



'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase     :   Union[str, Any]		    =     {
    'agents': ['Agent', 'AzureOpenAiAgent', 'HfAgent', 'LocalAgent', 'OpenAiAgent'],
    'base': ['PipelineTool', 'RemoteTool', 'Tool', 'launch_gradio_demo', 'load_tool'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase     :   List[str]		    =     ['DocumentQuestionAnsweringTool']
							__lowerCAmelCase     :   Dict		    =     ['ImageCaptioningTool']
							__lowerCAmelCase     :   Optional[int]		    =     ['ImageQuestionAnsweringTool']
							__lowerCAmelCase     :   Optional[int]		    =     ['ImageSegmentationTool']
							__lowerCAmelCase     :   List[Any]		    =     ['SpeechToTextTool']
							__lowerCAmelCase     :   Tuple		    =     ['TextClassificationTool']
							__lowerCAmelCase     :   Dict		    =     ['TextQuestionAnsweringTool']
							__lowerCAmelCase     :   Optional[Any]		    =     ['TextSummarizationTool']
							__lowerCAmelCase     :   Optional[Any]		    =     ['TextToSpeechTool']
							__lowerCAmelCase     :   List[Any]		    =     ['TranslationTool']

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							__lowerCAmelCase     :   Tuple		    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									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 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   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()




