


"""simple docstring"""
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     LayoutLMvaForQuestionAnswering,
     LayoutLMvaForSequenceClassification,
     LayoutLMvaForTokenClassification,
     LayoutLMvaModel,
 )
 from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=36      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=16      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=1000      ,   )  ->  Tuple:
      _lowerCamelCase	:					Dict   =		parent
      _lowerCamelCase	:					Union[str, Any]   =		batch_size
      _lowerCamelCase	:					Dict   =		num_channels
      _lowerCamelCase	:					int   =		image_size
      _lowerCamelCase	:					Optional[Any]   =		patch_size
      _lowerCamelCase	:					str   =		text_seq_length
      _lowerCamelCase	:					int   =		is_training
      _lowerCamelCase	:					Optional[Any]   =		use_input_mask
      _lowerCamelCase	:					List[str]   =		use_token_type_ids
      _lowerCamelCase	:					Dict   =		use_labels
      _lowerCamelCase	:					Dict   =		vocab_size
      _lowerCamelCase	:					Optional[int]   =		hidden_size
      _lowerCamelCase	:					List[Any]   =		num_hidden_layers
      _lowerCamelCase	:					Optional[int]   =		num_attention_heads
      _lowerCamelCase	:					List[Any]   =		intermediate_size
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					int   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					List[str]   =		max_position_embeddings
      _lowerCamelCase	:					Optional[int]   =		type_vocab_size
      _lowerCamelCase	:					int   =		type_sequence_label_size
      _lowerCamelCase	:					str   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		coordinate_size
      _lowerCamelCase	:					Dict   =		shape_size
      _lowerCamelCase	:					Tuple   =		num_labels
      _lowerCamelCase	:					Union[str, Any]   =		num_choices
      _lowerCamelCase	:					str   =		scope
      _lowerCamelCase	:					int   =		range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      _lowerCamelCase	:					str   =		text_seq_length
      _lowerCamelCase	:					List[Any]   =		(image_size // patch_size) ** 2 + 1
      _lowerCamelCase	:					List[Any]   =		self.text_seq_length + self.image_seq_length





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[Any]   =		ids_tensor([self.batch_size, self.text_seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Union[str, Any]   =		ids_tensor([self.batch_size, self.text_seq_length, 4]      ,   self.range_bbox)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]):
         for j in range(bbox.shape[1]):
            if bbox[i, j, 3] < bbox[i, j, 1]:
               _lowerCamelCase	:					Optional[int]   =		bbox[i, j, 3]
               _lowerCamelCase	:					Tuple   =		bbox[i, j, 1]
               _lowerCamelCase	:					Optional[Any]   =		t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               _lowerCamelCase	:					Any   =		bbox[i, j, 2]
               _lowerCamelCase	:					str   =		bbox[i, j, 0]
               _lowerCamelCase	:					Dict   =		t

      _lowerCamelCase	:					Optional[Any]   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowerCamelCase	:					Any   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Tuple   =		random_attention_mask([self.batch_size, self.text_seq_length])

      _lowerCamelCase	:					str   =		None
      if self.use_token_type_ids:
         _lowerCamelCase	:					List[str]   =		ids_tensor([self.batch_size, self.text_seq_length]      ,   self.type_vocab_size)

      _lowerCamelCase	:					Tuple   =		None
      _lowerCamelCase	:					Optional[int]   =		None
      if self.use_labels:
         _lowerCamelCase	:					Optional[int]   =		ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)
         _lowerCamelCase	:					List[str]   =		ids_tensor([self.batch_size, self.text_seq_length]      ,   self.num_labels)

      _lowerCamelCase	:					Optional[int]   =		LayoutLMvaConfig(
          vocab_size=self.vocab_size      ,   hidden_size=self.hidden_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      ,   initializer_range=self.initializer_range      ,   coordinate_size=self.coordinate_size      ,   shape_size=self.shape_size      ,   input_size=self.image_size      ,   patch_size=self.patch_size      ,   )

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					Tuple   =		LayoutLMvaModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      # text + image
      _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		model(SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE)

      self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))

      # text only
      _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (self.batch_size, self.text_seq_length, self.hidden_size))

