

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
     import torch

     from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

     if is_vision_available():
          from transformers import MaskaFormerImageProcessor

if is_vision_available():
     from PIL import Image






class 	A_		:
       def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      Dict=False       ,SCREAMING_SNAKE_CASE__		:      Dict=1_0       ,SCREAMING_SNAKE_CASE__		:      Tuple=3       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3_2 * 8       ,SCREAMING_SNAKE_CASE__		:      Dict=3_2 * 8       ,SCREAMING_SNAKE_CASE__		:      str=4       ,SCREAMING_SNAKE_CASE__		:      List[Any]=6_4       ,):
        __lowerCamelCase    :    str 	=			parent
        __lowerCamelCase    :    str 	=			batch_size
        __lowerCamelCase    :    Dict 	=			is_training
        __lowerCamelCase    :    Any 	=			use_auxiliary_loss
        __lowerCamelCase    :    Optional[int] 	=			num_queries
        __lowerCamelCase    :    Optional[int] 	=			num_channels
        __lowerCamelCase    :    Tuple 	=			min_size
        __lowerCamelCase    :    Optional[Any] 	=			max_size
        __lowerCamelCase    :    Optional[int] 	=			num_labels
        __lowerCamelCase    :    Any 	=			hidden_dim
        __lowerCamelCase    :    Dict 	=			hidden_dim
       def    lowerCAmelCase					(		self		:      Dict):
        __lowerCamelCase    :    Optional[Any] 	=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
            SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    Optional[int] 	=			torch.ones([self.batch_size, self.min_size, self.max_size]       ,device=SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    Any 	=			(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,device=SCREAMING_SNAKE_CASE__) > 0.5
        ).float()
        __lowerCamelCase    :    Dict 	=			(torch.rand((self.batch_size, self.num_labels)       ,device=SCREAMING_SNAKE_CASE__) > 0.5).long()

        __lowerCamelCase    :    Any 	=			self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels
       def    lowerCAmelCase					(		self		:      int):
        __lowerCamelCase    :    List[str] 	=			MaskaFormerConfig(
            hidden_size=self.hidden_dim       ,)
        __lowerCamelCase    :    Tuple 	=			self.num_queries
        __lowerCamelCase    :    Tuple 	=			self.num_labels

        __lowerCamelCase    :    List[Any] 	=			[1, 1, 1, 1]
        __lowerCamelCase    :    str 	=			self.num_channels

        __lowerCamelCase    :    List[str] 	=			6_4
        __lowerCamelCase    :    str 	=			1_2_8
        __lowerCamelCase    :    Union[str, Any] 	=			self.hidden_dim
        __lowerCamelCase    :    int 	=			self.hidden_dim
        __lowerCamelCase    :    Any 	=			self.hidden_dim
        return config
       def    lowerCAmelCase					(		self		:      Dict):
        __lowerCamelCase    :    Union[str, Any] 	=			self.prepare_config_and_inputs()
        __lowerCamelCase    :    Optional[int] 	=			{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
        return config, inputs_dict
       def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
        __lowerCamelCase    :    str 	=			output.encoder_hidden_states
        __lowerCamelCase    :    List[str] 	=			output.pixel_decoder_hidden_states
        __lowerCamelCase    :    int 	=			output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,len(config.backbone_config.depths))
        self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,len(config.backbone_config.depths))
        self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,config.decoder_layers)
       def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]=False):




        with torch.no_grad():
         __lowerCamelCase    :    Optional[int] 	=			MaskaFormerModel(config=SCREAMING_SNAKE_CASE__)
         model.to(SCREAMING_SNAKE_CASE__)
         model.eval()

         __lowerCamelCase    :    str 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
         __lowerCamelCase    :    List[Any] 	=			model(SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape       ,(self.batch_size, self.num_queries, self.hidden_dim)       ,)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
        self.parent.assertTrue(output.encoder_last_hidden_state is not None)

        if output_hidden_states:
         self.check_output_hidden_state(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)


       def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
        __lowerCamelCase    :    Any 	=			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__)
        model.to(SCREAMING_SNAKE_CASE__)
        model.eval()

        def comm_check_on_output(SCREAMING_SNAKE_CASE__		:      Any):
         # let's still check that all the required stuff is there
         self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
         self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
         self.parent.assertTrue(result.encoder_last_hidden_state is not None)
         # okay, now we need to check the logits shape
         # due to the encoder compression, masks have a //4 spatial size
         self.parent.assertEqual(
             result.masks_queries_logits.shape       ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,)
         # + 1 for null class
         self.parent.assertEqual(
             result.class_queries_logits.shape       ,(self.batch_size, self.num_queries, self.num_labels + 1))

        with torch.no_grad():
         __lowerCamelCase    :    Optional[int] 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
         __lowerCamelCase    :    List[str] 	=			model(SCREAMING_SNAKE_CASE__)

         comm_check_on_output(SCREAMING_SNAKE_CASE__)

