




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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Dict										=[[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_:
																__magic_name__						:						Optional[Any]										=max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]						,  dp[i - 1][w_] )
												else:
																__magic_name__						:						str										=dp[i - 1][w_]

				return dp[n][w_], dp




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				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""" )

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

				__magic_name__					,			__magic_name__						:						List[Any]										=knapsack(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						set										=set()
				_construct_solution(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

				return optimal_val, example_optional_set




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# 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(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__":
							UpperCAmelCase_		:					Dict          =  [3, 2, 4, 4]
							UpperCAmelCase_		:					str          =  [4, 3, 2, 3]
							UpperCAmelCase_		:					Any          =  4
							UpperCAmelCase_		:					List[str]          =  6
							UpperCAmelCase_		:					Tuple          =  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
							UpperCAmelCase_    ,  UpperCAmelCase_		:					Dict          =  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
							UpperCAmelCase_    ,  UpperCAmelCase_		:					Any          =  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)










from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




