






import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 						SCREAMING_SNAKE_CASE				( __UpperCAmelCase  ,	__UpperCAmelCase  ,	unittest.TestCase					):





							"""simple docstring"""

							A_      =					IFImgaImgSuperResolutionPipeline
							A_      =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
							A_      =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}					)
							A_      =					PipelineTesterMixin.required_optional_params - {"""latents"""}

							def       __A (  self:   Tuple   )    ->				int:
												return self._get_superresolution_dummy_components()

							def       __A (  self:   List[str]		,     __A:   Tuple		,     __A:   Dict=0   )    ->				Optional[Any]:
												if str(UpperCAmelCase_   ).startswith('''mps'''   ):
																	_A								= torch.manual_seed(UpperCAmelCase_   )
												else:
																	_A								= torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )

												_A								= floats_tensor((1, 3, 32, 32)		,     rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
												_A								= floats_tensor((1, 3, 16, 16)		,     rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )

												_A								= {
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''original_image''': original_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs

							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()		,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,     )
							def       __A (  self:   Tuple   )    ->				List[str]:
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

							def       __A (  self:   Any   )    ->				str:
												self._test_save_load_optional_components()

							@unittest.skipIf(torch_device != '''cuda'''		,     reason='''float16 requires CUDA'''   )
							def       __A (  self:   List[str]   )    ->				Optional[int]:
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1e-1   )

							def       __A (  self:   Any   )    ->				int:
												self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												self._test_save_load_local()



							def       __A (  self:   Tuple   )    ->				str:
												self._test_inference_batch_single_identical(
												    expected_max_diff=1e-2		,     )











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''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					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''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      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

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

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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


if __name__ == "__main__":
				__A      =				[3, 2, 4, 4]
				__A      =				[4, 3, 2, 3]
				__A      =				4
				__A      =				6
				__A      =				[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				__A ,  __A      =				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
				__A ,  __A      =				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)



