






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Any						=						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(_UpperCamelCase    ,		'tf_padding'       )       )
							self.parent.assertTrue(hasattr(_UpperCamelCase    ,		'depth_multiplier'       )       )





class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=3    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=0.25    ,		_UpperCamelCase=8    ,		_UpperCamelCase=8    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="relu6"    ,		_UpperCamelCase=1_2_8_0    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=1_0    ,		_UpperCamelCase=None    ,		)     ->							str:
							UpperCAmelCase_   :				int						=						parent
							UpperCAmelCase_   :				int						=						batch_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				int						=						image_size
							UpperCAmelCase_   :				Optional[Any]						=						depth_multiplier
							UpperCAmelCase_   :				Optional[Any]						=						depth_divisible_by
							UpperCAmelCase_   :				Dict						=						min_depth
							UpperCAmelCase_   :				Tuple						=						expand_ratio
							UpperCAmelCase_   :				Optional[int]						=						tf_padding
							UpperCAmelCase_   :				Union[str, Any]						=						output_stride
							UpperCAmelCase_   :				Tuple						=						first_layer_is_expansion
							UpperCAmelCase_   :				Dict						=						finegrained_output
							UpperCAmelCase_   :				Optional[int]						=						hidden_act
							UpperCAmelCase_   :				Any						=						last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
							UpperCAmelCase_   :				Optional[Any]						=						classifier_dropout_prob
							UpperCAmelCase_   :				Tuple						=						use_labels
							UpperCAmelCase_   :				int						=						is_training
							UpperCAmelCase_   :				Dict						=						num_labels
							UpperCAmelCase_   :				Optional[int]						=						initializer_range
							UpperCAmelCase_   :				Dict						=						scope



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[int]						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							UpperCAmelCase_   :				Union[str, Any]						=						None
							UpperCAmelCase_   :				List[str]						=						None
							if self.use_labels:
												UpperCAmelCase_   :				List[str]						=						ids_tensor([self.batch_size]    ,		self.num_labels       )
												UpperCAmelCase_   :				Dict						=						ids_tensor([self.batch_size, self.image_size, self.image_size]    ,		self.num_labels       )

							UpperCAmelCase_   :				List[Any]						=						self.get_config()

							return config, pixel_values, labels, pixel_labels



		def  __UpperCAmelCase      (							self       )     ->							Any:
							return MobileNetVaConfig(
							    num_channels=self.num_channels    ,		image_size=self.image_size    ,		depth_multiplier=self.depth_multiplier    ,		depth_divisible_by=self.depth_divisible_by    ,		min_depth=self.min_depth    ,		expand_ratio=self.expand_ratio    ,		output_stride=self.output_stride    ,		first_layer_is_expansion=self.first_layer_is_expansion    ,		finegrained_output=self.finegrained_output    ,		hidden_act=self.hidden_act    ,		tf_padding=self.tf_padding    ,		classifier_dropout_prob=self.classifier_dropout_prob    ,		initializer_range=self.initializer_range    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							str:
							UpperCAmelCase_   :				List[Any]						=						MobileNetVaModel(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				int						=						model(_UpperCamelCase       )
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,		(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,		)
							self.parent.assertEqual(
							    result.pooler_output.shape    ,		(self.batch_size, self.last_hidden_size)    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						self.num_labels
							UpperCAmelCase_   :				Dict						=						MobileNetVaForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_   :				str						=						self.num_labels
							UpperCAmelCase_   :				Union[str, Any]						=						MobileNetVaForSemanticSegmentation(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Tuple						=						model(_UpperCamelCase       )
							self.parent.assertEqual(
							    result.logits.shape    ,		(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,		)
							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(
							    result.logits.shape    ,		(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				List[str]						=						self.prepare_config_and_inputs()
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						config_and_inputs
							UpperCAmelCase_   :				Tuple						=						{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class   lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  (
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		_snake_case   :   Optional[Any]							=  (
		    {
		        '''feature-extraction''': MobileNetVaModel,
		        '''image-classification''': MobileNetVaForImageClassification,
		        '''image-segmentation''': MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_snake_case   :   int							=  False
		_snake_case   :   Tuple							=  False
		_snake_case   :   str							=  False
		_snake_case   :   List[str]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				str						=						MobileNetVaModelTester(self       )
							UpperCAmelCase_   :				Any						=						MobileNetVaConfigTester(self    ,		config_class=_UpperCamelCase    ,		has_text_modality=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							self.config_tester.run_common_tests()



		@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'       )
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							pass



		@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'       )
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass



		@unittest.skip(reason='MobileNetV2 does not output attentions'       )
		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase_   :				List[str]						=						['pixel_values']
												self.assertListEqual(arg_names[:1]    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							def check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				int						=						model_class(_UpperCamelCase       )
												model.to(_UpperCamelCase       )
												model.eval()

												with torch.no_grad():
																	UpperCAmelCase_   :				int						=						model(**self._prepare_for_class(_UpperCamelCase    ,		_UpperCamelCase       )       )

												UpperCAmelCase_   :				List[Any]						=						outputs.hidden_states

												UpperCAmelCase_   :				List[str]						=						1_6
												self.assertEqual(len(_UpperCamelCase       )    ,		_UpperCamelCase       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				Dict						=						True
												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase_   :				Dict						=						True

												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Tuple						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Tuple						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				str						=						MobileNetVaModel.from_pretrained(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






@require_torch
@require_vision
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		@cached_property
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return (
							    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'       ) if is_vision_available() else None
							)



		@slow
		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				str						=						MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'       ).to(_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						self.default_image_processor
							UpperCAmelCase_   :				Optional[Any]						=						prepare_img()
							UpperCAmelCase_   :				Tuple						=						image_processor(images=_UpperCamelCase    ,		return_tensors='pt'       ).to(_UpperCamelCase       )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_   :				List[Any]						=						model(**_UpperCamelCase       )

							# verify the logits
							UpperCAmelCase_   :				str						=						torch.Size((1, 1_0_0_1)       )
							self.assertEqual(outputs.logits.shape    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						torch.tensor([0.24_45, -1.19_93, 0.19_05]       ).to(_UpperCamelCase       )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		_UpperCamelCase    ,		atol=1E-4       )       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				int						=						MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'       )
							UpperCAmelCase_   :				Tuple						=						model.to(_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'       )

							UpperCAmelCase_   :				List[Any]						=						prepare_img()
							UpperCAmelCase_   :				List[str]						=						image_processor(images=_UpperCamelCase    ,		return_tensors='pt'       ).to(_UpperCamelCase       )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_   :				List[str]						=						model(**_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						outputs.logits

							# verify the logits
							UpperCAmelCase_   :				List[Any]						=						torch.Size((1, 2_1, 6_5, 6_5)       )
							self.assertEqual(logits.shape    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						torch.tensor(
							    [
							        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
							        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
							        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
							    ]    ,		device=_UpperCamelCase    ,		)

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,		_UpperCamelCase    ,		atol=1E-4       )       )














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) == 0:
										return False
					UpperCAmelCase_   :				Optional[int]						=						len(__snake_case  ) // 2
					if a_list[midpoint] == item:
										return True
					if item < a_list[midpoint]:
										return binary_search(a_list[:midpoint]					,	__snake_case  )
					else:
										return binary_search(a_list[midpoint + 1 :]					,	__snake_case  )


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






