




def 			a   (_lowerCAmelCase     ):
							if not isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														raise ValueError('''check_bouncy() accepts only integer arguments'''     )
							SCREAMING_SNAKE_CASE_   	=    str(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    ''''''.join(sorted(_lowerCAmelCase     )     )
							return sorted_str_n != str_n and sorted_str_n[::-1] != str_n

def 			a   (_lowerCAmelCase = 9_9     ):
							if not 0 < percent < 1_0_0:
														raise ValueError('''solution() only accepts values from 0 to 100'''     )
							SCREAMING_SNAKE_CASE_   	=    0
							SCREAMING_SNAKE_CASE_   	=    1

							while True:
														if check_bouncy(_lowerCAmelCase     ):
																					bouncy_num += 1
														if (bouncy_num / num) * 1_0_0 >= percent:
																					return num
														num += 1


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			print(f"""{solution(99)}""")








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 16,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 32,
											    '''embedding_proj_dim''': self.text_embedder_hidden_size,
											    '''time_embed_dim''': self.time_embed_dim,
											    '''num_layers''': 1,
											    '''clip_embed_dim''': self.time_input_dim * 2,
											    '''additional_embeddings''': 0,
											    '''time_embed_act_fn''': '''gelu''',
											    '''norm_in_type''': '''layer''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''param_shapes''': (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    '''d_latent''': self.time_input_dim,
											    '''d_hidden''': self.renderer_dim,
											    '''n_output''': 12,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


				def     _A							(							self:   str		):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

											for key in inputs.keys():
																		if key in self.batch_params:
																									SCREAMING_SNAKE_CASE_   	=    batch_size * [inputs[key]]

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Any		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


