






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
							import tensorflow as tf




class 							__A	(     TensorFormatter[Mapping, """tf.Tensor""", Mapping]					):





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__=None				,    **lowerCamelCase__       ):




									"""simple docstring"""

									super().__init__(features=lowerCamelCase__       )
									__UpperCamelCase :   Tuple									=tf_tensor_kwargs
									import tensorflow as tf  # noqa: import tf at initialization


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									import tensorflow as tf

									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ) and column:
														if all(
														    isinstance(lowerCamelCase__				,    tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																			return tf.stack(lowerCamelCase__       )
														elif all(
														    isinstance(lowerCamelCase__				,    (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
														    for x in column       ):
																			# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																			return tf.ragged.stack(lowerCamelCase__       )

									return column


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									import tensorflow as tf

									if value is None:
														return value

									__UpperCamelCase :   List[str]									={}

									if isinstance(lowerCamelCase__				,    (np.number, np.ndarray)       ) and np.issubdtype(value.dtype				,    np.integer       ):
														__UpperCamelCase :   str									={'dtype': tf.intaa}
									elif isinstance(lowerCamelCase__				,    (np.number, np.ndarray)       ) and np.issubdtype(value.dtype				,    np.floating       ):
														__UpperCamelCase :   Optional[int]									={'dtype': tf.floataa}
									elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(lowerCamelCase__				,    PIL.Image.Image       ):
																			__UpperCamelCase :   Any									=np.asarray(lowerCamelCase__       )

									return tf.convert_to_tensor(lowerCamelCase__				,    **{**default_dtype, **self.tf_tensor_kwargs}       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									import tensorflow as tf

									# support for torch, tf, jax etc.
									if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(lowerCamelCase__				,    torch.Tensor       ):
																			return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
									if hasattr(lowerCamelCase__				,    '__array__'       ) and not isinstance(lowerCamelCase__				,    tf.Tensor       ):
														__UpperCamelCase :   str									=data_struct.__array__()
									# support for nested types like struct of list of struct
									if isinstance(lowerCamelCase__				,    np.ndarray       ):
														if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																			return self._consolidate([self.recursive_tensorize(lowerCamelCase__       ) for substruct in data_struct]       )
									elif isinstance(lowerCamelCase__				,    (list, tuple)       ):
														return self._consolidate([self.recursive_tensorize(lowerCamelCase__       ) for substruct in data_struct]       )
									return self._tensorize(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									return map_nested(self._recursive_tensorize				,    lowerCamelCase__				,    map_list=lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=self.numpy_arrow_extractor().extract_row(lowerCamelCase__       )
									__UpperCamelCase :   Tuple									=self.python_features_decoder.decode_row(lowerCamelCase__       )
									return self.recursive_tensorize(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=self.numpy_arrow_extractor().extract_column(lowerCamelCase__       )
									__UpperCamelCase :   Optional[Any]									=self.python_features_decoder.decode_column(lowerCamelCase__				,    pa_table.column_names[0]       )
									__UpperCamelCase :   Any									=self.recursive_tensorize(lowerCamelCase__       )
									__UpperCamelCase :   Optional[int]									=self._consolidate(lowerCamelCase__       )
									return column







				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.numpy_arrow_extractor().extract_batch(lowerCamelCase__       )
									__UpperCamelCase :   int									=self.python_features_decoder.decode_batch(lowerCamelCase__       )
									__UpperCamelCase :   int									=self.recursive_tensorize(lowerCamelCase__       )
									for column_name in batch:
														__UpperCamelCase :   List[Any]									=self._consolidate(batch[column_name]       )
									return batch









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

