




'''simple docstring'''



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
     from .tokenization_deberta_va import DebertaVaTokenizer
else:
     UpperCAmelCase_       :  Optional[Any]      =		None

UpperCAmelCase_       :  Optional[int]      =		logging.get_logger(__name__)

UpperCAmelCase_       :  Tuple      =		{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :  Union[str, Any]      =		{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

UpperCAmelCase_       :  Optional[int]      =		{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

UpperCAmelCase_       :  Any      =		{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Tuple        =			VOCAB_FILES_NAMES
       __lowercase  :      str        =			PRETRAINED_VOCAB_FILES_MAP
       __lowercase  :      Union[str, Any]        =			PRETRAINED_INIT_CONFIGURATION
       __lowercase  :      Union[str, Any]        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase  :      int        =			DebertaVaTokenizer


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=False    ,  __SCREAMING_SNAKE_CASE=False    ,  __SCREAMING_SNAKE_CASE="[CLS]"    ,  __SCREAMING_SNAKE_CASE="[SEP]"    ,  __SCREAMING_SNAKE_CASE="[UNK]"    ,  __SCREAMING_SNAKE_CASE="[SEP]"    ,  __SCREAMING_SNAKE_CASE="[PAD]"    ,  __SCREAMING_SNAKE_CASE="[CLS]"    ,  __SCREAMING_SNAKE_CASE="[MASK]"    ,  **__SCREAMING_SNAKE_CASE    ,  )      ->  None:



            '''simple docstring'''




            super().__init__(
                __SCREAMING_SNAKE_CASE    ,  tokenizer_file=__SCREAMING_SNAKE_CASE    ,  do_lower_case=__SCREAMING_SNAKE_CASE    ,  bos_token=__SCREAMING_SNAKE_CASE    ,  eos_token=__SCREAMING_SNAKE_CASE    ,  unk_token=__SCREAMING_SNAKE_CASE    ,  sep_token=__SCREAMING_SNAKE_CASE    ,  pad_token=__SCREAMING_SNAKE_CASE    ,  cls_token=__SCREAMING_SNAKE_CASE    ,  mask_token=__SCREAMING_SNAKE_CASE    ,  split_by_punct=__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE    ,  )

            __snake_case    					=  do_lower_case
            __snake_case    					=  split_by_punct
            __snake_case    					=  vocab_file
            __snake_case    					=  False if not self.vocab_file else True


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None  )      ->  Optional[Any]:



            '''simple docstring'''




            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case    					=  [self.cls_token_id]
            __snake_case    					=  [self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=False  )      ->  Tuple:



            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__SCREAMING_SNAKE_CASE    ,  token_ids_a=__SCREAMING_SNAKE_CASE    ,  already_has_special_tokens=__SCREAMING_SNAKE_CASE  )

            if token_ids_a is not None:
                 return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]
            return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None  )      ->  Dict:



            '''simple docstring'''




            __snake_case    					=  [self.sep_token_id]
            __snake_case    					=  [self.cls_token_id]
            if token_ids_a is None:
                 return len(cls + token_ids_a + sep  ) * [0]
            return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = None  )      ->  Tuple[str]:



            '''simple docstring'''




            if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                     '''tokenizer.'''  )

            if not os.path.isdir(__SCREAMING_SNAKE_CASE  ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
            __snake_case    					=  os.path.join(
                __SCREAMING_SNAKE_CASE    ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

            if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ):
                 copyfile(self.vocab_file    ,  __SCREAMING_SNAKE_CASE  )

            return (out_vocab_file,)












'''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     lowerCAmelCase   :


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  key
            __snake_case    					=  freq




       def __str__(   self  )      ->  List[Any]:



            '''simple docstring'''




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







def 			_UpperCamelCase (_lowerCamelCase							:     str   ,	_lowerCamelCase							:     List[str]   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     int   ,	_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Tuple	)->   Any:




     '''simple docstring'''





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

     __snake_case    					=  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 			_UpperCamelCase (_lowerCamelCase							:     List[Any]	)->   Union[str, Any]:




     '''simple docstring'''





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

     __snake_case    					=  len(_lowerCamelCase	)

     __snake_case    					=  [nodes[i].key for i in range(_lowerCamelCase	)]
     __snake_case    					=  [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.
     __snake_case    					=  [[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
     __snake_case    					=  [[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
     __snake_case    					=  [[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	):
               __snake_case    					=  i + interval_length - 1

               __snake_case    					=  sys.maxsize  # set the value to "infinity"
               __snake_case    					=  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    					=  dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    __snake_case    					=  dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    __snake_case    					=  left + total[i][j] + right

                    if dp[i][j] > cost:
                         __snake_case    					=  cost
                         __snake_case    					=  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 			_UpperCamelCase ()->   Union[str, Any]:




     '''simple docstring'''





     __snake_case    					=  [Node(_lowerCamelCase   ,	randint(1   ,	50	)	) for i in range(10   ,	0   ,	-1	)]
     find_optimal_binary_search_tree(_lowerCamelCase	)


if __name__ == "__main__":
     main()






