

import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     LayoutLMForMaskedLM,
     LayoutLMForQuestionAnswering,
     LayoutLMForSequenceClassification,
     LayoutLMForTokenClassification,
     LayoutLMModel,
 )






class 	__magic_name__				:






     def __init__(							self		,	_lowercase		,	_lowercase=13		,	_lowercase=7		,	_lowercase=True		,	_lowercase=True		,	_lowercase=True		,	_lowercase=True		,	_lowercase=99		,	_lowercase=32		,	_lowercase=5		,	_lowercase=4		,	_lowercase=37		,	_lowercase="gelu"		,	_lowercase=0.1		,	_lowercase=0.1		,	_lowercase=512		,	_lowercase=16		,	_lowercase=2		,	_lowercase=0.02		,	_lowercase=3		,	_lowercase=4		,	_lowercase=None		,	_lowercase=1_000		,	)->    Optional[int]:
         UpperCamelCase_      =	parent
         UpperCamelCase_      =	batch_size
         UpperCamelCase_      =	seq_length
         UpperCamelCase_      =	is_training
         UpperCamelCase_      =	use_input_mask
         UpperCamelCase_      =	use_token_type_ids
         UpperCamelCase_      =	use_labels
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	hidden_size
         UpperCamelCase_      =	num_hidden_layers
         UpperCamelCase_      =	num_attention_heads
         UpperCamelCase_      =	intermediate_size
         UpperCamelCase_      =	hidden_act
         UpperCamelCase_      =	hidden_dropout_prob
         UpperCamelCase_      =	attention_probs_dropout_prob
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	type_vocab_size
         UpperCamelCase_      =	type_sequence_label_size
         UpperCamelCase_      =	initializer_range
         UpperCamelCase_      =	num_labels
         UpperCamelCase_      =	num_choices
         UpperCamelCase_      =	scope
         UpperCamelCase_      =	range_bbox






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size  )

         UpperCamelCase_      =	ids_tensor([self.batch_size, self.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]:
                     UpperCamelCase_      =	bbox[i, j, 3]
                     UpperCamelCase_      =	bbox[i, j, 1]
                     UpperCamelCase_      =	t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     UpperCamelCase_      =	bbox[i, j, 2]
                     UpperCamelCase_      =	bbox[i, j, 0]
                     UpperCamelCase_      =	t

         UpperCamelCase_      =	None
         if self.use_input_mask:
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2  )

         UpperCamelCase_      =	None
         if self.use_token_type_ids:
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size  )

         UpperCamelCase_      =	None
         UpperCamelCase_      =	None
         UpperCamelCase_      =	None
         if self.use_labels:
             UpperCamelCase_      =	ids_tensor([self.batch_size]		,	self.type_sequence_label_size  )
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels  )
             UpperCamelCase_      =	ids_tensor([self.batch_size]		,	self.num_choices  )

         UpperCamelCase_      =	self.get_config()

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






     def   UpperCAmelCase_							(							self  )->    str:
         return LayoutLMConfig(
             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		,	)






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    int:
         UpperCamelCase_      =	LayoutLMModel(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(_lowercase		,	_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase  )
         UpperCamelCase_      =	model(_lowercase		,	_lowercase		,	token_type_ids=_lowercase  )
         UpperCamelCase_      =	model(_lowercase		,	_lowercase  )
         self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[Any]:
         UpperCamelCase_      =	LayoutLMForMaskedLM(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(_lowercase		,	_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase  )
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[int]:
         UpperCamelCase_      =	self.num_labels
         UpperCamelCase_      =	LayoutLMForSequenceClassification(_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(
             _lowercase		,	_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase  )
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[Any]:
         UpperCamelCase_      =	self.num_labels
         UpperCamelCase_      =	LayoutLMForTokenClassification(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(_lowercase		,	_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase  )
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    int:
         UpperCamelCase_      =	LayoutLMForQuestionAnswering(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(
             _lowercase		,	bbox=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	start_positions=_lowercase		,	end_positions=_lowercase		,	)
         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  )->    Union[str, Any]:
         UpperCamelCase_      =	self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					
         )      =	config_and_inputs
         UpperCamelCase_      =	{
             "input_ids": input_ids,
             "bbox": bbox,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict






@require_torch
class 	__magic_name__				(       snake_case ,			snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Dict								=			(
         (
             LayoutLMModel,
             LayoutLMForMaskedLM,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
             LayoutLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     UpperCamelCase_  :Any								=			(
         {
             """document-question-answering""": LayoutLMForQuestionAnswering,
             """feature-extraction""": LayoutLMModel,
             """fill-mask""": LayoutLMForMaskedLM,
             """text-classification""": LayoutLMForSequenceClassification,
             """token-classification""": LayoutLMForTokenClassification,
             """zero-shot""": LayoutLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCamelCase_  :int								=			True






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	LayoutLMModelTester(self  )
         UpperCamelCase_      =	ConfigTester(self		,	config_class=_lowercase		,	hidden_size=37  )






     def   UpperCAmelCase_							(							self  )->    Any:
         self.config_tester.run_common_tests()






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCamelCase_      =	type
             self.model_tester.create_and_check_model(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_lowercase  )






     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         pass




def  lowerCAmelCase(			)->	Any:



    """simple docstring"""






