






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:
		__UpperCAmelCase										=		None


__UpperCAmelCase										=		logging.get_logger(__name__)

__UpperCAmelCase										=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase										=		{
    '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'
        ),
    },
}

__UpperCAmelCase										=		{
    '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   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					List[Any] 		=						VOCAB_FILES_NAMES
		_snake_case					:					Tuple 		=						PRETRAINED_VOCAB_FILES_MAP
		_snake_case					:					List[Any] 		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case					:					Any 		=						['''input_ids''', '''attention_mask''']
		_snake_case					:					Dict 		=						XLMRobertaTokenizer



		def __init__(							self       ,	_UpperCamelCase=None       ,	_UpperCamelCase=None       ,	_UpperCamelCase="<s>"       ,	_UpperCamelCase="</s>"       ,	_UpperCamelCase="</s>"       ,	_UpperCamelCase="<s>"       ,	_UpperCamelCase="<unk>"       ,	_UpperCamelCase="<pad>"       ,	_UpperCamelCase="<mask>"       ,	**_UpperCamelCase       ,	)			->							str:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_					:   List[str]   =		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       ,	)

							UpperCAmelCase_					:   Any   =		vocab_file
							UpperCAmelCase_					:   Tuple   =		False if not self.vocab_file else True



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:

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



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:
							UpperCAmelCase_					:   int   =		[self.sep_token_id]
							UpperCAmelCase_					:   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       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							Tuple[str]:
							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
							UpperCAmelCase_					:   Optional[Any]   =		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,)














from __future__ import annotations



class 						lowerCamelCase   :







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase = 0     )			->							None:
							UpperCAmelCase_					:   int   =		value
							UpperCAmelCase_					:   Node | None   =		None
							UpperCAmelCase_					:   Node | None   =		None





def   lowercase__   (  __snake_case					:							Node | None  , __snake_case					:							Node | None						):


					'''simple docstring'''


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

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




def   lowercase__   (  __snake_case					:							Node | None						):


					'''simple docstring'''


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


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

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

		print('Tree1 is: ')
		print_preorder(treea)
		print('Tree2 is: ')
		print_preorder(treea)
		__UpperCAmelCase										=		merge_two_binary_trees(treea, treea)
		print('Merged Tree is: ')
		print_preorder(merged_tree)






