

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

								__lowerCamelCase    :    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    :    Tuple 	=			(torch.rand((self.batch_size, self.num_labels)       ,device=SCREAMING_SNAKE_CASE__) > 0.5).long()

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

								__lowerCamelCase    :    Tuple 	=			[1, 1, 1, 1]
								__lowerCamelCase    :    Dict 	=			self.num_channels

								__lowerCamelCase    :    Dict 	=			6_4
								__lowerCamelCase    :    str 	=			1_2_8
								__lowerCamelCase    :    Union[str, Any] 	=			self.hidden_dim
								__lowerCamelCase    :    Tuple 	=			self.hidden_dim
								__lowerCamelCase    :    List[str] 	=			self.hidden_dim
								return config
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase    :    str 	=			{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
								return config, inputs_dict
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any):
								__lowerCamelCase    :    List[Any] 	=			output.encoder_hidden_states
								__lowerCamelCase    :    Dict 	=			output.pixel_decoder_hidden_states
								__lowerCamelCase    :    Optional[Any] 	=			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		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str=False):
								with torch.no_grad():
									__lowerCamelCase    :    Tuple 	=			MaskaFormerModel(config=SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									__lowerCamelCase    :    int 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    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		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Any):
								__lowerCamelCase    :    Tuple 	=			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								def comm_check_on_output(SCREAMING_SNAKE_CASE__		:      List[str]):
									# 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    :    List[str] 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[Any] 	=			model(SCREAMING_SNAKE_CASE__)

									comm_check_on_output(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Any 	=			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_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  str										=				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_UpperCAmelCase   :  Optional[int]										=				{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

							_UpperCAmelCase   :  Union[str, Any]										=				False
							_UpperCAmelCase   :  Any										=				False
							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  Dict										=				False
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			MaskaFormerModelTester(self)
								__lowerCamelCase    :    List[str] 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple):
								self.config_tester.run_common_tests()
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase	,			__lowerCamelCase    :    List[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[Any]):
								__lowerCamelCase    :    Optional[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		:      Optional[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		:      Dict):
								pass
							@unittest.skip(reason='Mask2Former does not use token embeddings')
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								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		:      Optional[int]):
								pass
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def    lowerCAmelCase					(		self		:      List[str]):
								pass
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase    :    str 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									__lowerCamelCase    :    Optional[Any] 	=			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Optional[int] 	=			(self.model_tester.min_size,) * 2
								__lowerCamelCase    :    str 	=			{
								    '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    :    str 	=			self.model_tester.get_config()

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

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

								__lowerCamelCase    :    Optional[int] 	=			self.all_model_classes[1]
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs()

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

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

								__lowerCamelCase    :    Dict 	=			model(SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__)

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

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

								__lowerCamelCase    :    Union[str, Any] 	=			outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								__lowerCamelCase    :    int 	=			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    :    Union[str, Any] 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image






@require_vision
@slow
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								return "facebook/mask2former-swin-small-coco-instance"
							@cached_property
							def    lowerCAmelCase					(		self		:      List[str]):
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[Any] 	=			MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			self.default_image_processor
								__lowerCamelCase    :    str 	=			prepare_img()
								__lowerCamelCase    :    Optional[int] 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, 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    :    Optional[int] 	=			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    :    Dict 	=			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		:      Any):
								__lowerCamelCase    :    Dict 	=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
								__lowerCamelCase    :    str 	=			self.default_image_processor
								__lowerCamelCase    :    Any 	=			prepare_img()
								__lowerCamelCase    :    List[str] 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[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    :    Dict 	=			model(**SCREAMING_SNAKE_CASE__)
								# masks_queries_logits
								__lowerCamelCase    :    Tuple 	=			outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape       ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
								__lowerCamelCase    :    Optional[Any] 	=			[
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								__lowerCamelCase    :    Optional[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    :    Union[str, Any] 	=			outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape       ,(1, model.config.num_queries, model.config.num_labels + 1))
								__lowerCamelCase    :    Optional[int] 	=			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		:      Union[str, Any]):
								__lowerCamelCase    :    Dict 	=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
								__lowerCamelCase    :    Optional[Any] 	=			self.default_image_processor

								__lowerCamelCase    :    Tuple 	=			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    :    List[str] 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs['mask_labels']]
								__lowerCamelCase    :    str 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs['class_labels']]

								with torch.no_grad():
									__lowerCamelCase    :    Any 	=			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}""")




