


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      str ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  key
             lowerCamelCase__		:    Any   =  freq





       def __str__(       self							:      Optional[Any]	):


             '''simple docstring'''



             return f"Node(key={self.key}, freq={self.freq})"



def 				lowercase_		(		_A      :   List[Any]   , _A      :   Optional[int]   , _A      :   str   , _A      :   str   , _A      :   Union[str, Any]   , _A      :   Dict						):

      """simple docstring"""




      if i > j or i < 0 or j > len(_A						) - 1:
            return

      lowerCamelCase__		:    int   =  root[i][j]
      if parent == -1:  # root does not have a parent
            print(F"{key[node]} is the root of the binary search tree."						)
      elif is_left:
            print(F"{key[node]} is the left child of key {parent}."						)
      else:
            print(F"{key[node]} is the right child of key {parent}."						)

      print_binary_search_tree(_A   , _A   , _A   , node - 1   , key[node]   , _A						)
      print_binary_search_tree(_A   , _A   , node + 1   , _A   , key[node]   , _A						)

def 				lowercase_		(		_A      :   List[str]						):

      """simple docstring"""




      nodes.sort(key=lambda _A						: node.key						)

      lowerCamelCase__		:    str   =  len(_A						)

      lowerCamelCase__		:    Optional[int]   =  [nodes[i].key for i in range(_A						)]
      lowerCamelCase__		:    Optional[Any]   =  [nodes[i].freq for i in range(_A						)]

      # This 2D array stores the overall tree cost (which's as minimized as possible);
      # for a single key, cost is equal to frequency of the key.
      lowerCamelCase__		:    str   =  [[freqs[i] if i == j else 0 for j in range(_A						)] for i in range(_A						)]
      # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
      # array
      lowerCamelCase__		:    Dict   =  [[freqs[i] if i == j else 0 for j in range(_A						)] for i in range(_A						)]
      # stores tree roots that will be used later for constructing binary search tree
      lowerCamelCase__		:    Any   =  [[i if i == j else 0 for j in range(_A						)] for i in range(_A						)]

      for interval_length in range(2   , n + 1						):
            for i in range(n - interval_length + 1						):
                  lowerCamelCase__		:    Tuple   =  i + interval_length - 1

                  lowerCamelCase__		:    Any   =  sys.maxsize  # set the value to "infinity"
                  lowerCamelCase__		:    int   =  total[i][j - 1] + freqs[j]

                  # Apply Knuth's optimization
                  # Loop without optimization: for r in range(i, j + 1):
                  for r in range(root[i][j - 1]   , root[i + 1][j] + 1						):  # r is a temporal root
                        lowerCamelCase__		:    Tuple   =  dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                        lowerCamelCase__		:    Union[str, Any]   =  dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                        lowerCamelCase__		:    Optional[Any]   =  left + total[i][j] + right

                        if dp[i][j] > cost:
                              lowerCamelCase__		:    Optional[Any]   =  cost
                              lowerCamelCase__		:    int   =  r

      print("Binary search tree nodes:"						)
      for node in nodes:
            print(_A						)

      print(F"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."						)
      print_binary_search_tree(_A   , _A   , 0   , n - 1   , -1   , _A						)

def 				lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:    int   =  [Node(_A   , randint(1   , 50						)						) for i in range(10   , 0   , -1						)]
      find_optimal_binary_search_tree(_A						)


if __name__ == "__main__":
    main()






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A				:   int		= logging.get_logger(__name__)

A				:   Any		= {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
    import detectrona







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "layoutlmv2"





       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      int=30522 ,				__lowerCamelCase							:      List[Any]=768 ,				__lowerCamelCase							:      Any=12 ,				__lowerCamelCase							:      List[Any]=12 ,				__lowerCamelCase							:      Optional[Any]=3072 ,				__lowerCamelCase							:      List[Any]="gelu" ,				__lowerCamelCase							:      Any=0.1 ,				__lowerCamelCase							:      Union[str, Any]=0.1 ,				__lowerCamelCase							:      int=512 ,				__lowerCamelCase							:      str=2 ,				__lowerCamelCase							:      Any=0.0_2 ,				__lowerCamelCase							:      List[Any]=1E-1_2 ,				__lowerCamelCase							:      List[str]=0 ,				__lowerCamelCase							:      Union[str, Any]=1024 ,				__lowerCamelCase							:      Tuple=128 ,				__lowerCamelCase							:      Tuple=32 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      List[Any]=256 ,				__lowerCamelCase							:      Dict=64 ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Union[str, Any]=[7, 7, 256] ,				__lowerCamelCase							:      str=128 ,				__lowerCamelCase							:      Dict=128 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      int=True ,				__lowerCamelCase							:      str=False ,				__lowerCamelCase							:      Tuple=None ,				**__lowerCamelCase							:      str ,				):


             '''simple docstring'''



             super().__init__(
                 vocab_size=__lowerCamelCase ,				hidden_size=__lowerCamelCase ,				num_hidden_layers=__lowerCamelCase ,				num_attention_heads=__lowerCamelCase ,				intermediate_size=__lowerCamelCase ,				hidden_act=__lowerCamelCase ,				hidden_dropout_prob=__lowerCamelCase ,				attention_probs_dropout_prob=__lowerCamelCase ,				max_position_embeddings=__lowerCamelCase ,				type_vocab_size=__lowerCamelCase ,				initializer_range=__lowerCamelCase ,				layer_norm_eps=__lowerCamelCase ,				pad_token_id=__lowerCamelCase ,				**__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  max_ad_position_embeddings
             lowerCamelCase__		:    int   =  max_rel_pos
             lowerCamelCase__		:    List[Any]   =  rel_pos_bins
             lowerCamelCase__		:    str   =  fast_qkv
             lowerCamelCase__		:    Optional[Any]   =  max_rel_ad_pos
             lowerCamelCase__		:    List[Any]   =  rel_ad_pos_bins
             lowerCamelCase__		:    Optional[Any]   =  convert_sync_batchnorm
             lowerCamelCase__		:    Any   =  image_feature_pool_shape
             lowerCamelCase__		:    List[Any]   =  coordinate_size
             lowerCamelCase__		:    Any   =  shape_size
             lowerCamelCase__		:    List[str]   =  has_relative_attention_bias
             lowerCamelCase__		:    Optional[int]   =  has_spatial_attention_bias
             lowerCamelCase__		:    Optional[Any]   =  has_visual_segment_embedding
             lowerCamelCase__		:    List[str]   =  (
                 detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
             )





       @classmethod
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "MODEL.MASK_ON": True,
                 "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                 "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                 "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                 "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
                 "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
                 "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
                 "MODEL.POST_NMS_TOPK_TEST": 1000,
                 "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
                 "MODEL.ROI_HEADS.NUM_CLASSES": 5,
                 "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
                 "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
                 "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
                 "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
                 "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                 "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                 "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                 "MODEL.RESNETS.DEPTH": 101,
                 "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                 "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.RESNETS.NUM_GROUPS": 32,
                 "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                 "MODEL.RESNETS.STRIDE_IN_1X1": False,
             }





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  detectrona.config.get_cfg()
             for k, v in self.detectrona_config_args.items():
                   lowerCamelCase__		:    List[Any]   =  k.split("."	)
                   lowerCamelCase__		:    int   =  detectrona_config
                   for attribute in attributes[:-1]:
                         lowerCamelCase__		:    Any   =  getattr(__lowerCamelCase ,				__lowerCamelCase	)
                   setattr(__lowerCamelCase ,				attributes[-1] ,				__lowerCamelCase	)

             return detectrona_config


