

from __future__ import annotations

import queue






class       __A :






    def __init__(		self	:			Union[str, Any]			, UpperCAmelCase_	:			Any			):
        lowerCAmelCase :	Optional[Any]				=					data
        lowerCAmelCase :	int				=					None
        lowerCAmelCase :	Optional[int]				=					None



def 						SCREAMING_SNAKE_CASE__ (							)    ->       List[Any]:



    '''simple docstring'''






    print('\n********Press N to stop entering at any point of time********\n' )
    lowerCAmelCase :	Union[str, Any]				=					input('Enter the value of the root node: ' ).strip().lower()
    lowerCAmelCase :	Tuple				=					queue.Queue()
    lowerCAmelCase :	Optional[int]				=					TreeNode(int(__lowerCAmelCase ) )
    q.put(__lowerCAmelCase )
    while not q.empty():
        lowerCAmelCase :	Optional[Any]				=					q.get()
        lowerCAmelCase :	Any				=					f"Enter the left node of {node_found.data}: "
        lowerCAmelCase :	Union[str, Any]				=					input(__lowerCAmelCase ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        lowerCAmelCase :	List[Any]				=					TreeNode(int(__lowerCAmelCase ) )
        lowerCAmelCase :	List[str]				=					left_node
        q.put(__lowerCAmelCase )
        lowerCAmelCase :	List[Any]				=					f"Enter the right node of {node_found.data}: "
        lowerCAmelCase :	Union[str, Any]				=					input(__lowerCAmelCase ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        lowerCAmelCase :	str				=					TreeNode(int(__lowerCAmelCase ) )
        lowerCAmelCase :	List[str]				=					right_node
        q.put(__lowerCAmelCase )
    raise
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Tuple:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    print(node.data,					end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       int:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    in_order(node.left )
    print(node.data,					end=',' )
    in_order(node.right )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Any:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data,					end=',' )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Tuple:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    lowerCAmelCase :	Tuple				=					queue.Queue()
    q.put(__lowerCAmelCase )
    while not q.empty():
        lowerCAmelCase :	Dict				=					q.get()
        print(node_dequeued.data,					end=',' )
        if node_dequeued.left:
            q.put(node_dequeued.left )
        if node_dequeued.right:
            q.put(node_dequeued.right )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       int:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    lowerCAmelCase :	Union[str, Any]				=					queue.Queue()
    q.put(__lowerCAmelCase )
    while not q.empty():
        lowerCAmelCase :	List[Any]				=					[]
        while not q.empty():
            lowerCAmelCase :	List[Any]				=					q.get()
            print(node_dequeued.data,					end=',' )
            if node_dequeued.left:
                list_.append(node_dequeued.left )
            if node_dequeued.right:
                list_.append(node_dequeued.right )
        print()
        for node in list_:
            q.put(__lowerCAmelCase )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       List[Any]:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    lowerCAmelCase :	List[str]				=					[]
    lowerCAmelCase :	Tuple				=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data,					end=',' )
            stack.append(__lowerCAmelCase )
            lowerCAmelCase :	Any				=					n.left
        # end of while means current node doesn't have left child
        lowerCAmelCase :	Optional[int]				=					stack.pop()
        # start to traverse its right child
        lowerCAmelCase :	Union[str, Any]				=					n.right
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Tuple:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    lowerCAmelCase :	Any				=					[]
    lowerCAmelCase :	int				=					node
    while n or stack:
        while n:
            stack.append(__lowerCAmelCase )
            lowerCAmelCase :	Tuple				=					n.left
        lowerCAmelCase :	List[Any]				=					stack.pop()
        print(n.data,					end=',' )
        lowerCAmelCase :	int				=					n.right
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[int]:



    '''simple docstring'''






    if not isinstance(__lowerCAmelCase,					__lowerCAmelCase ) or not node:
        return
    lowerCAmelCase	,     lowerCAmelCase :	Optional[int]				=					[], []
    lowerCAmelCase :	List[Any]				=					node
    stacka.append(__lowerCAmelCase )
    while stacka:  # to find the reversed order of post order, store it in stack2
        lowerCAmelCase :	List[Any]				=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(__lowerCAmelCase )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data,					end=',' )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase = "",					_UpperCAmelCase=50,					_UpperCAmelCase="*" )    ->       int:



    '''simple docstring'''






    if not s:
        return "\n" + width * char
    lowerCAmelCase	,     lowerCAmelCase :	Optional[Any]				=					divmod(width - len(__lowerCAmelCase ) - 2,					2 )
    return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt('''Binary Tree Traversals'''))

 __A      :   Optional[Any]          =	build_tree()
 print(prompt('''Pre Order Traversal'''))
 pre_order(node)
 print(prompt() + '''\n''')

 print(prompt('''In Order Traversal'''))
 in_order(node)
 print(prompt() + '''\n''')

 print(prompt('''Post Order Traversal'''))
 post_order(node)
 print(prompt() + '''\n''')

 print(prompt('''Level Order Traversal'''))
 level_order(node)
 print(prompt() + '''\n''')

 print(prompt('''Actual Level Order Traversal'''))
 level_order_actual(node)
 print('''*''' * 50 + '''\n''')

 print(prompt('''Pre Order Traversal - Iteration Version'''))
 pre_order_iter(node)
 print(prompt() + '''\n''')

 print(prompt('''In Order Traversal - Iteration Version'''))
 in_order_iter(node)
 print(prompt() + '''\n''')

 print(prompt('''Post Order Traversal - Iteration Version'''))
 post_order_iter(node)
 print(prompt())







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["DistilBertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "DistilBertForMaskedLM",
			    "DistilBertForMultipleChoice",
			    "DistilBertForQuestionAnswering",
			    "DistilBertForSequenceClassification",
			    "DistilBertForTokenClassification",
			    "DistilBertModel",
			    "DistilBertPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFDistilBertForMaskedLM",
			    "TFDistilBertForMultipleChoice",
			    "TFDistilBertForQuestionAnswering",
			    "TFDistilBertForSequenceClassification",
			    "TFDistilBertForTokenClassification",
			    "TFDistilBertMainLayer",
			    "TFDistilBertModel",
			    "TFDistilBertPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxDistilBertForMaskedLM",
			    "FlaxDistilBertForMultipleChoice",
			    "FlaxDistilBertForQuestionAnswering",
			    "FlaxDistilBertForSequenceClassification",
			    "FlaxDistilBertForTokenClassification",
			    "FlaxDistilBertModel",
			    "FlaxDistilBertPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_distilbert import (
			    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DistilBertConfig,
			    DistilBertOnnxConfig,
			)
			from .tokenization_distilbert import DistilBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_distilbert_fast import DistilBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_distilbert import (
						    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DistilBertForMaskedLM,
						    DistilBertForMultipleChoice,
						    DistilBertForQuestionAnswering,
						    DistilBertForSequenceClassification,
						    DistilBertForTokenClassification,
						    DistilBertModel,
						    DistilBertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_distilbert import (
						    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDistilBertForMaskedLM,
						    TFDistilBertForMultipleChoice,
						    TFDistilBertForQuestionAnswering,
						    TFDistilBertForSequenceClassification,
						    TFDistilBertForTokenClassification,
						    TFDistilBertMainLayer,
						    TFDistilBertModel,
						    TFDistilBertPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						    FlaxDistilBertPreTrainedModel,
						)

else:
			import sys

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



