





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""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Union[str, Any]			   =							VOCAB_FILES_NAMES
						__magic_name__:      Optional[Any]			   =							PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:      Union[str, Any]			   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:      int			   =							["input_ids", "attention_mask"]
						__magic_name__:      Dict			   =							XLMRobertaTokenizer






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







							"""simple docstring"""


							snake_case_		:      Dict           =						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		, )

							snake_case_		:      List[Any]           =						vocab_file
							snake_case_		:      List[str]           =						False if not self.vocab_file else True






						def UpperCAmelCase_					(   self   :   Tuple		, _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]
							snake_case_		:      List[Any]           =						[self.cls_token_id]
							snake_case_		:      int           =						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   List[int]		, _A   :   Optional[List[int]] = None						)				->     List[int]:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						[self.sep_token_id]
							snake_case_		:      Optional[int]           =						[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   :   int		, _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
							snake_case_		:      Any           =						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,)








def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if not root or root not in binary_tree_mirror_dictionary:
		return
	snake_case_    ,snake_case_		:      Tuple           =						binary_tree_mirror_dictionary[root][:2]
	snake_case_		:      List[str]           =						[right_child, left_child]
	binary_tree_mirror_dict(__a ,			__a )
	binary_tree_mirror_dict(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = 1 ):
	if not binary_tree:
		raise ValueError('binary tree cannot be empty' )
	if root not in binary_tree:
		snake_case_		:      Dict           =						f"""root {root} is not present in the binary_tree"""
		raise ValueError(__a )
	snake_case_		:      Any           =						dict(__a )
	binary_tree_mirror_dict(__a ,			__a )
	return binary_tree_mirror_dictionary


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
						print(F'''Binary tree: {binary_tree}''')
						_SCREAMING_SNAKE_CASE   						=		binary_tree_mirror(binary_tree, 5)
						print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')

