





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   						=		{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		["""DebertaV2TokenizerFast"""]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		[
						    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDebertaV2ForMaskedLM""",
						    """TFDebertaV2ForQuestionAnswering""",
						    """TFDebertaV2ForSequenceClassification""",
						    """TFDebertaV2ForTokenClassification""",
						    """TFDebertaV2Model""",
						    """TFDebertaV2PreTrainedModel""",
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		[
						    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DebertaV2ForMaskedLM""",
						    """DebertaV2ForMultipleChoice""",
						    """DebertaV2ForQuestionAnswering""",
						    """DebertaV2ForSequenceClassification""",
						    """DebertaV2ForTokenClassification""",
						    """DebertaV2Model""",
						    """DebertaV2PreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

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






'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						if node is None:
							return

						if path_sum == self.target:
							self.paths += 1

						if node.left:
							self.depth_first_search(node.left  ,	path_sum + node.left.value       )
						if node.right:
							self.depth_first_search(node.right  ,	path_sum + node.right.value       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
