
# 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      A__       :





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Union[str, Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= key
											__lowercase								= freq





							def __str__(			self  :       Optional[int]    )						->		List[str]:



											"""simple docstring"""


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






def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				int      )					->						List[str]:
				if i > j or i < 0 or j > len(SCREAMING_SNAKE_CASE      ) - 1:
								return

				__lowercase								= 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(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							node - 1   ,							key[node]   ,							SCREAMING_SNAKE_CASE      )
				print_binary_search_tree(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							node + 1   ,							SCREAMING_SNAKE_CASE   ,							key[node]   ,							SCREAMING_SNAKE_CASE      )




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int      )					->						Any:
				nodes.sort(key=lambda SCREAMING_SNAKE_CASE      : node.key      )

				__lowercase								= len(SCREAMING_SNAKE_CASE      )

				__lowercase								= [nodes[i].key for i in range(SCREAMING_SNAKE_CASE      )]
				__lowercase								= [nodes[i].freq for i in range(SCREAMING_SNAKE_CASE      )]

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

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

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

																if dp[i][j] > cost:
																				__lowercase								= cost
																				__lowercase								= r

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

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




def 							__SCREAMING_SNAKE_CASE       (		)					->						int:
				# A sample binary search tree
				__lowercase								= [Node(SCREAMING_SNAKE_CASE   ,							randint(1   ,							50      )      ) for i in range(10   ,							0   ,							-1      )]
				find_optimal_binary_search_tree(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
							main()







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							SCREAMING_SNAKE_CASE__							=    None


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__							=    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						List[str]         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						List[Any]         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Dict         =							["input_ids", "attention_mask"]
							lowerCAmelCase__		:						List[str]         =							XLMRobertaTokenizer





							def __init__(			self  :       Tuple					,			_UpperCAmelCase  :       Tuple=None					,			_UpperCAmelCase  :       int=None					,			_UpperCAmelCase  :       Tuple="<s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       List[Any]="<s>"					,			_UpperCAmelCase  :       List[Any]="<unk>"					,			_UpperCAmelCase  :       Optional[int]="<pad>"					,			_UpperCAmelCase  :       Union[str, Any]="<mask>"					,			**_UpperCAmelCase  :       Optional[int]					,			)						->		Dict:



											"""simple docstring"""


											__lowercase								= AddedToken(_UpperCAmelCase					,			lstrip=_UpperCAmelCase					,			rstrip=_UpperCAmelCase    ) if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) else mask_token

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

											__lowercase								= vocab_file
											__lowercase								= False if not self.vocab_file else True





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


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





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											__lowercase								= [self.sep_token_id]
											__lowercase								= [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 + sep + token_ids_a + sep    ) * [0]





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[str] = 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(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

											return (out_vocab_file,)





