

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Optional[Any])     ->						Optional[int]:
   '''simple docstring'''


   global f  # a global dp table for knapsack
   if f[i][j] < 0:
      if j < wt[i - 1]:
         __lowercase								=						mf_knapsack(i - 1,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
      else:
         __lowercase								=						max(
             mf_knapsack(i - 1,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_),      mf_knapsack(i - 1,      UpperCamelCase_,      UpperCamelCase_,      j - wt[i - 1]) + val[i - 1],      )
      __lowercase								=						val
   return f[i][j]
def   _A  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	int,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


   __lowercase								=						[[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_:
            __lowercase								=						max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],      dp[i - 1][w_])
         else:
            __lowercase								=						dp[i - 1][w_]

   return dp[n][w_], dp
def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list,      UpperCamelCase_					:	list)     ->						Any:
   '''simple docstring'''


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

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

   __lowercase		,__lowercase								=						knapsack(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						set()
   _construct_solution(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)

   return optimal_val, example_optional_set
def   _A  (  UpperCamelCase_					:	list,      UpperCamelCase_					:	list,      UpperCamelCase_					:	int,      UpperCamelCase_					:	int,      UpperCamelCase_					:	set)     ->						List[Any]:
   '''simple docstring'''


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


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)







"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

_a				,						_a							=							os.path.split(__file__)
_a							=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()




