'''simple docstring'''


# 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 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = key
       UpperCAmelCase_				:  Tuple  = freq






  def __str__(					self		):




       '''simple docstring'''

       return F'''Node(key={self.key}, freq={self.freq})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





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

     UpperCAmelCase_				:  Optional[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(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	node - 1      ,	key[node]      ,	lowerCamelCase_  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	node + 1      ,	lowerCamelCase_      ,	key[node]      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





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

     UpperCAmelCase_				:  int  = len(lowerCamelCase_  )

     UpperCAmelCase_				:  Dict  = [nodes[i].key for i in range(lowerCamelCase_  )]
     UpperCAmelCase_				:  Any  = [nodes[i].freq for i in range(lowerCamelCase_  )]

     # 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.
     UpperCAmelCase_				:  Optional[Any]  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCAmelCase_				:  Any  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # stores tree roots that will be used later for constructing binary search tree
     UpperCAmelCase_				:  Optional[int]  = [[i if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]

     for interval_length in range(2      ,	n + 1  ):
          for i in range(n - interval_length + 1  ):
               UpperCAmelCase_				:  Optional[Any]  = i + interval_length - 1

               UpperCAmelCase_				:  Tuple  = sys.maxsize  # set the value to "infinity"
               UpperCAmelCase_				:  Any  = 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
                    UpperCAmelCase_				:  str  = dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    UpperCAmelCase_				:  Optional[int]  = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    UpperCAmelCase_				:  Optional[int]  = left + total[i][j] + right

                    if dp[i][j] > cost:
                         UpperCAmelCase_				:  Dict  = cost
                         UpperCAmelCase_				:  Tuple  = r

     print('Binary search tree nodes:'  )
     for node in nodes:
          print(lowerCamelCase_  )

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


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = [Node(lowerCamelCase_      ,	randint(1      ,	50  )  ) for i in range(10      ,	0      ,	-1  )]
     find_optimal_binary_search_tree(lowerCamelCase_  )


if __name__ == "__main__":
 main()







"""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


lowerCAmelCase__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				patch_size
									UpperCAmelCase__			 =				hidden_sizes
									UpperCAmelCase__			 =				neck_hidden_sizes
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				mlp_ratio
									UpperCAmelCase__			 =				expand_ratio
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				conv_kernel_size
									UpperCAmelCase__			 =				output_stride
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				qkv_bias

									# decode head attributes for semantic segmentation
									UpperCAmelCase__			 =				aspp_out_channels
									UpperCAmelCase__			 =				atrous_rates
									UpperCAmelCase__			 =				aspp_dropout_prob
									UpperCAmelCase__			 =				semantic_loss_ignore_index






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







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







					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									if self.task == "image-classification":
													return OrderedDict([('logits', {0: 'batch'})]     )
									else:
													return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )







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



