
'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

 return dp[n][w_], dp


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 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"			)

 lowerCAmelCase__     :   List[str]     		=	len(lowerCamelCase_			)
 if num_items != len(lowerCamelCase_			):
  lowerCAmelCase__     :   Optional[int]     		=	(
      "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_			):
   lowerCAmelCase__     :   Tuple     		=	(
       "All weights must be integers but got weight of "
       f'''type {type(wt[i]			)} at index {i}'''
   )
   raise TypeError(lowerCamelCase_			)

 lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	knapsack(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
 lowerCAmelCase__     :   set     		=	set()
 _construct_solution(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)

 return optimal_val, example_optional_set


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 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__":
     snake_case									=    [3, 2, 4, 4]
     snake_case									=    [4, 3, 2, 3]
     snake_case									=    4
     snake_case									=    6
     snake_case									=    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     snake_case					,					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
     snake_case					,					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'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

  if depth_multiplier <= 0:
   raise ValueError("depth_multiplier must be greater than zero."    )

  lowerCAmelCase__     :   Any     		=	num_channels
  lowerCAmelCase__     :   Union[str, Any]     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	depth_multiplier
  lowerCAmelCase__     :   Optional[int]     		=	depth_divisible_by
  lowerCAmelCase__     :   Union[str, Any]     		=	min_depth
  lowerCAmelCase__     :   List[str]     		=	expand_ratio
  lowerCAmelCase__     :   Union[str, Any]     		=	output_stride
  lowerCAmelCase__     :   Optional[int]     		=	first_layer_is_expansion
  lowerCAmelCase__     :   Tuple     		=	finegrained_output
  lowerCAmelCase__     :   Optional[int]     		=	hidden_act
  lowerCAmelCase__     :   Optional[Any]     		=	tf_padding
  lowerCAmelCase__     :   Union[str, Any]     		=	classifier_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   str     		=	layer_norm_eps
  lowerCAmelCase__     :   int     		=	semantic_loss_ignore_index




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return OrderedDict([("pixel_values", {0: "batch"})]    )


 @property
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  if self.task == "image-classification":
   return OrderedDict([("logits", {0: "batch"})]    )
  else:
   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]    )


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





