


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       _UpperCamelCase    (					__UpperCAmelCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				self.config_class(**self.inputs_dict   )
					self.parent.assertTrue(hasattr(_lowerCamelCase				,   """tf_padding"""   )   )
					self.parent.assertTrue(hasattr(_lowerCamelCase				,   """depth_multiplier"""   )   )






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=13				,   a__=3				,   a__=32				,   a__=0.25				,   a__=8				,   a__=8				,   a__=6				,   a__=32				,   a__=True				,   a__=True				,   a__=True				,   a__="relu6"				,   a__=1280				,   a__=0.1				,   a__=0.02				,   a__=True				,   a__=True				,   a__=10				,   a__=None				,   )	->				Optional[int]:
					A								=				parent
					A								=				batch_size
					A								=				num_channels
					A								=				image_size
					A								=				depth_multiplier
					A								=				depth_divisible_by
					A								=				min_depth
					A								=				expand_ratio
					A								=				tf_padding
					A								=				output_stride
					A								=				first_layer_is_expansion
					A								=				finegrained_output
					A								=				hidden_act
					A								=				last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier   )
					A								=				classifier_dropout_prob
					A								=				use_labels
					A								=				is_training
					A								=				num_labels
					A								=				initializer_range
					A								=				scope

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					A								=				None
					A								=				None
					if self.use_labels:
						A								=				ids_tensor([self.batch_size]				,   self.num_labels   )
						A								=				ids_tensor([self.batch_size, self.image_size, self.image_size]				,   self.num_labels   )

					A								=				self.get_config()

					return config, pixel_values, labels, pixel_labels

				def      _UpperCAmelCase					(						self   )	->				Optional[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				,   a__				,   a__				,   a__				,   a__   )	->				Optional[Any]:
					A								=				MobileNetVaModel(config=_lowerCamelCase   )
					model.to(_lowerCamelCase   )
					model.eval()
					A								=				model(_lowerCamelCase   )
					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				,   a__				,   a__				,   a__				,   a__   )	->				Dict:
					A								=				self.num_labels
					A								=				MobileNetVaForImageClassification(_lowerCamelCase   )
					model.to(_lowerCamelCase   )
					model.eval()
					A								=				model(_lowerCamelCase				,   labels=_lowerCamelCase   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__   )	->				List[Any]:
					A								=				self.num_labels
					A								=				MobileNetVaForSemanticSegmentation(_lowerCamelCase   )
					model.to(_lowerCamelCase   )
					model.eval()
					A								=				model(_lowerCamelCase   )
					self.parent.assertEqual(
					    result.logits.shape				,   (
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )				,   )
					A								=				model(_lowerCamelCase				,   labels=_lowerCamelCase   )
					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   )	->				List[str]:
					A								=				self.prepare_config_and_inputs()
					A					,			A					,			A					,			A								=				config_and_inputs
					A								=				{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class       _UpperCamelCase    (					__UpperCAmelCase    ,		__UpperCAmelCase    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= (
				    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				lowerCAmelCase 		= (
				    {
				        "feature-extraction": MobileNetVaModel,
				        "image-classification": MobileNetVaForImageClassification,
				        "image-segmentation": MobileNetVaForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				lowerCAmelCase 		= False
				lowerCAmelCase 		= False
				lowerCAmelCase 		= False
				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				MobileNetVaModelTester(self   )
					A								=				MobileNetVaConfigTester(self				,   config_class=_lowerCamelCase				,   has_text_modality=_lowerCamelCase   )

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					self.config_tester.run_common_tests()

				@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""   )
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					pass

				@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""   )
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					pass

				@unittest.skip(reason="""MobileNetV2 does not output attentions"""   )
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					pass

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						A								=				model_class(_lowerCamelCase   )
						A								=				inspect.signature(model.forward   )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						A								=				[*signature.parameters.keys()]

						A								=				["""pixel_values"""]
						self.assertListEqual(arg_names[:1]				,   _lowerCamelCase   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase   )

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					def check_hidden_states_output(a__				,   a__				,   a__   ):
						A								=				model_class(_lowerCamelCase   )
						model.to(_lowerCamelCase   )
						model.eval()

						with torch.no_grad():
							A								=				model(**self._prepare_for_class(_lowerCamelCase				,   _lowerCamelCase   )   )

						A								=				outputs.hidden_states

						A								=				16
						self.assertEqual(len(_lowerCamelCase   )				,   _lowerCamelCase   )

					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						A								=				True
						check_hidden_states_output(_lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase   )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						A								=				True

						check_hidden_states_output(_lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase   )

				@slow
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A								=				MobileNetVaModel.from_pretrained(_lowerCamelCase   )
						self.assertIsNotNone(_lowerCamelCase   )





def   _lowerCAmelCase				(   ) ->   Optional[int]:





	"""simple docstring"""







	A								=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
	return image






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

				"""simple docstring"""







				@cached_property
				def      _UpperCAmelCase					(						self   )	->				str:
					return (
					    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""   ) if is_vision_available() else None
					)

				@slow
				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""   ).to(_lowerCamelCase   )

					A								=				self.default_image_processor
					A								=				prepare_img()
					A								=				image_processor(images=_lowerCamelCase				,   return_tensors="""pt"""   ).to(_lowerCamelCase   )

					# forward pass
					with torch.no_grad():
						A								=				model(**_lowerCamelCase   )

					# verify the logits
					A								=				torch.Size((1, 1001)   )
					self.assertEqual(outputs.logits.shape				,   _lowerCamelCase   )

					A								=				torch.tensor([0.24_45, -1.19_93, 0.19_05]   ).to(_lowerCamelCase   )

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

				@slow
				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""   )
					A								=				model.to(_lowerCamelCase   )

					A								=				MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""   )

					A								=				prepare_img()
					A								=				image_processor(images=_lowerCamelCase				,   return_tensors="""pt"""   ).to(_lowerCamelCase   )

					# forward pass
					with torch.no_grad():
						A								=				model(**_lowerCamelCase   )
					A								=				outputs.logits

					# verify the logits
					A								=				torch.Size((1, 21, 65, 65)   )
					self.assertEqual(logits.shape				,   _lowerCamelCase   )

					A								=				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=_lowerCamelCase				,   )

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







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



