
'''simple docstring'''







import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							int    					=   None
  __UpperCAmelCase							:							List[str]    					=   None


  @property
  def     __lowercase						(						self :	Dict			):


     '''simple docstring'''






     return self.feat_extract_tester.prepare_feat_extract_dict()


  def     __lowercase						(						self :	Dict			):


     '''simple docstring'''






     _a							:      int     =  self.feature_extraction_class(**self.feat_extract_dict			)
     self.assertTrue(hasattr(UpperCamelCase_				,'feature_size'			)			)
     self.assertTrue(hasattr(UpperCamelCase_				,'sampling_rate'			)			)
     self.assertTrue(hasattr(UpperCamelCase_				,'padding_value'			)			)


  def     __lowercase						(						self :	Dict			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  self.feat_extract_tester.prepare_inputs_for_common()
     _a							:      str     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      List[str]     =  feat_extract.model_input_names[0]

     _a							:      Dict     =  BatchFeature({input_name: speech_inputs}			)

     self.assertTrue(all(len(UpperCamelCase_			) == len(UpperCamelCase_			) for x, y in zip(UpperCamelCase_				,processed_features[input_name]			)			)			)

     _a							:      List[Any]     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
     _a							:      Optional[Any]     =  BatchFeature({input_name: speech_inputs}				,tensor_type='np'			)

     _a							:      Optional[Any]     =  processed_features[input_name]

     if len(batch_features_input.shape			) < 3:
        _a							:      Dict     =  batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)


  @require_torch
  def     __lowercase						(						self :	Optional[Any]			):


     '''simple docstring'''






     _a							:      Any     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
     _a							:      str     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      List[Any]     =  feat_extract.model_input_names[0]

     _a							:      Union[str, Any]     =  BatchFeature({input_name: speech_inputs}				,tensor_type='pt'			)

     _a							:      Dict     =  processed_features[input_name]

     if len(batch_features_input.shape			) < 3:
        _a							:      Optional[Any]     =  batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)


  @require_tf
  def     __lowercase						(						self :	Tuple			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
     _a							:      Optional[int]     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      Tuple     =  feat_extract.model_input_names[0]

     _a							:      Any     =  BatchFeature({input_name: speech_inputs}				,tensor_type='tf'			)

     _a							:      Any     =  processed_features[input_name]

     if len(batch_features_input.shape			) < 3:
        _a							:      Dict     =  batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)


  def     __lowercase						(						self :	Optional[Any]				,_a :	Tuple=False			):


     '''simple docstring'''






     def _inputs_have_equal_length(_a :	Any			):
        _a							:      List[Any]     =  len(input[0]			)
        for input_slice in input[1:]:
           if len(UpperCamelCase_			) != length:
              return False
        return True

     def _inputs_are_equal(_a :	Optional[Any]				,_a :	Tuple			):
        if len(UpperCamelCase_			) != len(UpperCamelCase_			):
           return False

        for input_slice_a, input_slice_a in zip(UpperCamelCase_				,UpperCamelCase_			):
           if not np.allclose(np.asarray(UpperCamelCase_			)				,np.asarray(UpperCamelCase_			)				,atol=1E-3			):
              return False
        return True

     _a							:      Dict     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      Union[str, Any]     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_			)
     _a							:      int     =  feat_extract.model_input_names[0]

     _a							:      List[str]     =  BatchFeature({input_name: speech_inputs}			)

     _a							:      int     =  self.feat_extract_tester.seq_length_diff
     _a							:      Union[str, Any]     =  self.feat_extract_tester.max_seq_length + pad_diff
     _a							:      int     =  self.feat_extract_tester.min_seq_length
     _a							:      Tuple     =  self.feat_extract_tester.batch_size
     _a							:      Dict     =  self.feat_extract_tester.feature_size

     # test padding for List[int] + numpy
     _a							:      Any     =  feat_extract.pad(UpperCamelCase_				,padding=UpperCamelCase_			)
     _a							:      List[str]     =  input_a[input_name]

     _a							:      Dict     =  feat_extract.pad(UpperCamelCase_				,padding='longest'			)
     _a							:      int     =  input_a[input_name]

