
"""simple docstring"""

def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		global f  # a global dp table for knapsack
		if f[i][j] < 0:
				if j < wt[i - 1]:
						lowerCamelCase					:      List[Any]          =   mf_knapsack(i - 1,						a_,						a_,						a_      )
				else:
						lowerCamelCase					:      Tuple          =   max(
						    mf_knapsack(i - 1,						a_,						a_,						a_      ),						mf_knapsack(i - 1,						a_,						a_,						j - wt[i - 1]      ) + val[i - 1],						)
				lowerCamelCase					:      Dict          =   val
		return f[i][j]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[int]          =   [[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 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


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

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

		lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   knapsack(a_,						a_,						a_,						a_      )
		lowerCamelCase					:      set          =   set()
		_construct_solution(a_,						a_,						a_,						a_,						a_      )

		return optimal_val, example_optional_set
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_      ):





		'''simple docstring'''


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


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"""

from collections import OrderedDict
from typing import Mapping

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'big_bird'




	def __init__(			self		,			UpperCAmelCase_=50358		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_="gelu_new"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			UpperCAmelCase_=66		,			UpperCAmelCase_="block_sparse"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=False		,			UpperCAmelCase_=64		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			sep_token_id=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      Tuple          =   vocab_size
			lowerCamelCase					:      str          =   max_position_embeddings
			lowerCamelCase					:      str          =   hidden_size
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      List[str]          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   intermediate_size
			lowerCamelCase					:      Optional[int]          =   hidden_act
			lowerCamelCase					:      Optional[int]          =   hidden_dropout_prob
			lowerCamelCase					:      Dict          =   attention_probs_dropout_prob
			lowerCamelCase					:      int          =   initializer_range
			lowerCamelCase					:      Union[str, Any]          =   type_vocab_size
			lowerCamelCase					:      Union[str, Any]          =   layer_norm_eps
			lowerCamelCase					:      int          =   use_cache

			lowerCamelCase					:      Tuple          =   rescale_embeddings
			lowerCamelCase					:      Dict          =   attention_type
			lowerCamelCase					:      Optional[int]          =   use_bias
			lowerCamelCase					:      Optional[int]          =   block_size
			lowerCamelCase					:      Tuple          =   num_random_blocks
			lowerCamelCase					:      List[Any]          =   classifier_dropout






class 	_lowercase       (    __UpperCAmelCase		):




	@property
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCamelCase					:      Tuple          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
			else:
					lowerCamelCase					:      int          =   {0: 'batch', 1: 'sequence'}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ] )






