
"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           RemBertConfig,
           RemBertForCausalLM,
           RemBertForMaskedLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
           RemBertModel,
       )
       from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Dict     ,   a_       : Union[str, Any]     ,   a_       : Optional[Any]=13     ,   a_       : Any=7     ,   a_       : Optional[Any]=True     ,   a_       : Dict=True     ,   a_       : Union[str, Any]=True     ,   a_       : Optional[Any]=True     ,   a_       : Dict=99     ,   a_       : Union[str, Any]=32     ,   a_       : Dict=18     ,   a_       : Optional[Any]=43     ,   a_       : Tuple=5     ,   a_       : Optional[int]=4     ,   a_       : Optional[Any]=37     ,   a_       : str="gelu"     ,   a_       : Dict=0.1     ,   a_       : Optional[int]=0.1     ,   a_       : List[str]=5_12     ,   a_       : Optional[int]=16     ,   a_       : Tuple=2     ,   a_       : List[str]=0.02     ,   a_       : Optional[int]=3     ,   a_       : Dict=4     ,   a_       : Any=None     ,   )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  parent
      UpperCAmelCase_						:    Union[str, Any]    					=  batch_size
      UpperCAmelCase_						:    str    					=  seq_length
      UpperCAmelCase_						:    List[Any]    					=  is_training
      UpperCAmelCase_						:    Optional[int]    					=  use_input_mask
      UpperCAmelCase_						:    List[str]    					=  use_token_type_ids
      UpperCAmelCase_						:    str    					=  use_labels
      UpperCAmelCase_						:    str    					=  vocab_size
      UpperCAmelCase_						:    List[str]    					=  hidden_size
      UpperCAmelCase_						:    str    					=  input_embedding_size
      UpperCAmelCase_						:    Optional[int]    					=  output_embedding_size
      UpperCAmelCase_						:    Optional[int]    					=  num_hidden_layers
      UpperCAmelCase_						:    Any    					=  num_attention_heads
      UpperCAmelCase_						:    Dict    					=  intermediate_size
      UpperCAmelCase_						:    Dict    					=  hidden_act
      UpperCAmelCase_						:    Any    					=  hidden_dropout_prob
      UpperCAmelCase_						:    Tuple    					=  attention_probs_dropout_prob
      UpperCAmelCase_						:    int    					=  max_position_embeddings
      UpperCAmelCase_						:    Any    					=  type_vocab_size
      UpperCAmelCase_						:    int    					=  type_sequence_label_size
      UpperCAmelCase_						:    Any    					=  initializer_range
      UpperCAmelCase_						:    Union[str, Any]    					=  num_labels
      UpperCAmelCase_						:    List[str]    					=  num_choices
      UpperCAmelCase_						:    Dict    					=  scope




 def   a      ( self       : List[Any] )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size )

      UpperCAmelCase_						:    str    					=  None
      if self.use_input_mask:
           UpperCAmelCase_						:    int    					=  random_attention_mask([self.batch_size, self.seq_length] )

      UpperCAmelCase_						:    Union[str, Any]    					=  None
      if self.use_token_type_ids:
           UpperCAmelCase_						:    Optional[int]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.type_vocab_size )

      UpperCAmelCase_						:    Optional[Any]    					=  None
      UpperCAmelCase_						:    Any    					=  None
      UpperCAmelCase_						:    Dict    					=  None
      if self.use_labels:
           UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size]     ,   self.type_sequence_label_size )
           UpperCAmelCase_						:    Dict    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.num_labels )
           UpperCAmelCase_						:    Tuple    					=  ids_tensor([self.batch_size]     ,   self.num_choices )

      UpperCAmelCase_						:    Optional[Any]    					=  RemBertConfig(
          vocab_size=self.vocab_size     ,   hidden_size=self.hidden_size     ,   input_embedding_size=self.input_embedding_size     ,   output_embedding_size=self.output_embedding_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   max_position_embeddings=self.max_position_embeddings     ,   type_vocab_size=self.type_vocab_size     ,   is_decoder=a_     ,   initializer_range=self.initializer_range     ,   )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def   a      ( self       : Optional[int] )->					Optional[Any]:



      """simple docstring"""

      (
          (
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,
      )						:    Tuple    					=  self.prepare_config_and_inputs()

      UpperCAmelCase_						:    List[Any]    					=  True
      UpperCAmelCase_						:    List[str]    					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      UpperCAmelCase_						:    Optional[Any]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   vocab_size=2 )