     _a							:      Optional[Any]     =  feat_extract.pad(UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[-1]			)			)
     _a							:      Union[str, Any]     =  input_a[input_name]

     _a							:      List[str]     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='np'			)
     _a							:      Tuple     =  input_a[input_name]

     # max_length parameter has to be provided when setting `padding="max_length"`
     with self.assertRaises(UpperCamelCase_			):
        feat_extract.pad(UpperCamelCase_				,padding='max_length'			)[input_name]

     _a							:      Dict     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=UpperCamelCase_				,return_tensors='np'			)
     _a							:      Union[str, Any]     =  input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(_inputs_are_equal(UpperCamelCase_				,UpperCamelCase_			)			)
     self.assertTrue(len(input_a[0]			) == pad_min_length			)
     self.assertTrue(len(input_a[1]			) == pad_min_length + pad_diff			)
     self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]			))			)
     self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)			)

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size			)

     # test padding for `pad_to_multiple_of` for List[int] + numpy
     _a							:      List[Any]     =  feat_extract.pad(UpperCamelCase_				,pad_to_multiple_of=10			)
     _a							:      int     =  input_a[input_name]

     _a							:      int     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,pad_to_multiple_of=10			)
     _a							:      Tuple     =  input_a[input_name]

     _a							:      Any     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,pad_to_multiple_of=10				,max_length=UpperCamelCase_			)
     _a							:      List[str]     =  input_a[input_name]

     _a							:      Union[str, Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,pad_to_multiple_of=10				,max_length=UpperCamelCase_				,return_tensors='np'				,)
     _a							:      Optional[Any]     =  input_a[input_name]

     self.assertTrue(all(len(UpperCamelCase_			) % 10 == 0 for x in input_a			)			)
     self.assertTrue(_inputs_are_equal(UpperCamelCase_				,UpperCamelCase_			)			)

     _a							:      int     =  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
     self.assertTrue(all(len(UpperCamelCase_			) == expected_mult_pad_length for x in input_a			)			)
     self.assertEqual(input_a.shape[:2]				,(batch_size, expected_mult_pad_length)			)

     if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size			)

     # Check padding value is correct
     _a							:      Union[str, Any]     =  (np.ones(self.feat_extract_tester.feature_size			) * feat_extract.padding_value).sum()
     self.assertTrue(
         abs(np.asarray(input_a[0]			)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			)
         < 1E-3			)
     self.assertTrue(
         abs(
             np.asarray(input_a[1]			)[pad_min_length + pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)			)
         < 1E-3			)
     self.assertTrue(
         abs(
             np.asarray(input_a[2]			)[pad_min_length + 2 * pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)			)
         < 1E-3			)
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			) < 1E-3			)
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)			)
         < 1E-3			)


  def     __lowercase						(						self :	Optional[int]				,_a :	Union[str, Any]=False			):


     '''simple docstring'''






     def _inputs_have_equal_length(_a :	int			):
        _a							:      int     =  len(input[0]			)
        for input_slice in input[1:]:
           if len(UpperCamelCase_			) != length:
              return False
        return True

     def _inputs_are_equal(_a :	List[str]				,_a :	int			):
        if len(UpperCamelCase_			) != len(UpperCamelCase_			):
           return False

        for input_slice_a, input_slice_a in zip(UpperCamelCase_				,UpperCamelCase_			):
           if not np.allclose(np.asarray(UpperCamelCase_			)				,np.asarray(UpperCamelCase_			)				,atol=1E-3			):
              return False
        return True

     _a							:      Any     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      str     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_			)
     _a							:      Any     =  feat_extract.model_input_names[0]

     _a							:      str     =  BatchFeature({input_name: speech_inputs}			)

     # truncate to smallest
     _a							:      List[str]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)				,truncation=UpperCamelCase_			)
     _a							:      Any     =  input_a[input_name]

     _a							:      Tuple     =  feat_extract.pad(UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)			)
     _a							:      Tuple     =  input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)

     # truncate to smallest with np
     _a							:      str     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)				,return_tensors='np'				,truncation=UpperCamelCase_				,)
     _a							:      Dict     =  input_a[input_name]

     _a							:      List[Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)				,return_tensors='np'			)
     _a							:      Tuple     =  input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(input_a.shape[1] == len(speech_inputs[0]			)			)