         __lowerCamelCase    :    Tuple 	=			model(
             pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__)

        comm_check_on_output(SCREAMING_SNAKE_CASE__)

        self.parent.assertTrue(result.loss is not None)
        self.parent.assertEqual(result.loss.shape       ,torch.Size([1]))






@require_torch
class 	A_		(	lowerCamelCase__      ,							lowerCamelCase__      ,							unittest.TestCase    ):
       _UpperCAmelCase   :  List[Any]										=				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       _UpperCAmelCase   :  Optional[int]										=				{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

       _UpperCAmelCase   :  Any										=				False
       _UpperCAmelCase   :  List[Any]										=				False
       _UpperCAmelCase   :  List[Any]										=				False
       _UpperCAmelCase   :  str										=				False
       def    lowerCAmelCase					(		self		:      Union[str, Any]):
        __lowerCamelCase    :    List[Any] 	=			MaskaFormerModelTester(self)
        __lowerCamelCase    :    Dict 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__)
       def    lowerCAmelCase					(		self		:      List[str]):
        self.config_tester.run_common_tests()
       def    lowerCAmelCase					(		self		:      Optional[Any]):
        __lowerCamelCase    :    List[str] 	=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)
       def    lowerCAmelCase					(		self		:      int):
        __lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__)
       @unittest.skip(reason='Mask2Former does not use inputs_embeds')
       def    lowerCAmelCase					(		self		:      List[Any]):
        pass
       @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method')
       def    lowerCAmelCase					(		self		:      Dict):
        pass
       @unittest.skip(reason='Mask2Former is not a generative model')
       def    lowerCAmelCase					(		self		:      List[Any]):
        pass
       @unittest.skip(reason='Mask2Former does not use token embeddings')
       def    lowerCAmelCase					(		self		:      Optional[int]):
        pass
       @require_torch_multi_gpu
       @unittest.skip(
           reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
       def    lowerCAmelCase					(		self		:      str):
        pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
       def    lowerCAmelCase					(		self		:      Union[str, Any]):
        pass
       def    lowerCAmelCase					(		self		:      List[str]):
        __lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         __lowerCamelCase    :    List[str] 	=			model_class(SCREAMING_SNAKE_CASE__)
         __lowerCamelCase    :    List[str] 	=			inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __lowerCamelCase    :    Union[str, Any] 	=			[*signature.parameters.keys()]

         __lowerCamelCase    :    Union[str, Any] 	=			["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
       @slow
       def    lowerCAmelCase					(		self		:      int):




        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
         __lowerCamelCase    :    List[str] 	=			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
       def    lowerCAmelCase					(		self		:      str):
        __lowerCamelCase    :    str 	=			(self.model_tester.min_size,) * 2
        __lowerCamelCase    :    Any 	=			{
            "pixel_values": torch.randn((2, 3, *size)       ,device=SCREAMING_SNAKE_CASE__),
            "mask_labels": torch.randn((2, 1_0, *size)       ,device=SCREAMING_SNAKE_CASE__),
            "class_labels": torch.zeros(2       ,1_0       ,device=SCREAMING_SNAKE_CASE__).long(),
        }
        __lowerCamelCase    :    Tuple 	=			self.model_tester.get_config()

        __lowerCamelCase    :    Optional[int] 	=			MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    List[str] 	=			model(**SCREAMING_SNAKE_CASE__)
        self.assertTrue(outputs.loss is not None)
       def    lowerCAmelCase					(		self		:      List[str]):
        __lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)
       def    lowerCAmelCase					(		self		:      Optional[int]):
        __lowerCamelCase    :    Union[str, Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         __lowerCamelCase    :    Dict 	=			model_class(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
         __lowerCamelCase    :    List[Any] 	=			model(**SCREAMING_SNAKE_CASE__       ,output_attentions=SCREAMING_SNAKE_CASE__)
         self.assertTrue(outputs.attentions is not None)
       def    lowerCAmelCase					(		self		:      str):




        if not self.model_tester.is_training:
         return

        __lowerCamelCase    :    str 	=			self.all_model_classes[1]
        __lowerCamelCase    :    Any 	=			self.model_tester.prepare_config_and_inputs()

