






"""simple docstring"""


from __future__ import annotations

from queue import Queue



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  a ^= b
  b ^= a
  a ^= b
  return a, b



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					1
  while (1 << j) < max_node:
    for i in range(1						,			max_node + 1					):
      __A		     =					parent[j - 1][parent[j - 1][i]]
    j += 1
  return parent



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if level[u] < level[v]:
    __A     ,       __A		     =					swap(__UpperCamelCase						,			__UpperCamelCase					)
  # making depth of u same as depth of v
  for i in range(1_8						,			-1						,			-1					):
    if level[u] - (1 << i) >= level[v]:
      __A		     =					parent[i][u]
    # at the same depth if u==v that mean lca is found
  if u == v:
    return u
  # moving both nodes upwards till lca in found
  for i in range(1_8						,			-1						,			-1					):
    if parent[i][u] not in [0, parent[i][v]]:
      __A     ,       __A		     =					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
  return parent[0][u]



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase = 1						,			):




  """simple docstring"""







  __A		     =					0
  __A		     =					Queue(maxsize=__UpperCamelCase					)
  q.put(__UpperCamelCase					)
  while q.qsize() != 0:
    __A		     =					q.get()
    for v in graph[u]:
      if level[v] == -1:
        __A		     =					level[u] + 1
        q.put(__UpperCamelCase					)
        __A		     =					u
  return level, parent



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					1_3
  # initializing with 0
  __A		     =					[[0 for _ in range(max_node + 1_0					)] for _ in range(2_0					)]
  # initializing with -1 which means every node is unvisited
  __A		     =					[-1 for _ in range(max_node + 1_0					)]
  __A		     =					{
      1: [2, 3, 4],
      2: [5],
      3: [6, 7],
      4: [8],
      5: [9, 1_0],
      6: [1_1],
      7: [],
      8: [1_2, 1_3],
      9: [],
      1_0: [],
      1_1: [],
      1_2: [],
      1_3: [],
  }
  __A     ,       __A		     =					breadth_first_search(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			1					)
  __A		     =					create_sparse(__UpperCamelCase						,			__UpperCamelCase					)
  print('''LCA of node 1 and 3 is: '''						,			lowest_common_ancestor(1						,			3						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 5 and 6 is: '''						,			lowest_common_ancestor(5						,			6						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 7 and 11 is: '''						,			lowest_common_ancestor(7						,			1_1						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 6 and 7 is: '''						,			lowest_common_ancestor(6						,			7						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 4 and 12 is: '''						,			lowest_common_ancestor(4						,			1_2						,			__UpperCamelCase						,			__UpperCamelCase					)					)
  print('''LCA of node 8 and 8 is: '''						,			lowest_common_ancestor(8						,			8						,			__UpperCamelCase						,			__UpperCamelCase					)					)


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:
 a_ 		=  None


a_ 		=  logging.get_logger(__name__)

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

a_ 		=  {
    "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"
        ),
    },
}

a_ 		=  {
    "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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       VOCAB_FILES_NAMES
      lowerCAmelCase__      :			List[Any]     =       PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :			Optional[Any]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :			Optional[int]     =       ["input_ids", "attention_mask"]
      lowerCAmelCase__      :			Union[str, Any]     =       XLMRobertaTokenizer







      def __init__(  self       :			str			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			Any=None			,							snake_case       :			Union[str, Any]="<s>"			,							snake_case       :			Tuple="</s>"			,							snake_case       :			List[Any]="</s>"			,							snake_case       :			Any="<s>"			,							snake_case       :			str="<unk>"			,							snake_case       :			int="<pad>"			,							snake_case       :			Any="<mask>"			,							**snake_case       :			str			,							):
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase			=    AddedToken(snake_case			,							lstrip=snake_case			,							rstrip=snake_case					) if isinstance(snake_case			,							snake_case					) else mask_token

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

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







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):

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







      def    snake_case						(  self       :			Any			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):
          __UpperCamelCase			=    [self.sep_token_id]
          __UpperCamelCase			=    [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    snake_case						(  self       :			Dict			,							snake_case       :			str			,							snake_case       :			Optional[str] = None					):
          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(snake_case					):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory."					)
              return
          __UpperCamelCase			=    os.path.join(
              snake_case			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

          return (out_vocab_file,)