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )




 def   a      ( self       : Dict     ,   a_       : Union[str, Any]     ,   a_       : List[Any]     ,   a_       : Dict     ,   a_       : Tuple     ,   a_       : str     ,   a_       : str     ,   a_       : Dict )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  RemBertModel(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    Optional[Any]    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_ )
      UpperCAmelCase_						:    Tuple    					=  model(a_     ,   token_type_ids=a_ )
      UpperCAmelCase_						:    Optional[int]    					=  model(a_ )
      self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size) )




 def   a      ( self       : Union[str, Any]     ,   a_       : str     ,   a_       : int     ,   a_       : Optional[Any]     ,   a_       : Tuple     ,   a_       : Optional[Any]     ,   a_       : int     ,   a_       : List[Any]     ,   a_       : List[Any]     ,   a_       : int     ,   )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  True
      UpperCAmelCase_						:    Optional[int]    					=  RemBertModel(a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    Union[str, Any]    					=  model(
          a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   encoder_hidden_states=a_     ,   encoder_attention_mask=a_     ,   )
      UpperCAmelCase_						:    Optional[int]    					=  model(
          a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   encoder_hidden_states=a_     ,   )
      UpperCAmelCase_						:    str    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_ )
      self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size) )




 def   a      ( self       : Any     ,   a_       : List[str]     ,   a_       : Optional[Any]     ,   a_       : Any     ,   a_       : Any     ,   a_       : Optional[int]     ,   a_       : Any     ,   a_       : int     ,   a_       : int     ,   a_       : Optional[int]     ,   )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  RemBertForCausalLM(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    int    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size) )




 def   a      ( self       : List[str]     ,   a_       : List[str]     ,   a_       : List[str]     ,   a_       : Optional[Any]     ,   a_       : Any     ,   a_       : str     ,   a_       : Union[str, Any]     ,   a_       : Tuple )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  RemBertForMaskedLM(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    int    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size) )




 def   a      ( self       : str     ,   a_       : Optional[Any]     ,   a_       : List[str]     ,   a_       : List[str]     ,   a_       : int     ,   a_       : int     ,   a_       : Dict     ,   a_       : int     ,   a_       : List[str]     ,   a_       : Tuple     ,   )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  True
      UpperCAmelCase_						:    Any    					=  True
      UpperCAmelCase_						:    Dict    					=  RemBertForCausalLM(config=a_ )
      model.to(a_ )
      model.eval()

      # first forward pass
      UpperCAmelCase_						:    List[Any]    					=  model(
          a_     ,   attention_mask=a_     ,   encoder_hidden_states=a_     ,   encoder_attention_mask=a_     ,   use_cache=a_     ,   )
      UpperCAmelCase_						:    Tuple    					=  outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      UpperCAmelCase_						:    Optional[Any]    					=  ids_tensor((self.batch_size, 3)     ,   config.vocab_size )
      UpperCAmelCase_						:    List[Any]    					=  ids_tensor((self.batch_size, 3)     ,   vocab_size=2 )

      # append to next input_ids and
      UpperCAmelCase_						:    Tuple    					=  torch.cat([input_ids, next_tokens]     ,   dim=-1 )
      UpperCAmelCase_						:    str    					=  torch.cat([input_mask, next_mask]     ,   dim=-1 )

      UpperCAmelCase_						:    str    					=  model(
          a_     ,   attention_mask=a_     ,   encoder_hidden_states=a_     ,   encoder_attention_mask=a_     ,   output_hidden_states=a_     ,   )["""hidden_states"""][0]
      UpperCAmelCase_						:    int    					=  model(
          a_     ,   attention_mask=a_     ,   encoder_hidden_states=a_     ,   encoder_attention_mask=a_     ,   past_key_values=a_     ,   output_hidden_states=a_     ,   )["""hidden_states"""][0]

      # select random slice
      UpperCAmelCase_						:    Tuple    					=  ids_tensor((1,)     ,   output_from_past.shape[-1] ).item()
      UpperCAmelCase_						:    str    					=  output_from_no_past[:, -3:, random_slice_idx].detach()
      UpperCAmelCase_						:    int    					=  output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(a_     ,   a_     ,   atol=1E-3 ) )




 def   a      ( self       : Tuple     ,   a_       : str     ,   a_       : Any     ,   a_       : Tuple     ,   a_       : Dict     ,   a_       : Any     ,   a_       : Any     ,   a_       : Optional[Any] )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  RemBertForQuestionAnswering(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    List[Any]    					=  model(
          a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   start_positions=a_     ,   end_positions=a_     ,   )
      self.parent.assertEqual(result.start_logits.shape     ,   (self.batch_size, self.seq_length) )
      self.parent.assertEqual(result.end_logits.shape     ,   (self.batch_size, self.seq_length) )




 def   a      ( self       : Dict     ,   a_       : Dict     ,   a_       : Dict     ,   a_       : Optional[Any]     ,   a_       : Optional[Any]     ,   a_       : List[str]     ,   a_       : Union[str, Any]     ,   a_       : Tuple )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.num_labels
      UpperCAmelCase_						:    List[str]    					=  RemBertForSequenceClassification(a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    Dict    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels) )




