






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case							      =						{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							      =						[
     """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FlaubertForMultipleChoice""",
     """FlaubertForQuestionAnswering""",
     """FlaubertForQuestionAnsweringSimple""",
     """FlaubertForSequenceClassification""",
     """FlaubertForTokenClassification""",
     """FlaubertModel""",
     """FlaubertWithLMHeadModel""",
     """FlaubertPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							      =						[
     """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFFlaubertForMultipleChoice""",
     """TFFlaubertForQuestionAnsweringSimple""",
     """TFFlaubertForSequenceClassification""",
     """TFFlaubertForTokenClassification""",
     """TFFlaubertModel""",
     """TFFlaubertPreTrainedModel""",
     """TFFlaubertWithLMHeadModel""",
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

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













from __future__ import annotations







class     UpperCAmelCase_		:



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
          UpperCamelCase :Tuple										=     value
          UpperCamelCase :Node | None										=     None
          UpperCamelCase :Node | None										=     None







class     UpperCAmelCase_		:



      """simple docstring"""
      UpperCamelCase_       :      int
      def __init__(       self				)       ->   None:
          UpperCamelCase :List[Any]										=     0
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   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 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None				)       ->   int:
          if node is None:
              return 0
          if target is not None:
              UpperCamelCase :Tuple										=     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()





