




'''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


a  :				Tuple									=  logging.get_logger(__name__)

a  :				List[str]									=  {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"data2vec-vision"


				def __init__(		self    :					Optional[Any]       ,				lowercase_    :					str=768       ,				lowercase_    :					Union[str, Any]=12       ,				lowercase_    :					int=12       ,				lowercase_    :					str=3072       ,				lowercase_    :					Dict="gelu"       ,				lowercase_    :					Any=0.0       ,				lowercase_    :					Optional[int]=0.0       ,				lowercase_    :					int=0.02       ,				lowercase_    :					Union[str, Any]=1e-12       ,				lowercase_    :					Optional[Any]=224       ,				lowercase_    :					List[Any]=16       ,				lowercase_    :					List[Any]=3       ,				lowercase_    :					Tuple=False       ,				lowercase_    :					Dict=False       ,				lowercase_    :					Union[str, Any]=False       ,				lowercase_    :					List[Any]=False       ,				lowercase_    :					Dict=0.1       ,				lowercase_    :					Optional[int]=0.1       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Optional[int]=[3, 5, 7, 11]       ,				lowercase_    :					Tuple=[1, 2, 3, 6]       ,				lowercase_    :					List[Any]=True       ,				lowercase_    :					Tuple=0.4       ,				lowercase_    :					Tuple=256       ,				lowercase_    :					Optional[int]=1       ,				lowercase_    :					Optional[Any]=False       ,				lowercase_    :					Union[str, Any]=255       ,				**lowercase_    :					str       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				layer_norm_eps

									snake_case_									=				image_size
									snake_case_									=				patch_size
									snake_case_									=				num_channels
									snake_case_									=				use_mask_token
									snake_case_									=				use_absolute_position_embeddings
									snake_case_									=				use_relative_position_bias
									snake_case_									=				use_shared_relative_position_bias
									snake_case_									=				layer_scale_init_value
									snake_case_									=				drop_path_rate
									snake_case_									=				use_mean_pooling
									# decode head attributes (semantic segmentation)
									snake_case_									=				out_indices
									snake_case_									=				pool_scales
									# auxiliary head attributes (semantic segmentation)
									snake_case_									=				use_auxiliary_head
									snake_case_									=				auxiliary_loss_weight
									snake_case_									=				auxiliary_channels
									snake_case_									=				auxiliary_num_convs
									snake_case_									=				auxiliary_concat_input
									snake_case_									=				semantic_loss_ignore_index







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		version.parse("1.11"     )


				@property
				def   A_ (		self    :					Any					):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									    ]					)


				@property
				def   A_ (		self    :					str					):
									return 1e-4




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






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

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






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

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

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






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


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

