


"""simple docstring"""
def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      Dict						, __snake_case							:      int						, __snake_case							:      int   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      Dict						, __snake_case							:      Tuple						, __snake_case							:      str						, __snake_case							:      List[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		[[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_:
            _lowerCamelCase	:					List[str]   =		max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]						, dp[i - 1][w_]   )
         else:
            _lowerCamelCase	:					Union[str, Any]   =		dp[i - 1][w_]

   return dp[n][w_], dp

def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      list						, __snake_case							:      list   ):




   """simple docstring"""







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

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

   _lowerCamelCase   ,    _lowerCamelCase	:					int   =		knapsack(__snake_case						, __snake_case						, __snake_case						, __snake_case   )
   _lowerCamelCase	:					set   =		set()
   _construct_solution(__snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case   )

   return optimal_val, example_optional_set

def 				_snake_case      (   __snake_case							:      list						, __snake_case							:      list						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      set   ):




   """simple docstring"""







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


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




'''simple docstring'''



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



@slow
@require_flax
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([canny_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'Chef in the kitchen'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([pose_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





