



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class 		UpperCAmelCase						:
			SCREAMING_SNAKE_CASE__							=     4_2
			SCREAMING_SNAKE_CASE__							=     None
			SCREAMING_SNAKE_CASE__							=     None






UpperCAmelCase_									=							namedtuple("CoinsDistribResult", "moves excess")
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	TreeNode | None				)->int:

						if root is None:
												return 0

						# Validation
						def count_nodes(_SCREAMING_SNAKE_CASE   :	TreeNode | None				) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left				) + count_nodes(node.right				) + 1

						def count_coins(_SCREAMING_SNAKE_CASE   :	TreeNode | None				) -> int:
												if node is None:
																		return 0

												return count_coins(node.left				) + count_coins(node.right				) + node.data

						if count_nodes(_SCREAMING_SNAKE_CASE				) != count_coins(_SCREAMING_SNAKE_CASE				):
												raise ValueError('''The nodes number should be same as the number of coins'''				)

						# Main calculation
						def get_distrib(_SCREAMING_SNAKE_CASE   :	TreeNode | None				) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0     ,				1				)

												_lowerCAmelCase 			=  get_distrib(node.left				)
												_lowerCAmelCase 			=  get_distrib(node.right				)

												_lowerCAmelCase 			=  1 - left_distrib_excess
												_lowerCAmelCase 			=  1 - right_distrib_excess

												_lowerCAmelCase 			=  (
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(_SCREAMING_SNAKE_CASE				)
												    + abs(_SCREAMING_SNAKE_CASE				)
												)
												_lowerCAmelCase 			=  node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

						return get_distrib(_SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)