






"""simple docstring"""
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__   :







    """simple docstring"""







    def __init__(		self   :  int							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Any=1_3							,						UpperCamelCase_   :  Tuple=7							,						UpperCamelCase_   :  Any=True							,						UpperCamelCase_   :  Tuple=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Optional[Any]=True							,						UpperCamelCase_   :  List[str]=9_9							,						UpperCamelCase_   :  Any=3_2							,						UpperCamelCase_   :  Dict=5							,						UpperCamelCase_   :  str=4							,						UpperCamelCase_   :  Any=3_7							,						UpperCamelCase_   :  str="gelu"							,						UpperCamelCase_   :  int=0.1							,						UpperCamelCase_   :  Tuple=0.1							,						UpperCamelCase_   :  List[str]=5_1_2							,						UpperCamelCase_   :  Any=1_6							,						UpperCamelCase_   :  int=2							,						UpperCamelCase_   :  List[Any]=0.02							,						UpperCamelCase_   :  str=3							,						UpperCamelCase_   :  int=4							,						UpperCamelCase_   :  Dict=None							,						UpperCamelCase_   :  List[Any]=1_0_0_0							,						):





           '''simple docstring'''

           __magic_name__										=						parent
           __magic_name__										=						batch_size
           __magic_name__										=						seq_length
           __magic_name__										=						is_training
           __magic_name__										=						use_input_mask
           __magic_name__										=						use_token_type_ids
           __magic_name__										=						use_labels
           __magic_name__										=						vocab_size
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_act
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						type_vocab_size
           __magic_name__										=						type_sequence_label_size
           __magic_name__										=						initializer_range
           __magic_name__										=						num_labels
           __magic_name__										=						num_choices
           __magic_name__										=						scope
           __magic_name__										=						range_bbox







    def 							a__ (		self   :  int   ):





           '''simple docstring'''

           __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )

           __magic_name__										=						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]:
                                __magic_name__										=						bbox[i, j, 3]
                                __magic_name__										=						bbox[i, j, 1]
                                __magic_name__										=						t
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                __magic_name__										=						bbox[i, j, 2]
                                __magic_name__										=						bbox[i, j, 0]
                                __magic_name__										=						t

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

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

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

           __magic_name__										=						self.get_config()

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







    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           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 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Any   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMModel(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(UpperCamelCase_							,						UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_   )
           __magic_name__										=						model(UpperCamelCase_							,						UpperCamelCase_							,						token_type_ids=UpperCamelCase_   )
           __magic_name__										=						model(UpperCamelCase_							,						UpperCamelCase_   )
           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 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  List[Any]   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMForMaskedLM(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(UpperCamelCase_							,						UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						labels=UpperCamelCase_   )
           self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )







    def 							a__ (		self   :  Any							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  int   ):





           '''simple docstring'''

           __magic_name__										=						self.num_labels
           __magic_name__										=						LayoutLMForSequenceClassification(UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(
               UpperCamelCase_							,						UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						labels=UpperCamelCase_   )
           self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)   )







    def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]   ):





           '''simple docstring'''

           __magic_name__										=						self.num_labels
           __magic_name__										=						LayoutLMForTokenClassification(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(UpperCamelCase_							,						UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						labels=UpperCamelCase_   )
           self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)   )







    def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  int   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMForQuestionAnswering(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(
               UpperCamelCase_							,						bbox=UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						start_positions=UpperCamelCase_							,						end_positions=UpperCamelCase_							,						)
           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 							a__ (		self   :  int   ):





           '''simple docstring'''

           __magic_name__										=						self.prepare_config_and_inputs()
           (
               (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     
           )										=						config_and_inputs
           __magic_name__										=						{
               'input_ids': input_ids,
               'bbox': bbox,
               'token_type_ids': token_type_ids,
               'attention_mask': input_mask,
           }
           return config, inputs_dict



@require_torch
class 					UpperCamelCase__   (							a_       ,  a_       ,  unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase											=   (
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    __UpperCAmelCase											=   (
        {
            """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											=   True







    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMModelTester(self   )
           __magic_name__										=						ConfigTester(self							,						config_class=UpperCamelCase_							,						hidden_size=3_7   )







    def 							a__ (		self   :  List[Any]   ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    def 							a__ (		self   :  List[str]   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_   )







    def 							a__ (		self   :  int   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __magic_name__										=						type
                  self.model_tester.create_and_check_model(*UpperCamelCase_   )







    def 							a__ (		self   :  Dict   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_   )







    def 							a__ (		self   :  Tuple   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_   )







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





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_   )







    def 							a__ (		self   :  Any   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_   )







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
    def 							a__ (		self   :  List[Any]   ):





           '''simple docstring'''

           pass


def        A	(							)						->		Tuple:

       """simple docstring"""





       __magic_name__										=						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=__snake_case     )  # noqa: E231
       __magic_name__										=						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=__snake_case     )  # noqa: E231
       __magic_name__										=						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=__snake_case     )  # noqa: E231
       __magic_name__										=						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=__snake_case     )  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       __magic_name__										=						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=__snake_case     )  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class 					UpperCamelCase__   (							unittest.TestCase):







    """simple docstring"""







    @slow
    def 							a__ (		self   :  str   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(UpperCamelCase_   )

           __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						prepare_layoutlm_batch_inputs()

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

           # test the sequence output on [0, :3, :3]
           __magic_name__										=						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=UpperCamelCase_							,						)

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

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

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







    @slow
    def 							a__ (		self   :  int   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,						num_labels=2   ).to(
               UpperCamelCase_   )

           __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						prepare_layoutlm_batch_inputs()

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

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

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







    @slow
    def 							a__ (		self   :  str   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,						num_labels=1_3   ).to(
               UpperCamelCase_   )

           __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						prepare_layoutlm_batch_inputs()

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

           # 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
           __magic_name__										=						outputs.logits
           __magic_name__										=						torch.Size((2, 2_5, 1_3)   )
           self.assertEqual(logits.shape							,						UpperCamelCase_   )







    @slow
    def 							a__ (		self   :  Union[str, Any]   ):





           '''simple docstring'''

           __magic_name__										=						LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(UpperCamelCase_   )

           __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						prepare_layoutlm_batch_inputs()

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

           # test the shape of the logits
           __magic_name__										=						torch.Size((2, 2_5)   )
           self.assertEqual(outputs.start_logits.shape							,						UpperCamelCase_   )
           self.assertEqual(outputs.end_logits.shape							,						UpperCamelCase_   )







"""simple docstring"""
def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:

       """simple docstring"""





       sequence.append(__snake_case     )

       __magic_name__										=						0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__snake_case     ):
              return None

       return index


if __name__ == "__main__":
 snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
 snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]

 snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
 snake_case   :   str    		=		int(target_input)
 snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print("""Not found""")