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)

     # truncate to middle
     _a							:      Optional[int]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[1]			)				,truncation=UpperCamelCase_				,return_tensors='np'				,)
     _a							:      List[Any]     =  input_a[input_name]

     _a							:      List[Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[1]			)				,truncation=UpperCamelCase_			)
     _a							:      List[Any]     =  input_a[input_name]

     _a							:      Optional[Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[1]			)				,return_tensors='np'			)
     _a							:      List[Any]     =  input_a[input_name]

     self.assertTrue(input_a.shape[1] == len(speech_inputs[1]			)			)
     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(_inputs_are_equal(UpperCamelCase_				,UpperCamelCase_			)			)

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(UpperCamelCase_			):
        feat_extract.pad(UpperCamelCase_				,truncation=UpperCamelCase_			)[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(UpperCamelCase_			):
        feat_extract.pad(UpperCamelCase_				,padding='longest'				,truncation=UpperCamelCase_			)[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(UpperCamelCase_			):
        feat_extract.pad(UpperCamelCase_				,padding='longest'				,truncation=UpperCamelCase_			)[input_name]

     # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
     with self.assertRaises(UpperCamelCase_			):
        feat_extract.pad(UpperCamelCase_				,padding='max_length'				,truncation=UpperCamelCase_			)[input_name]

     # test truncation for `pad_to_multiple_of` for List[int] + numpy
     _a							:      List[str]     =  12
     _a							:      Tuple     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)				,pad_to_multiple_of=UpperCamelCase_				,truncation=UpperCamelCase_				,)
     _a							:      Union[str, Any]     =  input_a[input_name]

     _a							:      Union[str, Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=len(speech_inputs[0]			)				,pad_to_multiple_of=UpperCamelCase_				,)
     _a							:      List[str]     =  input_a[input_name]

     # retrieve expected_length as multiple of pad_to_multiple_of
     _a							:      List[str]     =  len(speech_inputs[0]			)
     if expected_length % pad_to_multiple_of != 0:
        _a							:      Optional[Any]     =  ((len(speech_inputs[0]			) // pad_to_multiple_of) + 1) * pad_to_multiple_of

     self.assertTrue(len(input_a[0]			) == expected_length			)
     self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
     self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






     self._check_padding(numpify=UpperCamelCase_			)


  def     __lowercase						(						self :	Optional[int]			):


     '''simple docstring'''






     self._check_padding(numpify=UpperCamelCase_			)


  def     __lowercase						(						self :	Union[str, Any]			):


     '''simple docstring'''






     self._check_truncation(numpify=UpperCamelCase_			)


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






     self._check_truncation(numpify=UpperCamelCase_			)


  @require_torch
  def     __lowercase						(						self :	Any			):


     '''simple docstring'''






     _a							:      Dict     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      Union[str, Any]     =  self.feat_extract_tester.prepare_inputs_for_common()
     _a							:      int     =  feat_extract.model_input_names[0]

     _a							:      int     =  BatchFeature({input_name: speech_inputs}			)

     _a							:      Dict     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='np'			)[input_name]
     _a							:      List[str]     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='pt'			)[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_pt.numpy().astype(np.floataa			).sum()			) < 1E-2			)


  @require_tf
  def     __lowercase						(						self :	Union[str, Any]			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  self.feature_extraction_class(**self.feat_extract_dict			)
     _a							:      List[Any]     =  self.feat_extract_tester.prepare_inputs_for_common()
     _a							:      Any     =  feat_extract.model_input_names[0]

     _a							:      Any     =  BatchFeature({input_name: speech_inputs}			)

     _a							:      Optional[Any]     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='np'			)[input_name]
     _a							:      int     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='tf'			)[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_tf.numpy().astype(np.floataa			).sum()			) < 1E-2			)


  def     __lowercase						(						self :	Dict			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  self.feat_extract_dict
     _a							:      List[Any]     =  True
     _a							:      int     =  self.feature_extraction_class(**UpperCamelCase_			)
     _a							:      int     =  self.feat_extract_tester.prepare_inputs_for_common()
     _a							:      Tuple     =  [len(UpperCamelCase_			) for x in speech_inputs]
     _a							:      List[Any]     =  feat_extract.model_input_names[0]