        __lowerCamelCase    :    List[str] 	=			model_class(SCREAMING_SNAKE_CASE__)
        model.to(SCREAMING_SNAKE_CASE__)
        model.train()

        __lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__).loss
        loss.backward()


       def    lowerCAmelCase					(		self		:      Dict):
        __lowerCamelCase    :    Union[str, Any] 	=			self.all_model_classes[1]
        __lowerCamelCase    :    Union[str, Any] 	=			self.model_tester.prepare_config_and_inputs()
        __lowerCamelCase    :    List[Any] 	=			True
        __lowerCamelCase    :    Optional[Any] 	=			True

        __lowerCamelCase    :    str 	=			model_class(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
        model.train()

        __lowerCamelCase    :    Union[str, Any] 	=			model(SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    str 	=			outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        __lowerCamelCase    :    Optional[Any] 	=			outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        __lowerCamelCase    :    int 	=			outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        __lowerCamelCase    :    Optional[Any] 	=			outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(pixel_decoder_hidden_states.grad)
        self.assertIsNotNone(transformer_decoder_hidden_states.grad)
        self.assertIsNotNone(attentions.grad)
a		=1E-4






def        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[Any]:
 __lowerCamelCase    :    str 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
 return image






@require_vision
@slow
class 	A_		(	unittest.TestCase    ):
       @cached_property
       def    lowerCAmelCase					(		self		:      Union[str, Any]):
        return "facebook/mask2former-swin-small-coco-instance"
       @cached_property
       def    lowerCAmelCase					(		self		:      str):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
       def    lowerCAmelCase					(		self		:      List[Any]):
        __lowerCamelCase    :    Union[str, Any] 	=			MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    str 	=			self.default_image_processor
        __lowerCamelCase    :    Optional[int] 	=			prepare_img()
        __lowerCamelCase    :    int 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    Optional[Any] 	=			inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
        # check size
        self.assertEqual(SCREAMING_SNAKE_CASE__       ,(1, 3, 3_8_4, 3_8_4))

        with torch.no_grad():
         __lowerCamelCase    :    Tuple 	=			model(**SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    List[str] 	=			torch.tensor(
            [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]).to(SCREAMING_SNAKE_CASE__)
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))

        __lowerCamelCase    :    str 	=			torch.tensor(
            [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]).to(SCREAMING_SNAKE_CASE__)
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))

        __lowerCamelCase    :    int 	=			torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]).to(SCREAMING_SNAKE_CASE__)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))
       def    lowerCAmelCase					(		self		:      Optional[Any]):
        __lowerCamelCase    :    Optional[Any] 	=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
        __lowerCamelCase    :    Union[str, Any] 	=			self.default_image_processor
        __lowerCamelCase    :    Dict 	=			prepare_img()
        __lowerCamelCase    :    Optional[int] 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    Any 	=			inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
        # check size
        self.assertEqual(SCREAMING_SNAKE_CASE__       ,(1, 3, 3_8_4, 3_8_4))

        with torch.no_grad():
         __lowerCamelCase    :    List[Any] 	=			model(**SCREAMING_SNAKE_CASE__)
        # masks_queries_logits
        __lowerCamelCase    :    Dict 	=			outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape       ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
        __lowerCamelCase    :    Any 	=			[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        __lowerCamelCase    :    Any 	=			torch.tensor(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))
        # class_queries_logits
        __lowerCamelCase    :    Dict 	=			outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape       ,(1, model.config.num_queries, model.config.num_labels + 1))
        __lowerCamelCase    :    List[str] 	=			torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ]).to(SCREAMING_SNAKE_CASE__)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))


       def    lowerCAmelCase					(		self		:      str):
        __lowerCamelCase    :    str 	=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
        __lowerCamelCase    :    Any 	=			self.default_image_processor

        __lowerCamelCase    :    Union[str, Any] 	=			image_processor(
            [np.zeros((3, 8_0_0, 1_3_3_3)), np.zeros((3, 8_0_0, 1_3_3_3))]       ,segmentation_maps=[np.zeros((3_8_4, 3_8_4)).astype(np.floataa), np.zeros((3_8_4, 3_8_4)).astype(np.floataa)]       ,return_tensors='pt'       ,)

        __lowerCamelCase    :    int 	=			inputs["pixel_values"].to(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    Optional[int] 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs["mask_labels"]]
        __lowerCamelCase    :    List[Any] 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs["class_labels"]]

        with torch.no_grad():
         __lowerCamelCase    :    Optional[int] 	=			model(**SCREAMING_SNAKE_CASE__)

        self.assertTrue(outputs.loss is not None)







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