 def   a      ( self       : Any     ,   a_       : Any     ,   a_       : Optional[Any]     ,   a_       : Dict     ,   a_       : List[Any]     ,   a_       : Optional[Any]     ,   a_       : Any     ,   a_       : Union[str, Any] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.num_labels
      UpperCAmelCase_						:    Optional[int]    					=  RemBertForTokenClassification(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    Union[str, Any]    					=  model(a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels) )




 def   a      ( self       : Any     ,   a_       : Optional[Any]     ,   a_       : Optional[int]     ,   a_       : List[str]     ,   a_       : Tuple     ,   a_       : int     ,   a_       : List[Any]     ,   a_       : Any )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  self.num_choices
      UpperCAmelCase_						:    int    					=  RemBertForMultipleChoice(config=a_ )
      model.to(a_ )
      model.eval()
      UpperCAmelCase_						:    Dict    					=  input_ids.unsqueeze(1 ).expand(-1     ,   self.num_choices     ,   -1 ).contiguous()
      UpperCAmelCase_						:    Optional[int]    					=  token_type_ids.unsqueeze(1 ).expand(-1     ,   self.num_choices     ,   -1 ).contiguous()
      UpperCAmelCase_						:    Optional[int]    					=  input_mask.unsqueeze(1 ).expand(-1     ,   self.num_choices     ,   -1 ).contiguous()
      UpperCAmelCase_						:    str    					=  model(
          a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_     ,   )
      self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_choices) )






 def   a      ( self       : int )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,
      )						:    Tuple    					=  config_and_inputs
      UpperCAmelCase_						:    Dict    					=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_  , unittest.TestCase						):
 """simple docstring"""




 UpperCamelCase_						:     Optional[Any]				   =    (
     (
         RemBertModel,
         RemBertForMaskedLM,
         RemBertForCausalLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 UpperCamelCase_						:     Optional[int]				   =    (RemBertForCausalLM,) if is_torch_available() else ()
 UpperCamelCase_						:     Dict				   =    (
     {
         """feature-extraction""": RemBertModel,
         """fill-mask""": RemBertForMaskedLM,
         """question-answering""": RemBertForQuestionAnswering,
         """text-classification""": RemBertForSequenceClassification,
         """text-generation""": RemBertForCausalLM,
         """token-classification""": RemBertForTokenClassification,
         """zero-shot""": RemBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )




 def   a      ( self       : List[Any] )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  RemBertModelTester(self )
      UpperCAmelCase_						:    Dict    					=  ConfigTester(self     ,   config_class=a_     ,   hidden_size=37 )




 def   a      ( self       : Union[str, Any] )->					Any:



      """simple docstring"""

      self.config_tester.run_common_tests()




 def   a      ( self       : Any )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a_ )




 def   a      ( self       : Tuple )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCAmelCase_						:    int    					=  type
           self.model_tester.create_and_check_model(*a_ )




 def   a      ( self       : Optional[int] )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*a_ )




 def   a      ( self       : Tuple )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*a_ )




 def   a      ( self       : Dict )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*a_ )




 def   a      ( self       : Optional[int] )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*a_ )




 def   a      ( self       : List[str] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*a_ )




 def   a      ( self       : List[Any] )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*a_ )




 def   a      ( self       : List[Any] )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*a_ )




 def   a      ( self       : Any )->					List[Any]:



      """simple docstring"""

      (
          (
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,(
          UpperCAmelCase_
      )					,
      )						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs_for_decoder()

      UpperCAmelCase_						:    Union[str, Any]    					=  None

      self.model_tester.create_and_check_model_as_decoder(
          a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   )






 @slow
 def   a      ( self       : Dict )->					Union[str, Any]:



      """simple docstring"""

      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_						:    Optional[int]    					=  RemBertModel.from_pretrained(a_ )
           self.assertIsNotNone(a_ )



@require_torch
class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 @slow
 def   a      ( self       : Optional[Any] )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[int]    					=  RemBertModel.from_pretrained("""google/rembert""" )
      UpperCAmelCase_						:    int    					=  torch.tensor([[3_12, 5_64_98, 3_13, 21_25, 3_13]] )
      UpperCAmelCase_						:    Tuple    					=  torch.tensor([[0, 0, 0, 1, 1]] )
      with torch.no_grad():
           UpperCAmelCase_						:    int    					=  model(a_     ,   token_type_ids=a_     ,   output_hidden_states=a_ )

      UpperCAmelCase_						:    Tuple    					=  11_52

      UpperCAmelCase_						:    Dict    					=  torch.Size((1, 5, hidden_size) )
      self.assertEqual(output["""last_hidden_state"""].shape     ,   a_ )

      UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor(
          [
              [
                  [0.0754, -0.2022, 0.1904],
                  [-0.3354, -0.3692, -0.4791],
                  [-0.2314, -0.6729, -0.0749],
                  [-0.0396, -0.3105, -0.4234],
                  [-0.1571, -0.0525, 0.5353],
              ]
          ] )

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output["""last_hidden_state"""][:, :, :3]     ,   a_     ,   atol=1E-4 ) )



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

