


"""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 						_A  :







    """simple docstring"""




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

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






    def      __snake_case							( self    :   int):
      a						:		Any											=      ids_tensor([self.batch_size, self.text_seq_length]    ,			self.vocab_size)

      a						:		Dict											=      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]:
            a						:		str											=      bbox[i, j, 3]
            a						:		Tuple											=      bbox[i, j, 1]
            a						:		Union[str, Any]											=      t
          if bbox[i, j, 2] < bbox[i, j, 0]:
            a						:		Tuple											=      bbox[i, j, 2]
            a						:		Tuple											=      bbox[i, j, 0]
            a						:		Tuple											=      t

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

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

      a						:		Any											=      None
      if self.use_token_type_ids:
        a						:		Tuple											=      ids_tensor([self.batch_size, self.text_seq_length]    ,			self.type_vocab_size)

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

      a						:		Any											=      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      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Union[str, Any]											=      LayoutLMvaModel(config=snake_case__)
      model.to(snake_case__)
      model.eval()

      # text + image
      a						:		Optional[int]											=      model(snake_case__    ,			pixel_values=snake_case__)
      a						:		str											=      model(
          snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__)
      a						:		Optional[Any]											=      model(snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__    ,			token_type_ids=snake_case__)
      a						:		str											=      model(snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__)

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

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

      # image only
      a						:		Optional[int]											=      model(pixel_values=snake_case__)
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,			(self.batch_size, self.image_seq_length, self.hidden_size))






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Any											=      self.num_labels
      a						:		List[str]											=      LayoutLMvaForSequenceClassification(snake_case__)
      model.to(snake_case__)
      model.eval()
      a						:		str											=      model(
          snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__    ,			)
      self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))






    def      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   str):
      a						:		Dict											=      self.num_labels
      a						:		Union[str, Any]											=      LayoutLMvaForTokenClassification(config=snake_case__)
      model.to(snake_case__)
      model.eval()
      a						:		Any											=      model(
          snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__    ,			)
      self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.text_seq_length, self.num_labels))






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]):
      a						:		Dict											=      LayoutLMvaForQuestionAnswering(config=snake_case__)
      model.to(snake_case__)
      model.eval()
      a						:		Optional[int]											=      model(
          snake_case__    ,			bbox=snake_case__    ,			pixel_values=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			start_positions=snake_case__    ,			end_positions=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      __snake_case							( self    :   Any):
      a						:		List[str]											=      self.prepare_config_and_inputs()
      (
          a 
      )						:		Any											=      config_and_inputs
      a						:		Tuple											=      {
          "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 						_A  (  A_   ,A_   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	False
    UpperCAmelCase	:							Any     =	False
    UpperCAmelCase	:							List[Any]     =	False

    UpperCAmelCase	:							int     =	(
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase	:							Dict     =	(
        {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Dict):
      return True






    def      __snake_case							( self    :   int):
      a						:		Union[str, Any]											=      LayoutLMvaModelTester(self)
      a						:		List[Any]											=      ConfigTester(self    ,			config_class=snake_case__    ,			hidden_size=37)






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   int=False):
      a						:		Dict											=      copy.deepcopy(snake_case__)
      if model_class in get_values(snake_case__):
        a						:		int											=      {
            k: v.unsqueeze(1).expand(-1    ,			self.model_tester.num_choices    ,			-1).contiguous()
            if isinstance(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(snake_case__):
          a						:		List[str]											=      torch.ones(self.model_tester.batch_size    ,			dtype=torch.long    ,			device=snake_case__)
        elif model_class in get_values(snake_case__):
          a						:		Tuple											=      torch.zeros(
              self.model_tester.batch_size    ,			dtype=torch.long    ,			device=snake_case__)
          a						:		Tuple											=      torch.zeros(
              self.model_tester.batch_size    ,			dtype=torch.long    ,			device=snake_case__)
        elif model_class in [
            *get_values(snake_case__),
        ]:
          a						:		Tuple											=      torch.zeros(
              self.model_tester.batch_size    ,			dtype=torch.long    ,			device=snake_case__)
        elif model_class in [
            *get_values(snake_case__),
        ]:
          a						:		Dict											=      torch.zeros(
              (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,			dtype=torch.long    ,			device=snake_case__    ,			)

      return inputs_dict






    def      __snake_case							( self    :   Dict):
      self.config_tester.run_common_tests()






    def      __snake_case							( self    :   Tuple):
      a						:		Optional[Any]											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case__)






    def      __snake_case							( self    :   Any):
      a						:		List[Any]											=      self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        a						:		Dict											=      type
        self.model_tester.create_and_check_model(*snake_case__)






    def      __snake_case							( self    :   Optional[int]):
      a						:		Any											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case__)






    def      __snake_case							( self    :   Optional[int]):
      a						:		Any											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case__)






    def      __snake_case							( self    :   Union[str, Any]):
      a						:		Tuple											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case__)






    @slow
    def      __snake_case							( self    :   str):


      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						:		Optional[Any]											=      LayoutLMvaModel.from_pretrained(snake_case__)
        self.assertIsNotNone(snake_case__)






def 					lowercase      (    )->				int:
  '''simple docstring'''







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







@require_torch
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @cached_property
    def      __snake_case							( self    :   Tuple):
      return LayoutLMvaImageProcessor(apply_ocr=snake_case__) if is_vision_available() else None






    @slow
    def      __snake_case							( self    :   Dict):
      a						:		str											=      LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base").to(snake_case__)

      a						:		List[str]											=      self.default_image_processor
      a						:		Tuple											=      prepare_img()
      a						:		List[Any]											=      image_processor(images=snake_case__    ,			return_tensors="pt").pixel_values.to(snake_case__)

      a						:		List[Any]											=      torch.tensor([[1, 2]])
      a						:		List[str]											=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

      # forward pass
      a						:		Union[str, Any]											=      model(
          input_ids=input_ids.to(snake_case__)    ,			bbox=bbox.to(snake_case__)    ,			pixel_values=pixel_values.to(snake_case__)    ,			)

      # verify the logits
      a						:		Dict											=      torch.Size((1, 199, 768))
      self.assertEqual(outputs.last_hidden_state.shape    ,			snake_case__)

      a						:		str											=      torch.tensor(
          [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]).to(snake_case__)

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










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      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 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		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(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		int											=      node
  while n or stack:
    while n:  # start from root node, find its left child
      print(n.data  ,       end=","	)
      stack.append(A_	)
      a						:		Tuple											=      n.left
    # end of while means current node doesn't have left child
    a						:		Optional[Any]											=      stack.pop()
    # start to traverse its right child
    a						:		str											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a							,		a						:		Tuple											=      [], []
  a						:		Tuple											=      node
  stacka.append(A_	)
  while stacka:  # to find the reversed order of post order, store it in stack2
    a						:		Optional[Any]											=      stacka.pop()
    if n.left:
      stacka.append(n.left	)
    if n.right:
      stacka.append(n.right	)
    stacka.append(A_	)
  while stacka:  # pop up from stack2 will be the post order
    print(stacka.pop().data  ,       end=","	)


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

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

 __lowercase    =				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())






