


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Dict							=		{
    """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 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''big_bird'''







				def __init__(						self       ,					__lowerCAmelCase=5_0_3_5_8       ,					__lowerCAmelCase=7_6_8       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=3_0_7_2       ,					__lowerCAmelCase="gelu_new"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=4_0_9_6       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=1E-12       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=0       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=6_6       ,					__lowerCAmelCase="block_sparse"       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=False       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								super().__init__(
								    pad_token_id=__lowerCAmelCase       ,					bos_token_id=__lowerCAmelCase       ,					eos_token_id=__lowerCAmelCase       ,					sep_token_id=__lowerCAmelCase       ,					**__lowerCAmelCase       ,					)

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

								__magic_name__		:Dict        =      rescale_embeddings
								__magic_name__		:Optional[Any]        =      attention_type
								__magic_name__		:Tuple        =      use_bias
								__magic_name__		:Dict        =      block_size
								__magic_name__		:List[Any]        =      num_random_blocks
								__magic_name__		:List[str]        =      classifier_dropout







class 						lowerCamelCase_				(      lowerCamelCase ):







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								if self.task == "multiple-choice":
												__magic_name__		:Union[str, Any]        =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
												__magic_name__		:List[Any]        =      {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								    ]     )














def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[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__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

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

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		[3, 2, 4, 4]
					SCREAMING_SNAKE_CASE__      :			Any							=		[4, 3, 2, 3]
					SCREAMING_SNAKE_CASE__      :			List[str]							=		4
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		6
					SCREAMING_SNAKE_CASE__      :			str							=		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		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
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		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)






