




'''simple docstring'''







import sys
from random import randint







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a       )			->							Any:
          snake_case :			Dict					   = key
          snake_case :			Optional[Any]					   = freq







     def __str__(							self       )			->							Union[str, Any]:
          return F'''Node(key={self.key}, freq={self.freq})'''



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase							):






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

     snake_case :			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(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      node - 1    ,      key[node]    ,      UpperCamelCase__							)
     print_binary_search_tree(UpperCamelCase__    ,      UpperCamelCase__    ,      node + 1    ,      UpperCamelCase__    ,      key[node]    ,      UpperCamelCase__							)



def 					lowerCamelCase__							(			__lowercase							):
     nodes.sort(key=lambda __lowercase							: node.key							)

     snake_case :			int					   = len(UpperCamelCase__							)

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

     # 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.
     snake_case :			Union[str, Any]					   = [[freqs[i] if i == j else 0 for j in range(UpperCamelCase__							)] for i in range(UpperCamelCase__							)]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     snake_case :			List[str]					   = [[freqs[i] if i == j else 0 for j in range(UpperCamelCase__							)] for i in range(UpperCamelCase__							)]
     # stores tree roots that will be used later for constructing binary search tree
     snake_case :			Tuple					   = [[i if i == j else 0 for j in range(UpperCamelCase__							)] for i in range(UpperCamelCase__							)]

     for interval_length in range(2    ,      n + 1							):
          for i in range(n - interval_length + 1							):
               snake_case :			str					   = i + interval_length - 1

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

                    if dp[i][j] > cost:
                         snake_case :			List[str]					   = cost
                         snake_case :			List[str]					   = r

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

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



def 					lowerCamelCase__							(			):
     snake_case :			str					   = [Node(UpperCamelCase__    ,      randint(1    ,      50							)							) for i in range(10    ,      0    ,      -1							)]
     find_optimal_binary_search_tree(UpperCamelCase__							)


if __name__ == "__main__":
      main()









'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""       )
          if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Optional[Any]					   = []
               for task in task_inputs:
                    snake_case :			Any					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Optional[int]					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

               snake_case :			Dict					   = []
               for texts in texts_list:
                    snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Dict					   = []
               for task in task_inputs:
                    snake_case :			str					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Any					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Dict					   = encoded_inputs.text_inputs

               snake_case :			Tuple					   = []
               for texts in texts_list:
                    snake_case :			Tuple					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



