




'''simple docstring'''




from __future__ import annotations

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	List[str]						,		_UpperCAmelCase    :	int = 0 ):






   """simple docstring"""






   UpperCAmelCase__										=    value
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Node | None       ,						SCREAMING_SNAKE_CASE__			:      Node | None       ):
 '''simple docstring'''





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

 UpperCAmelCase__										=    treea.value + treea.value
 UpperCAmelCase__										=    merge_two_binary_trees(treea.left       ,						treea.left       )
 UpperCAmelCase__										=    merge_two_binary_trees(treea.right       ,						treea.right       )
 return treea

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Node | None       ):
 '''simple docstring'''





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


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

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

    print('Tree1 is: ')
    print_preorder(treea)
    print('Tree2 is: ')
    print_preorder(treea)
    UpperCAmelCase_       				=      merge_two_binary_trees(treea, treea)
    print('Merged Tree is: ')
    print_preorder(merged_tree)






'''simple docstring'''




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
