





"""simple docstring"""




from graphs.minimum_spanning_tree_kruskal import kruskal






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Any	=				9
	a      :		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      :		Any	=				kruskal(_lowercase    ,       _lowercase					)

	a      :		Optional[int]	=				[
	    [7, 6, 1],
	    [2, 8, 2],
	    [6, 5, 2],
	    [0, 1, 4],
	    [2, 5, 4],
	    [2, 3, 7],
	    [0, 7, 8],
	    [3, 4, 9],
	]

	assert sorted(_lowercase					) == sorted(_lowercase					)








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






