





'''simple docstring'''


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
		from transformers.models.mobilevitva.modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    make_divisible,
		)


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   Tuple		):




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






class       UpperCamelCase__  :







	def __init__(				self			:   str , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any]=1_3 , lowerCamelCase			:   Any=6_4 , lowerCamelCase			:   Optional[int]=2 , lowerCamelCase			:   Union[str, Any]=3 , lowerCamelCase			:   List[str]="swish" , lowerCamelCase			:   str=3 , lowerCamelCase			:   Dict=3_2 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   Optional[Any]=0.02 , lowerCamelCase			:   str=True , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Optional[int]=1_0 , lowerCamelCase			:   Any=None , lowerCamelCase			:   Tuple=0.25 , lowerCamelCase			:   Optional[Any]=0.0 , lowerCamelCase			:   Any=0.0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = image_size
				a__     = patch_size
				a__     = num_channels
				a__     = make_divisible(5_1_2 * width_multiplier , divisor=8		)
				a__     = hidden_act
				a__     = conv_kernel_size
				a__     = output_stride
				a__     = classifier_dropout_prob
				a__     = use_labels
				a__     = is_training
				a__     = num_labels
				a__     = initializer_range
				a__     = scope
				a__     = width_multiplier
				a__     = ffn_dropout
				a__     = attn_dropout







	def 						__a   (				self			:   Dict		):




				'''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			:   Any		):




				'''simple docstring'''
				return MobileViTVaConfig(
				    image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   Dict , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = MobileViTVaModel(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,
				    ) , )







	def 						__a   (				self			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = MobileViTVaForImageClassification(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			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[str] , lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = MobileViTVaForSemanticSegmentation(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			:   Optional[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__					:			Optional[int]								=						(
	    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)

	lowerCAmelCase__					:			Union[str, Any]								=						(
	    {
	        "feature-extraction": MobileViTVaModel,
	        "image-classification": MobileViTVaForImageClassification,
	        "image-segmentation": MobileViTVaForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCAmelCase__					:			Optional[int]								=						False
	lowerCAmelCase__					:			Union[str, Any]								=						False
	lowerCAmelCase__					:			str								=						False
	lowerCAmelCase__					:			Any								=						False







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = MobileViTVaModelTester(self		)
				a__     = MobileViTVaConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any]		):




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







	@unittest.skip(reason="MobileViTV2 does not use inputs_embeds"		)
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="MobileViTV2 does not support input and output embeddings"		)
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="MobileViTV2 does not output attentions"		)
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				pass







	@require_torch_multi_gpu
	@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."		)
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				pass







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Union[str, Any]		):




				'''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			:   Union[str, Any]		):




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







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				def check_hidden_states_output(lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str		):
							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__     = 5
							self.assertEqual(len(lowerCamelCase		) , lowerCamelCase		)

							# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
							# with the width and height being successively divided by 2.
							a__     = 2
							for i in range(len(lowerCamelCase		)		):
										self.assertListEqual(
										    list(hidden_states[i].shape[-2:]		) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
										divisor *= 2

							self.assertEqual(self.model_tester.output_stride , divisor // 2		)

				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			:   Dict		):




				'''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			:   List[str]		):




				'''simple docstring'''
				for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = MobileViTVaModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)







def    _lowerCamelCase  ()   ->    Optional[int]:
			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			:   Optional[int]		):




				'''simple docstring'''
				return (
				    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		)
				    if is_vision_available()
				    else None
				)







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




				'''simple docstring'''
				a__     = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		).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_0)		)
				self.assertEqual(outputs.logits.shape , lowerCamelCase		)

				a__     = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		).to(lowerCamelCase		)

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







	@slow
	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
				a__     = model.to(lowerCamelCase		)

				a__     = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

				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, 3_2, 3_2)		)
				self.assertEqual(logits.shape , lowerCamelCase		)

				a__     = torch.tensor(
				    [
				        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
				        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
				        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
				    ] , device=lowerCamelCase , )

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







	@slow
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
				a__     = model.to(lowerCamelCase		)

				a__     = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

				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.detach().cpu()

				a__     = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase , target_sizes=[(5_0, 6_0)]		)
				a__     = torch.Size((5_0, 6_0)		)
				self.assertEqual(segmentation[0].shape , lowerCamelCase		)

				a__     = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase		)
				a__     = torch.Size((3_2, 3_2)		)
				self.assertEqual(segmentation[0].shape , lowerCamelCase		)










'''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))



