def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      global f  # a global dp table for knapsack
      if f[i][j] < 0:
            if j < wt[i - 1]:
                  SCREAMING_SNAKE_CASE_          =  mf_knapsack(i - 1     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_				)
            else:
                  SCREAMING_SNAKE_CASE_          =  max(
                      mf_knapsack(i - 1     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_				)     ,      mf_knapsack(i - 1     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      j - wt[i - 1]				) + val[i - 1]     ,      )
            SCREAMING_SNAKE_CASE_          =  val
      return f[i][j]


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  [[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_:
                        SCREAMING_SNAKE_CASE_          =  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]     ,      dp[i - 1][w_]				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  dp[i - 1][w_]

      return dp[n][w_], dp


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):


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

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

      SCREAMING_SNAKE_CASE_          =  knapsack(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_				)
      SCREAMING_SNAKE_CASE_          =  set()
      _construct_solution(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_				)

      return optimal_val, example_optional_set


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):


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


if __name__ == "__main__":
      A     :  Tuple					     =       [3, 2, 4, 4]
      A     :  Union[str, Any]					     =       [4, 3, 2, 3]
      A     :  int					     =       4
      A     :  Optional[Any]					     =       6
      A     :  int					     =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
      A     ,       A     :  int					     =       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     :  Tuple					     =       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 tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        assert np.abs(image - new_image   ).sum() < 1e-5, "Models don't have the same forward pass"







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=50  ,     output_type="numpy"   ).images

        SCREAMING_SNAKE_CASE_          =  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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