     _a							:      int     =  BatchFeature({input_name: speech_inputs}			)

     _a							:      List[str]     =  feat_extract.pad(UpperCamelCase_				,padding='longest'				,return_tensors='np'			)
     self.assertIn('attention_mask'				,UpperCamelCase_			)
     self.assertListEqual(list(processed.attention_mask.shape			)				,list(processed[input_name].shape[:2]			)			)
     self.assertListEqual(processed.attention_mask.sum(-1			).tolist()				,UpperCamelCase_			)


  def     __lowercase						(						self :	List[Any]			):


     '''simple docstring'''






     _a							:      Optional[Any]     =  self.feat_extract_dict
     _a							:      int     =  True
     _a							:      Any     =  self.feature_extraction_class(**UpperCamelCase_			)
     _a							:      Dict     =  self.feat_extract_tester.prepare_inputs_for_common()
     _a							:      List[Any]     =  [len(UpperCamelCase_			) for x in speech_inputs]
     _a							:      List[str]     =  feat_extract.model_input_names[0]

     _a							:      str     =  BatchFeature({input_name: speech_inputs}			)
     _a							:      Optional[Any]     =  min(UpperCamelCase_			)

     _a							:      Union[str, Any]     =  feat_extract.pad(
         UpperCamelCase_				,padding='max_length'				,max_length=UpperCamelCase_				,truncation=UpperCamelCase_				,return_tensors='np'			)
     self.assertIn('attention_mask'				,UpperCamelCase_			)
     self.assertListEqual(
         list(processed_pad.attention_mask.shape			)				,[processed_pad[input_name].shape[0], max_length]			)
     self.assertListEqual(
         processed_pad.attention_mask[:, :max_length].sum(-1			).tolist()				,[max_length for x in speech_inputs]			)





"""simple docstring"""



from __future__ import annotations

import queue





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Optional[int]     							=					None




def 					lowerCamelCase   (     ):
    print('\n********Press N to stop entering at any point of time********\n' )
    UpperCAmelCase__	:      int     							=					input('Enter the value of the root node: ' ).strip().lower()
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    UpperCAmelCase__	:      Dict     							=					TreeNode(int(_snake_case ) )
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Tuple     							=					q.get()
        UpperCAmelCase__	:      List[Any]     							=					F'''Enter the left node of {node_found.data}: '''
        UpperCAmelCase__	:      Dict     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      Optional[int]     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Tuple     							=					left_node
        q.put(_snake_case )
        UpperCAmelCase__	:      int     							=					F'''Enter the right node of {node_found.data}: '''
        UpperCAmelCase__	:      int     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      int     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Any     							=					right_node
        q.put(_snake_case )
    raise
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    print(node.data    ,end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    in_order(node.left )
    print(node.data    ,end=',' )
    in_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data    ,end=',' )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      int     							=					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 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Optional[Any]     							=					[]
        while not q.empty():
            UpperCAmelCase__	:      Union[str, 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(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[int]     							=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data    ,end=',' )
            stack.append(_snake_case )
            UpperCAmelCase__	:      Optional[int]     							=					n.left
        # end of while means current node doesn't have left child
        UpperCAmelCase__	:      Union[str, Any]     							=					stack.pop()
        # start to traverse its right child
        UpperCAmelCase__	:      Dict     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[Any]     							=					node
    while n or stack:
        while n:
            stack.append(_snake_case )
            UpperCAmelCase__	:      int     							=					n.left
        UpperCAmelCase__	:      Tuple     							=					stack.pop()
        print(n.data    ,end=',' )
        UpperCAmelCase__	:      Tuple     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    UpperCAmelCase__	:      Union[str, Any]     							=					node
    stacka.append(_snake_case )
    while stacka:  # to find the reversed order of post order, store it in stack2
        UpperCAmelCase__	:      Tuple     							=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(_snake_case )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data    ,end=',' )
def 					lowerCamelCase   (     _snake_case = ""    ,_snake_case=50    ,_snake_case="*" ):
    if not s:
        return "\n" + width * char
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					divmod(width - len(_snake_case ) - 2    ,2 )
    return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

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

    UpperCamelCase__          =	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())

