




"""simple docstring"""






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


lowercase__     						=						False






@skip_mps
class lowerCAmelCase__	(     lowercase,	lowercase,	lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			StableDiffusionAttendAndExcitePipeline
			lowerCamelCase__      =			False
			lowerCamelCase__      =			TEXT_TO_IMAGE_PARAMS
			lowerCamelCase__      =			TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}						)
			lowerCamelCase__      =			TEXT_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase__      =			TEXT_TO_IMAGE_IMAGE_PARAMS
			@classmethod
			def 					A_    (  cls  ):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowercase  )
			@classmethod
			def 					A_    (  cls  ):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowercase  )
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			Optional[int]    =    UNetaDConditionModel(
							    block_out_channels=(32, 64)  ,      layers_per_block=1  ,      sample_size=32  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,      cross_attention_dim=32  ,      attention_head_dim=(2, 4)  ,      use_linear_projection=lowercase  ,      )
							_lowerCamelCase     :			Tuple    =    DDIMScheduler(
							    beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      beta_schedule='scaled_linear'  ,      clip_sample=lowercase  ,      set_alpha_to_one=lowercase  ,      )
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    AutoencoderKL(
							    block_out_channels=[32, 64]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=4  ,      sample_size=128  ,      )
							torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    CLIPTextConfig(
							    bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=32  ,      intermediate_size=37  ,      layer_norm_eps=1E-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1000  ,      hidden_act='gelu'  ,      projection_dim=512  ,      )
							_lowerCamelCase     :			Dict    =    CLIPTextModel(lowercase  )
							_lowerCamelCase     :			Tuple    =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

							_lowerCamelCase     :			Any    =    {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Any    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			str    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    'cpu'

							_lowerCamelCase     :			Optional[Any]    =    self.get_dummy_components()
							_lowerCamelCase     :			List[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			Tuple    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Optional[Any]    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 64, 64, 3)  )
							_lowerCamelCase     :			List[Any]    =    np.array(
							    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4  )
			def 					A_    (  self  ):
							# 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  ):
							self._test_inference_batch_single_identical(batch_size=2  ,      expected_max_diff=7E-4  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=5E-4  )






			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=4E-4  )






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






			'''simple docstring'''



			@classmethod
			def 					A_    (  cls  ):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowercase  )
			@classmethod
			def 					A_    (  cls  ):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowercase  )
			def 					A_    (  self  ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    torch.manual_seed(51  )

							_lowerCamelCase     :			int    =    StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'  ,      safety_checker=lowercase  ,      torch_dtype=torch.floataa  )
							pipe.to('cuda'  )

							_lowerCamelCase     :			Tuple    =    'a painting of an elephant with glasses'
							_lowerCamelCase     :			Optional[Any]    =    [5, 7]

							_lowerCamelCase     :			Tuple    =    pipe(
							    prompt=lowercase  ,      token_indices=lowercase  ,      guidance_scale=7.5  ,      generator=lowercase  ,      num_inference_steps=5  ,      max_iter_to_alter=5  ,      output_type='numpy'  ,      ).images[0]

							_lowerCamelCase     :			Tuple    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'  )
							assert np.abs((expected_image - image).max()  ) < 5E-1





"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


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