    UpperCamelCase_      =	torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]] ,	device=SCREAMING_SNAKE_CASE_ )  # noqa: E231
    UpperCamelCase_      =	torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ,	device=SCREAMING_SNAKE_CASE_ )  # noqa: E231
    UpperCamelCase_      =	torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]] ,	device=SCREAMING_SNAKE_CASE_ )  # noqa: E231
    UpperCamelCase_      =	torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ,	device=SCREAMING_SNAKE_CASE_ )  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    UpperCamelCase_      =	torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]] ,	device=SCREAMING_SNAKE_CASE_ )  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class 	__magic_name__				(       unittest.TestCase							):






     @slow
     def   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"  ).to(_lowercase  )

         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	prepare_layoutlm_batch_inputs()

         # forward pass
         UpperCamelCase_      =	model(input_ids=_lowercase		,	bbox=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase  )

         # test the sequence output on [0, :3, :3]
         UpperCamelCase_      =	torch.tensor(
             [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]		,	device=_lowercase		,	)

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

         # test the pooled output on [1, :3]
         UpperCamelCase_      =	torch.tensor([-0.6_580, -0.0_214, 0.8_552]		,	device=_lowercase  )

         self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]		,	_lowercase		,	atol=1e-3  )  )






     @slow
     def   UpperCAmelCase_							(							self  )->    Tuple:
         # initialize model with randomly initialized sequence classification head
         UpperCamelCase_      =	LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=2  ).to(
             _lowercase  )

         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	prepare_layoutlm_batch_inputs()

         # forward pass
         UpperCamelCase_      =	model(
             input_ids=_lowercase		,	bbox=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=torch.tensor([1, 1]		,	device=_lowercase  )		,	)

         # test whether we get a loss as a scalar
         UpperCamelCase_      =	outputs.loss
         UpperCamelCase_      =	torch.Size([]  )
         self.assertEqual(loss.shape		,	_lowercase  )

         # test the shape of the logits
         UpperCamelCase_      =	outputs.logits
         UpperCamelCase_      =	torch.Size((2, 2)  )
         self.assertEqual(logits.shape		,	_lowercase  )






     @slow
     def   UpperCAmelCase_							(							self  )->    str:
         # initialize model with randomly initialized token classification head
         UpperCamelCase_      =	LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=13  ).to(
             _lowercase  )

         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	prepare_layoutlm_batch_inputs()

         # forward pass
         UpperCamelCase_      =	model(
             input_ids=_lowercase		,	bbox=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase  )

         # test the loss calculation to be around 2.65
         # expected_loss = torch.tensor(2.65, device=torch_device)

         # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
         # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

         # test the shape of the logits
         UpperCamelCase_      =	outputs.logits
         UpperCamelCase_      =	torch.Size((2, 25, 13)  )
         self.assertEqual(logits.shape		,	_lowercase  )






     @slow
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         # initialize model with randomly initialized token classification head
         UpperCamelCase_      =	LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"  ).to(_lowercase  )

         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	prepare_layoutlm_batch_inputs()

         # forward pass
         UpperCamelCase_      =	model(input_ids=_lowercase		,	bbox=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase  )

         # test the shape of the logits
         UpperCamelCase_      =	torch.Size((2, 25)  )
         self.assertEqual(outputs.start_logits.shape		,	_lowercase  )
         self.assertEqual(outputs.end_logits.shape		,	_lowercase  )



class 	__magic_name__				:






     def __init__(							self		,	_lowercase  )->    None:
         UpperCamelCase_      =	data
         UpperCamelCase_      =	42
         UpperCamelCase_      =	42




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	None:



    """simple docstring"""






    UpperCamelCase_      =	0
    UpperCamelCase_      =	x




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	None:



    """simple docstring"""






    UpperCamelCase_     ,					UpperCamelCase_      =	find_set(SCREAMING_SNAKE_CASE_ ), find_set(SCREAMING_SNAKE_CASE_ )
    if x == y:
        return

    elif x.rank > y.rank:
        UpperCamelCase_      =	x
    else:
        UpperCamelCase_      =	y
        if x.rank == y.rank:
            y.rank += 1




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Node:



    """simple docstring"""






    if x != x.parent:
        UpperCamelCase_      =	find_set(x.parent )
    return x.parent




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	set:



    """simple docstring"""






    UpperCamelCase_      =	({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    UpperCamelCase_      =	f"{node.data} is not in {sets}"
    raise ValueError(SCREAMING_SNAKE_CASE_ )




def  lowerCAmelCase(			)->	None:



    """simple docstring"""






    UpperCamelCase_      =	[Node(SCREAMING_SNAKE_CASE_ ) for i in range(6 )]
    for v in vertex:
        make_set(SCREAMING_SNAKE_CASE_ )

    union_set(vertex[0] ,	vertex[1] )
    union_set(vertex[1] ,	vertex[2] )
    union_set(vertex[3] ,	vertex[4] )
    union_set(vertex[3] ,	vertex[5] )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(SCREAMING_SNAKE_CASE_ ).isdisjoint(find_python_set(SCREAMING_SNAKE_CASE_ ) ):
                assert find_set(SCREAMING_SNAKE_CASE_ ) != find_set(SCREAMING_SNAKE_CASE_ )
            else:
                assert find_set(SCREAMING_SNAKE_CASE_ ) == find_set(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
 test_disjoint_set()
