
"""simple docstring"""







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:
       lowercase_						    =					None


lowercase_						    =					logging.get_logger(__name__)

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

lowercase_						    =					{
    "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"
        ),
    },
}

lowercase_						    =					{
    "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 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




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




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



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  AddedToken(a_     ,   lstrip=a_     ,   rstrip=a_ ) if isinstance(a_     ,   a_ ) else mask_token

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

      UpperCAmelCase_						:    Dict    					=  vocab_file
      UpperCAmelCase_						:    str    					=  False if not self.vocab_file else True




 def   a      ( self       : Any     ,   a_       : List[int]     ,   a_       : 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]
      UpperCAmelCase_						:    List[str]    					=  [self.cls_token_id]
      UpperCAmelCase_						:    int    					=  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep




 def   a      ( self       : Optional[int]     ,   a_       : List[int]     ,   a_       : Optional[List[int]] = None )->					List[int]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  [self.sep_token_id]
      UpperCAmelCase_						:    List[Any]    					=  [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       : Any     ,   a_       : str     ,   a_       : 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(a_ ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
           return
      UpperCAmelCase_						:    Dict    					=  os.path.join(
          a_     ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

      return (out_vocab_file,)



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

