





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 	_UpperCAmelCase						:




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=13     ,   __lowerCAmelCase=7     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=99     ,   __lowerCAmelCase=32     ,   __lowerCAmelCase=5     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=37     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=512     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=1_000     ,   )    ->	Optional[Any]:
							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 						snake_case_		(    self							)    ->	str:
							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 						snake_case_		(    self							)    ->	List[Any]:
							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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						LayoutLMModel(config=__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase							)
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase							)
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase							)
							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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	List[Any]:
							UpperCamelCase__								=						LayoutLMForMaskedLM(config=__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase     ,   labels=__lowerCAmelCase							)
							self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						self.num_labels
							UpperCamelCase__								=						LayoutLMForSequenceClassification(__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(
							    __lowerCAmelCase     ,   __lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase     ,   labels=__lowerCAmelCase							)
							self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Optional[int]:
							UpperCamelCase__								=						self.num_labels
							UpperCamelCase__								=						LayoutLMForTokenClassification(config=__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase     ,   labels=__lowerCAmelCase							)
							self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						LayoutLMForQuestionAnswering(config=__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(
							    __lowerCAmelCase     ,   bbox=__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase     ,   start_positions=__lowerCAmelCase     ,   end_positions=__lowerCAmelCase     ,   )
							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							)    ->	Tuple:
							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 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = (
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					__A        = (
					    {
					        "document-question-answering": LayoutLMForQuestionAnswering,
					        "feature-extraction": LayoutLMModel,
					        "fill-mask": LayoutLMForMaskedLM,
					        "text-classification": LayoutLMForSequenceClassification,
					        "token-classification": LayoutLMForTokenClassification,
					        "zero-shot": LayoutLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__A        = True




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						LayoutLMModelTester(self							)
							UpperCamelCase__								=						ConfigTester(self     ,   config_class=__lowerCAmelCase     ,   hidden_size=37							)




					def 						snake_case_		(    self							)    ->	List[Any]:
							self.config_tester.run_common_tests()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[int]:
							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(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[Any]:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase							)




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



def      a    (   )  ->List[str]:
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		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=_lowercase							)  # 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=_lowercase							)  # 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=_lowercase							)  # 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=_lowercase							)  # 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=_lowercase							)  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class 	_UpperCAmelCase						( unittest.TestCase       ):




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

							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						prepare_layoutlm_batch_inputs()

							# forward pass
							UpperCamelCase__								=						model(input_ids=__lowerCAmelCase     ,   bbox=__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase							)

							# test the sequence output on [0, :3, :3]
							UpperCamelCase__								=						torch.tensor(
							    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]     ,   device=__lowerCAmelCase     ,   )

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

							# test the pooled output on [1, :3]
							UpperCamelCase__								=						torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]     ,   device=__lowerCAmelCase							)

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




					@slow
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							# initialize model with randomly initialized sequence classification head
							UpperCamelCase__								=						LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=2							).to(
							    __lowerCAmelCase							)

							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						prepare_layoutlm_batch_inputs()

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

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

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




					@slow
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							# initialize model with randomly initialized token classification head
							UpperCamelCase__								=						LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=13							).to(
							    __lowerCAmelCase							)

							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						prepare_layoutlm_batch_inputs()

							# forward pass
							UpperCamelCase__								=						model(
							    input_ids=__lowerCAmelCase     ,   bbox=__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase     ,   labels=__lowerCAmelCase							)

							# 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     ,   __lowerCAmelCase							)




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

							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						prepare_layoutlm_batch_inputs()

							# forward pass
							UpperCamelCase__								=						model(input_ids=__lowerCAmelCase     ,   bbox=__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   token_type_ids=__lowerCAmelCase							)

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












import itertools
from pathlib import Path

def      a    (   _lowercase :  list[str]							)  ->int:
		UpperCamelCase__								=						[tuple(_lowercase							) for login in logins]

		UpperCamelCase__								=						{char for login in split_logins for char in login}

		for permutation in itertools.permutations(_lowercase							):
				UpperCamelCase__								=						True
				for login in logins:
						if not (
						    permutation.index(login[0]							)
						    < permutation.index(login[1]							)
						    < permutation.index(login[2]							)
						):
								UpperCamelCase__								=						False
								break

				if satisfied:
						return int("""""".join(_lowercase							)							)

		raise Exception("""Unable to find the secret passcode"""							)

def      a    (   _lowercase :  str = "keylog.txt"							)  ->int:
		UpperCamelCase__								=						Path(_lowercase							).parent.joinpath(_lowercase							).read_text().splitlines()

		return find_secret_passcode(_lowercase							)


if __name__ == "__main__":
				print(f"""{solution() = }""")





