'''simple docstring'''

from __future__ import annotations







class     _lowercase					:


 '''simple docstring'''






 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   None:
  __lowerCAmelCase    							=						value
  __lowerCAmelCase    							=						None
  __lowerCAmelCase    							=						None







class     _lowercase					:


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	int
 def __init__(				self       :     Optional[int]							)  ->   None:
  __lowerCAmelCase    							=						0
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Node | None				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   None:
  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]				,	SCREAMING_SNAKE_CASE__       :     Node | None				,	SCREAMING_SNAKE_CASE__       :     int | None = None							)  ->   int:
  if node is None:
   return 0
  if target is not None:
   __lowerCAmelCase    							=						target

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

  return self.paths


if __name__ == "__main__":
    import doctest

    doctest.testmod()







'''simple docstring'''

from typing import TYPE_CHECKING

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


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

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






