code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  _lowerCamelCase				    =       {'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				_lowerCamelCase				    =       [
  				    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'HubertForCTC',
  				    'HubertForSequenceClassification',
  				    'HubertModel',
  				    'HubertPreTrainedModel',
  				]
  
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				_lowerCamelCase				    =       [
  				    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'TFHubertForCTC',
  				    'TFHubertModel',
  				    'TFHubertPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_hubert import (
  								    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    HubertForCTC,
  								    HubertForSequenceClassification,
  								    HubertModel,
  								    HubertPreTrainedModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_hubert import (
  								    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    TFHubertForCTC,
  								    TFHubertModel,
  								    TFHubertPreTrainedModel,
  								)
  
  
  else:
  				import sys
  
  				_lowerCamelCase				    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ):
  			A							  =      value
  			A							  =      None
  			A							  =      None
  
  
  
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			A							  =      0
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      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        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int | None = None      ):
  			if node is None:
  				return 0
  			if target is not None:
  				A							  =      target
  
  			self.depth_first_search(_lowerCAmelCase      ,      node.value      )
  			self.path_sum(node.left      )
  			self.path_sum(node.right      )
  
  			return self.paths
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  