



"""simple docstring"""




def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				List[str]			,							_A			:				List[str]			,							_A			:				Optional[int]    )->       Union[str, Any]:





						"""simple docstring"""





						global f  # a global dp table for knapsack
						if f[i][j] < 0:
												if j < wt[i - 1]:
																		A__							=       mf_knapsack(i - 1			,							_A			,							_A			,							_A    )
												else:
																		A__							=       max(
																		    mf_knapsack(i - 1			,							_A			,							_A			,							_A    )			,							mf_knapsack(i - 1			,							_A			,							_A			,							j - wt[i - 1]    ) + val[i - 1]			,							)
												A__							=       val
						return f[i][j]
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				List[str]			,							_A			:				List[str]			,							_A			:				List[str]    )->       List[Any]:





						"""simple docstring"""





						A__							=       [[0] * (w + 1) for _ in range(n + 1    )]

						for i in range(1			,							n + 1    ):
												for w_ in range(1			,							w + 1    ):
																		if wt[i - 1] <= w_:
																								A__							=       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,							dp[i - 1][w_]    )
																		else:
																								A__							=       dp[i - 1][w_]

						return dp[n][w_], dp
def  UpperCamelCase						(       _A			:				int			,							_A			:				list			,							_A			:				list    )->       Dict:





						"""simple docstring"""





						if not (isinstance(_A			,							(list, tuple)    ) and isinstance(_A			,							(list, tuple)    )):
												raise ValueError(
												    "Both the weights and values vectors must be either lists or tuples"    )

						A__							=       len(_A    )
						if num_items != len(_A    ):
												A__							=       (
												    "The number of weights must be the same as the number of values.\n"
												    f"""But got {num_items} weights and {len(_A    )} values"""
												)
												raise ValueError(_A    )
						for i in range(_A    ):
												if not isinstance(wt[i]			,							_A    ):
																		A__							=       (
																		    "All weights must be integers but got weight of "
																		    f"""type {type(wt[i]    )} at index {i}"""
																		)
																		raise TypeError(_A    )

						A__						,       A__							=       knapsack(_A			,							_A			,							_A			,							_A    )
						A__							=       set()
						_construct_solution(_A			,							_A			,							_A			,							_A			,							_A    )

						return optimal_val, example_optional_set
def  UpperCamelCase						(       _A			:				list			,							_A			:				list			,							_A			:				int			,							_A			:				int			,							_A			:				set    )->       List[str]:





						"""simple docstring"""





						if i > 0 and j > 0:
												if dp[i - 1][j] == dp[i][j]:
																		_construct_solution(_A			,							_A			,							i - 1			,							_A			,							_A    )
												else:
																		optimal_set.add(_A    )
																		_construct_solution(_A			,							_A			,							i - 1			,							j - wt[i - 1]			,							_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      List[Any]      =  [3, 2, 4, 4]
						UpperCAmelCase_							:      str      =  [4, 3, 2, 3]
						UpperCAmelCase_							:      Union[str, Any]      =  4
						UpperCAmelCase_							:      str      =  6
						UpperCAmelCase_							:      Dict      =  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
						UpperCAmelCase_							:      Dict      =  knapsack(w, wt, val, n)
						print(optimal_solution)
						print(mf_knapsack(n, wt, val, w))  # switched the n and w

						# testing the dynamic programming problem with example
						# the optimal subset for the above example are items 3 and 4
						UpperCAmelCase_							:      Dict      =  knapsack_with_example_solution(w, wt, val)
						assert optimal_solution == 8
						assert optimal_subset == {3, 4}
						print("optimal_value = ", optimal_solution)
						print("An optimal subset corresponding to the optimal value", optimal_subset)










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


UpperCAmelCase_							:      Dict      =  False






@skip_mps
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	StableDiffusionAttendAndExcitePipeline
	lowerCAmelCase	:   Optional[Any]				=	False
	lowerCAmelCase	:   Any				=	TEXT_TO_IMAGE_PARAMS
	lowerCAmelCase	:   Dict				=	TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}     )
	lowerCAmelCase	:   List[str]				=	TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase	:   List[Any]				=	TEXT_TO_IMAGE_IMAGE_PARAMS



	@classmethod
	def 			__A							(       cls		):
							super().setUpClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	@classmethod
	def 			__A							(       cls		):
							super().tearDownClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	def 			__A							(       self		):
							torch.manual_seed(0		)
							A__							=       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=UpperCAmelCase__			,	)
							A__							=       DDIMScheduler(
							    beta_start=0.00_085			,	beta_end=0.012			,	beta_schedule="scaled_linear"			,	clip_sample=UpperCAmelCase__			,	set_alpha_to_one=UpperCAmelCase__			,	)
							torch.manual_seed(0		)
							A__							=       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		)
							A__							=       CLIPTextConfig(
							    bos_token_id=0			,	eos_token_id=2			,	hidden_size=32			,	intermediate_size=37			,	layer_norm_eps=1e-0_5			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1_000			,	hidden_act="gelu"			,	projection_dim=512			,	)
							A__							=       CLIPTextModel(UpperCAmelCase__		)
							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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       A__							=       {
							    "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		):
							A__							=       "cpu"

							A__							=       self.get_dummy_components()
							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       pipe(**UpperCAmelCase__		).images
							A__							=       image[0, -3:, -3:, -1]

							self.assertEqual(image.shape			,	(1, 64, 64, 3)		)
							A__							=       np.array(
							    [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
							A__							=       np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(UpperCAmelCase__			,	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      UpperCamelCase    (						unittest.TestCase     ):



	@classmethod
	def 			__A							(       cls		):
							super().setUpClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	@classmethod
	def 			__A							(       cls		):
							super().tearDownClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	def 			__A							(       self		):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	def 			__A							(       self		):
							A__							=       torch.manual_seed(51		)

							A__							=       StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4"			,	safety_checker=UpperCAmelCase__			,	torch_dtype=torch.floataa		)
							pipe.to("cuda"		)

							A__							=       "a painting of an elephant with glasses"
							A__							=       [5, 7]

							A__							=       pipe(
							    prompt=UpperCAmelCase__			,	token_indices=UpperCAmelCase__			,	guidance_scale=7.5			,	generator=UpperCAmelCase__			,	num_inference_steps=5			,	max_iter_to_alter=5			,	output_type="numpy"			,	).images[0]

							A__							=       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





