





'''simple docstring'''


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__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.config_class(**self.inputs_dict		)
				self.parent.assertTrue(hasattr(lowerCamelCase , "tf_padding"		)		)
				self.parent.assertTrue(hasattr(lowerCamelCase , "depth_multiplier"		)		)






class       UpperCamelCase__  :







	def __init__(				self			:   Dict , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Union[str, Any]=1_3 , lowerCamelCase			:   Tuple=3 , lowerCamelCase			:   List[Any]=3_2 , lowerCamelCase			:   Any=0.25 , lowerCamelCase			:   int=8 , lowerCamelCase			:   Optional[int]=8 , lowerCamelCase			:   Dict=6 , lowerCamelCase			:   int=3_2 , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   str="relu6" , lowerCamelCase			:   Union[str, Any]=1_2_8_0 , lowerCamelCase			:   Any=0.1 , lowerCamelCase			:   Any=0.02 , lowerCamelCase			:   Optional[Any]=True , lowerCamelCase			:   str=True , lowerCamelCase			:   str=1_0 , lowerCamelCase			:   List[Any]=None , ):




				'''simple docstring'''
				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 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				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 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				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 						__a   (				self			:   str , lowerCamelCase			:   int , lowerCamelCase			:   int , lowerCamelCase			:   int , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				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 						__a   (				self			:   Tuple , lowerCamelCase			:   Any , lowerCamelCase			:   int , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				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 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any , lowerCamelCase			:   Any		):




				'''simple docstring'''
				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 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				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__  (		__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Any								=						(
	    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__					:			Tuple								=						(
	    {
	        "feature-extraction": MobileNetVaModel,
	        "image-classification": MobileNetVaForImageClassification,
	        "image-segmentation": MobileNetVaForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCAmelCase__					:			Any								=						False
	lowerCAmelCase__					:			Optional[Any]								=						False
	lowerCAmelCase__					:			Optional[Any]								=						False
	lowerCAmelCase__					:			str								=						False







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = MobileNetVaModelTester(self		)
				a__     = MobileNetVaConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"		)
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"		)
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="MobileNetV2 does not output attentions"		)
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				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 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				def check_hidden_states_output(lowerCamelCase			:   Any , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Any		):
							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__     = 1_6
							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 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase		)







	@slow
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = MobileNetVaModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)






def    _lowerCamelCase  ()   ->    Optional[Any]:
			a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
			return image






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







	@cached_property
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				return (
				    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"		) if is_vision_available() else None
				)







	@slow
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				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, 1_0_0_1)		)
				self.assertEqual(outputs.logits.shape , lowerCamelCase		)

				a__     = torch.tensor([0.2445, -1.1993, 0.1905]		).to(lowerCamelCase		)

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







	@slow
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				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, 2_1, 6_5, 6_5)		)
				self.assertEqual(logits.shape , lowerCamelCase		)

				a__     = torch.tensor(
				    [
				        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
				        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
				        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
				    ] , device=lowerCamelCase , )

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










'''simple docstring'''


import math







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int = 0					,  __lowerCamelCase  :     int = 0     )   ->    list:
			a__     = end or len(__lowerCamelCase     )
			for i in range(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = i
						a__     = array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									a__     = array[temp_index - 1]
									temp_index -= 1
						a__     = temp_index_value
			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    None:  # Max Heap
			a__     = index
			a__     = 2 * index + 1  # Left Node
			a__     = 2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						a__     = left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						a__     = right_index

			if largest != index:
						a__   ,				a__     = array[largest], array[index]
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			a__     = len(__lowerCamelCase     )

			for i in range(n // 2					,  -1					,  -1     ):
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

			for i in range(n - 1					,  0					,  -1     ):
						a__   ,				a__     = array[0], array[i]
						heapify(__lowerCamelCase					,  0					,  __lowerCamelCase     )

			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			a__     = low
			a__     = high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						a__   ,				a__     = array[j], array[i]
						i += 1







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			if len(__lowerCamelCase     ) == 0:
						return array
			a__     = 2 * math.ceil(math.loga(len(__lowerCamelCase     )     )     )
			a__     = 16
			return intro_sort(__lowerCamelCase					,  0					,  len(__lowerCamelCase     )					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    list:
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase     )
						max_depth -= 1
						a__     = median_of_a(__lowerCamelCase					,  __lowerCamelCase					,  start + ((end - start) // 2) + 1					,  end - 1     )
						a__     = partition(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						intro_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						a__     = p
			return insertion_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase_   :   Optional[int]       =					input("Enter numbers separated by a comma : ").strip()
		lowerCAmelCase_   :   List[str]       =					[float(item) for item in user_input.split(",")]
		print(sort(unsorted))



