
'''simple docstring'''





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







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Any				       =							[[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_:
        UpperCAmelCase__      :					List[str]				       =							max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,       dp[i - 1][w_]		)
      else:
        UpperCAmelCase__      :					Union[str, Any]				       =							dp[i - 1][w_]

  return dp[n][w_], dp







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  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"		)

  UpperCAmelCase__      :					Union[str, Any]				       =							len(lowercase__		)
  if num_items != len(lowercase__		):
    UpperCAmelCase__      :					Any				       =							(
        "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__		):
      UpperCAmelCase__      :					Optional[int]				       =							(
          "All weights must be integers but got weight of "
          F"""type {type(wt[i]		)} at index {i}"""
      )
      raise TypeError(lowercase__		)

  UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							knapsack(lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		)
  UpperCAmelCase__      :					set				       =							set()
  _construct_solution(lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		)

  return optimal_val, example_optional_set







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		):
  # for the current item i at a maximum weight j to be part of an optimal subset,
  # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
  # where i - 1 means considering only the previous items at the given maximum weight
  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__":
      SCREAMING_SNAKE_CASE     =       [3, 2, 4, 4]
      SCREAMING_SNAKE_CASE     =       [4, 3, 2, 3]
      SCREAMING_SNAKE_CASE     =       4
      SCREAMING_SNAKE_CASE     =       6
      SCREAMING_SNAKE_CASE     =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
      SCREAMING_SNAKE_CASE,						SCREAMING_SNAKE_CASE     =       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
      SCREAMING_SNAKE_CASE,						SCREAMING_SNAKE_CASE     =       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 inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							PriorTransformer
 lowercase_					:    Dict				=							"hidden_states"







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							4
   UpperCAmelCase__      :					List[Any]				       =							8
   UpperCAmelCase__      :					Union[str, Any]				       =							7

   UpperCAmelCase__      :					Tuple				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					str				       =							4
   UpperCAmelCase__      :					int				       =							8
   UpperCAmelCase__      :					Optional[int]				       =							7

   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   return (4, 8)







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   return (4, 8)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   UpperCAmelCase__      :					Optional[Any]				       =							self.dummy_input
   return init_dict, inputs_dict







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"			,				output_loading_info=snake_case__ )
   self.assertIsNotNone(snake_case__ )
   self.assertEqual(len(loading_info["missing_keys"] )			,				0 )

   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							model(**self.dummy_input )[0]

   assert hidden_states is not None, "Make sure output is not None"







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__      :					Tuple				       =							self.model_class(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							inspect.signature(model.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__      :					int				       =							[*signature.parameters.keys()]

   UpperCAmelCase__      :					Optional[Any]				       =							["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
   UpperCAmelCase__      :					List[Any]				       =							model.to(snake_case__ )

   if hasattr(snake_case__			,				"set_default_attn_processor" ):
     model.set_default_attn_processor()

   UpperCAmelCase__      :					int				       =							self.get_dummy_seed_input()

   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )[0]

   UpperCAmelCase__      :					Any				       =							output[0, :5].flatten().cpu()
   print(snake_case__ )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   UpperCAmelCase__      :					Optional[int]				       =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
   self.assertTrue(torch_all_close(snake_case__			,				snake_case__			,				rtol=1e-2 ) )






@slow
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Any=7_68			,				snake_case__		:  List[Any]=77			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							batch_size
   UpperCAmelCase__      :					Any				       =							embedding_dim
   UpperCAmelCase__      :					Any				       =							num_embeddings

   UpperCAmelCase__      :					str				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ] )
 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior" )
   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							self.get_dummy_seed_input(seed=snake_case__ )

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**snake_case__ )[0]

   assert list(sample.shape ) == [1, 7_68]

   UpperCAmelCase__      :					List[str]				       =							sample[0, :8].flatten().cpu()
   print(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							torch.tensor(snake_case__ )

   assert torch_all_close(snake_case__			,				snake_case__			,				atol=1e-3 )



