



"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


SCREAMING_SNAKE_CASE_:Any					=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE_:int					=						{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE_:List[Any]					=						{
    "unc-nlp/lxmert-base-uncased": 512,
}

SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}






class SCREAMING_SNAKE_CASE__					(		_SCREAMING_SNAKE_CASE   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Dict =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[int] =				PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase      :			str =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			str =				LxmertTokenizer






    def __init__(	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__="[UNK]",		lowerCamelCase__="[SEP]",		lowerCamelCase__="[PAD]",		lowerCamelCase__="[CLS]",		lowerCamelCase__="[MASK]",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(
            lowerCamelCase__,		tokenizer_file=lowerCamelCase__,		do_lower_case=lowerCamelCase__,		unk_token=lowerCamelCase__,		sep_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		cls_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		tokenize_chinese_chars=lowerCamelCase__,		strip_accents=lowerCamelCase__,		**lowerCamelCase__,		)

        A			:	Optional[Any]										=       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
        if (
            normalizer_state.get("""lowercase""",		lowerCamelCase__		) != do_lower_case
            or normalizer_state.get("""strip_accents""",		lowerCamelCase__		) != strip_accents
            or normalizer_state.get("""handle_chinese_chars""",		lowerCamelCase__		) != tokenize_chinese_chars
        ):
            A			:	Tuple										=       getattr(lowerCamelCase__,		normalizer_state.pop("""type"""		)		)
            A			:	Optional[Any]										=       do_lower_case
            A			:	Dict										=       strip_accents
            A			:	List[Any]										=       tokenize_chinese_chars
            A			:	List[Any]										=       normalizer_class(**lowerCamelCase__		)

        A			:	Tuple										=       do_lower_case






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=None		):
        A			:	Any										=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

        return output






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	str										=       [self.sep_token_id]
        A			:	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		) * [0] + len(token_ids_a + sep		) * [1]






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	int										=       self._tokenizer.model.save(lowerCamelCase__,		name=lowerCamelCase__		)
        return tuple(lowerCamelCase__		)







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


