		TEXT_TO_IMAGE_BATCH_PARAMS
						lowerCAmelCase__  :						Optional[Any]													=		TEXT_TO_IMAGE_IMAGE_PARAMS
						lowerCAmelCase__  :						Any													=		TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						lowerCAmelCase__  :						Optional[Any]													=		False





						def 				snake_case__	(       self     )					->				int:
													A__	     =							32
													A__	     =							embedder_hidden_size

													# prior components

													torch.manual_seed(0     )
													A__	     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

													torch.manual_seed(0     )
													A__	     =							CLIPTextModelWithProjection(
													    CLIPTextConfig(
													        bos_token_id=0		,eos_token_id=2		,hidden_size=lowercase__		,projection_dim=lowercase__		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)     )

													torch.manual_seed(0     )
													A__	     =							PriorTransformer(
													    num_attention_heads=2		,attention_head_dim=12		,embedding_dim=lowercase__		,num_layers=1		,)

													torch.manual_seed(0     )
													A__	     =							DDPMScheduler(
													    variance_type='fixed_small_log'		,prediction_type='sample'		,num_train_timesteps=10_00		,clip_sample=lowercase__		,clip_sample_range=5.0		,beta_schedule='squaredcos_cap_v2'		,)

													# regular denoising components

													torch.manual_seed(0     )
													A__	     =							StableUnCLIPImageNormalizer(embedding_dim=lowercase__     )
													A__	     =							DDPMScheduler(beta_schedule='squaredcos_cap_v2'     )

													torch.manual_seed(0     )
													A__	     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

													torch.manual_seed(0     )
													A__	     =							CLIPTextModel(
													    CLIPTextConfig(
													        bos_token_id=0		,eos_token_id=2		,hidden_size=lowercase__		,projection_dim=32		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)     )

													torch.manual_seed(0     )
													A__	     =							UNetaDConditionModel(
													    sample_size=32		,in_channels=4		,out_channels=4		,down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')		,up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')		,block_out_channels=(32, 64)		,attention_head_dim=(2, 4)		,class_embed_type='projection'		,projection_class_embeddings_input_dim=embedder_projection_dim * 2		,cross_attention_dim=lowercase__		,layers_per_block=1		,upcast_attention=lowercase__		,use_linear_projection=lowercase__		,)

													torch.manual_seed(0     )
													A__	     =							DDIMScheduler(
													    beta_schedule='scaled_linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,prediction_type='v_prediction'		,set_alpha_to_one=lowercase__		,steps_offset=1		,)

													torch.manual_seed(0     )
													A__	     =							AutoencoderKL()

													A__	     =							{
													    # prior components
													    """prior_tokenizer""": prior_tokenizer,
													    """prior_text_encoder""": prior_text_encoder,
													    """prior""": prior,
													    """prior_scheduler""": prior_scheduler,
													    # image noising components
													    """image_normalizer""": image_normalizer,
													    """image_noising_scheduler""": image_noising_scheduler,
													    # regular denoising components
													    """tokenizer""": tokenizer,
													    """text_encoder""": text_encoder,
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													}

													return components





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=0     )					->				List[Any]:
													if str(lowercase__     ).startswith('mps'     ):
																				A__	     =							torch.manual_seed(lowercase__     )
													else:
																				A__	     =							torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
													A__	     =							{
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """prior_num_inference_steps""": 2,
													    """output_type""": """numpy""",
													}
													return inputs





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							torch_device == """cpu"""

													self._test_attention_slicing_forward_pass(test_max_difference=lowercase__     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							torch_device in ["""cpu""", """mps"""]

													self._test_inference_batch_single_identical(test_max_difference=lowercase__     )






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





						def 				snake_case__	(       self     )					->				int:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'     )

													A__	     =							StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'		,torch_dtype=torch.floataa     )
													pipe.to(lowercase__     )
													pipe.set_progress_bar_config(disable=lowercase__     )
													# stable unclip will oom when integration tests are run on a V100,
													# so turn on memory savings
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													A__	     =							torch.Generator(device='cpu'     ).manual_seed(0     )
													A__	     =							pipe('anime turle'		,generator=lowercase__		,output_type='np'     )

													A__	     =							output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowercase__		,lowercase__     )





						def 				snake_case__	(       self     )					->				List[str]:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													A__	     =							StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'		,torch_dtype=torch.floataa     )
													A__	     =							pipe.to(lowercase__     )
													pipe.set_progress_bar_config(disable=lowercase__     )
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													A__	     =							pipe(
													    'anime turtle'		,prior_num_inference_steps=2		,num_inference_steps=2		,output_type='np'		,)

													A__	     =							torch.cuda.max_memory_allocated()
													# make sure that less than 7 GB is allocated
													assert mem_bytes < 7 * 10**9






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if len(UpperCamelCase__				) <= 1:
														return [tuple(UpperCamelCase__				)]

							A__	     =							[]

							def generate(UpperCamelCase__       ,    UpperCamelCase__				):
														if k == 1:
																					res.append(tuple(arr[:]				)				)
																					return

														generate(k - 1       ,    UpperCamelCase__				)

														for i in range(k - 1				):
																					if k % 2 == 0:  # k is even
																												A__     ,  A__	     =							arr[k - 1], arr[i]
																					else:  # k is odd
																												A__     ,  A__	     =							arr[k - 1], arr[0]
																					generate(k - 1       ,    UpperCamelCase__				)

							generate(len(UpperCamelCase__				)       ,    UpperCamelCase__				)
							return res


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(heaps(arr))





