




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       a__   ( snake_case__				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.config_class(**self.inputs_dict      )
											self.parent.assertTrue(hasattr(_A		,      "width_multiplier"      )      )






class       a__   :






						def __init__(       self		,      _A		,      _A=1_3		,      _A=6_4		,      _A=2		,      _A=3		,      _A="swish"		,      _A=3		,      _A=3_2		,      _A=0.1		,      _A=0.02		,      _A=True		,      _A=True		,      _A=1_0		,      _A=None		,      _A=0.25		,      _A=0.0		,      _A=0.0		,      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

											__lowerCAmelCase           =	self.get_config()

											return config, pixel_values, labels, pixel_labels






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTVaModel(config=_A      )
											model.to(_A      )
											model.eval()
											__lowerCAmelCase           =	model(_A      )
											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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.num_labels
											__lowerCAmelCase           =	MobileViTVaForImageClassification(_A      )
											model.to(_A      )
											model.eval()
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.num_labels
											__lowerCAmelCase           =	MobileViTVaForSemanticSegmentation(_A      )
											model.to(_A      )
											model.eval()
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(
											    result.logits.shape		,      (
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )		,      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.prepare_config_and_inputs()
											__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	config_and_inputs
											__lowerCAmelCase           =	{"pixel_values": pixel_values}
											return config, inputs_dict






@require_torch
class       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   Tuple =   (
						    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)

						_a							:   Dict =   (
						    {
						        """feature-extraction""": MobileViTVaModel,
						        """image-classification""": MobileViTVaForImageClassification,
						        """image-segmentation""": MobileViTVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_a							:   List[Any] =   False
						_a							:   Dict =   False
						_a							:   Any =   False
						_a							:   Tuple =   False






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTVaModelTester(self      )
											__lowerCAmelCase           =	MobileViTVaConfigTester(self		,      config_class=_A		,      has_text_modality=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileViTV2 does not use inputs_embeds"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@unittest.skip(reason="MobileViTV2 does not support input and output embeddings"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@unittest.skip(reason="MobileViTV2 does not output attentions"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






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




											"""simple docstring"""



											pass






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




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase           =	["pixel_values"]
																self.assertListEqual(arg_names[:1]		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											def check_hidden_states_output(_A		,      _A		,      _A      ):
																__lowerCAmelCase           =	model_class(_A      )
																model.to(_A      )
																model.eval()

																with torch.no_grad():
																					__lowerCAmelCase           =	model(**self._prepare_for_class(_A		,      _A      )      )

																__lowerCAmelCase           =	outputs.hidden_states

																__lowerCAmelCase           =	5
																self.assertEqual(len(_A      )		,      _A      )

																# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																__lowerCAmelCase           =	2
																for i in range(len(_A      )      ):
																					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      )

											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase           =	True
																check_hidden_states_output(_A		,      _A		,      _A      )

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

																check_hidden_states_output(_A		,      _A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*_A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	MobileViTVaModel.from_pretrained(_A      )
																self.assertIsNotNone(_A      )







def      _a				(						):
					__lowerCAmelCase           =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


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






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"      ).to(
											    _A      )

											__lowerCAmelCase           =	self.default_image_processor
											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )

											# verify the logits
											__lowerCAmelCase           =	torch.Size((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape		,      _A      )

											__lowerCAmelCase           =	torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]      ).to(_A      )

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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"      )
											__lowerCAmelCase           =	model.to(_A      )

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

											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )
											__lowerCAmelCase           =	outputs.logits

											# verify the logits
											__lowerCAmelCase           =	torch.Size((1, 2_1, 3_2, 3_2)      )
											self.assertEqual(logits.shape		,      _A      )

											__lowerCAmelCase           =	torch.tensor(
											    [
											        [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
											        [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
											        [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
											    ]		,      device=_A		,      )

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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"      )
											__lowerCAmelCase           =	model.to(_A      )

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

											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )

											__lowerCAmelCase           =	outputs.logits.detach().cpu()

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

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










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