      # image only
      _lowerCamelCase	:					List[str]   =		model(pixel_values=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,   (self.batch_size, self.image_seq_length, self.hidden_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					int   =		self.num_labels
      _lowerCamelCase	:					List[str]   =		LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Tuple   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE      ,   )
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		self.num_labels
      _lowerCamelCase	:					int   =		LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Dict   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE      ,   )
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.text_seq_length, self.num_labels))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Optional[Any]   =		model(
          SCREAMING_SNAKE_CASE      ,   bbox=SCREAMING_SNAKE_CASE      ,   pixel_values=SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   start_positions=SCREAMING_SNAKE_CASE      ,   end_positions=SCREAMING_SNAKE_CASE      ,   )
      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 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_config_and_inputs()
      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					Any   =		config_and_inputs
      _lowerCamelCase	:					Optional[int]   =		{
          """input_ids""": input_ids,
          """bbox""": bbox,
          """pixel_values""": pixel_values,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False

   __UpperCAmelCase							       =					(
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase							       =					(
       {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
       if is_torch_available()
       else {}
   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
      # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
      # the sequence dimension of the text embedding only.
      # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
      return True





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		LayoutLMvaModelTester(self)
      _lowerCamelCase	:					Optional[Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   hidden_size=37)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  Optional[Any]:
      _lowerCamelCase	:					List[str]   =		copy.deepcopy(SCREAMING_SNAKE_CASE)
      if model_class in get_values(SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Union[str, Any]   =		{
             k: v.unsqueeze(1).expand(-1      ,   self.model_tester.num_choices      ,   -1).contiguous()
             if isinstance(SCREAMING_SNAKE_CASE      ,   torch.Tensor) and v.ndim > 1
             else v
             for k, v in inputs_dict.items()
         }
      if return_labels:
         if model_class in get_values(SCREAMING_SNAKE_CASE):
            _lowerCamelCase	:					Union[str, Any]   =		torch.ones(self.model_tester.batch_size      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE)
         elif model_class in get_values(SCREAMING_SNAKE_CASE):
            _lowerCamelCase	:					Dict   =		torch.zeros(
                self.model_tester.batch_size      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					Dict   =		torch.zeros(
                self.model_tester.batch_size      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE)
         elif model_class in [
             *get_values(SCREAMING_SNAKE_CASE),
         ]:
            _lowerCamelCase	:					List[str]   =		torch.zeros(
                self.model_tester.batch_size      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE)
         elif model_class in [
             *get_values(SCREAMING_SNAKE_CASE),
         ]:
            _lowerCamelCase	:					Optional[int]   =		torch.zeros(
                (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE      ,   )

      return inputs_dict





   def 		UpperCamelCase_  (							self)  ->  str:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCamelCase	:					Dict   =		type
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Any   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Any   =		LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)







def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image





@require_torch
class 	lowercase__				(					unittest.TestCase				):





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  int:
      return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE) if is_vision_available() else None





   @slow
   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					str   =		LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		self.default_image_processor
      _lowerCamelCase	:					List[Any]   =		prepare_img()
      _lowerCamelCase	:					List[str]   =		image_processor(images=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		torch.tensor([[1, 2]])
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

      # forward pass
      _lowerCamelCase	:					Tuple   =		model(
          input_ids=input_ids.to(SCREAMING_SNAKE_CASE)      ,   bbox=bbox.to(SCREAMING_SNAKE_CASE)      ,   pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE)      ,   )

      # verify the logits
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size((1, 199, 768))
      self.assertEqual(outputs.last_hidden_state.shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		torch.tensor(
          [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]).to(SCREAMING_SNAKE_CASE)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))





"""simple docstring"""
from __future__ import annotations

import queue




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		data
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					Any   =		None







def 				_snake_case      (   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   print(node.data						, end=""","""   )
   pre_order(node.left   )
   pre_order(node.right   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   in_order(node.left   )
   print(node.data						, end=""","""   )
   in_order(node.right   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   post_order(node.left   )
   post_order(node.right   )
   print(node.data						, end=""","""   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Any   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Optional[Any]   =		[]
      while not q.empty():
         _lowerCamelCase	:					Dict   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					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   )
         _lowerCamelCase	:					Tuple   =		n.left
      # end of while means current node doesn't have left child
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      # start to traverse its right child
      _lowerCamelCase	:					Dict   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					int   =		node
   while n or stack:
      while n:
         stack.append(__snake_case   )
         _lowerCamelCase	:					Any   =		n.left
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      print(n.data						, end=""","""   )
      _lowerCamelCase	:					List[Any]   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		[], []
   _lowerCamelCase	:					Optional[Any]   =		node
   stacka.append(__snake_case   )
   while stacka:  # to find the reversed order of post order, store it in stack2
      _lowerCamelCase	:					Union[str, Any]   =		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 				_snake_case      (   __snake_case							:      str = ""						, __snake_case							:      Any=50						, __snake_case							:      List[str]="*"   ):




   """simple docstring"""







   if not s:
      return "\n" + width * char
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		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())

