

"""simple docstring"""






from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
						from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor






class       _UpperCAmelCase     (						__a):
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(_A ,						"""hidden_sizes"""       )       )
				self.parent.assertTrue(hasattr(_A ,						"""neck_hidden_sizes"""       )       )
				self.parent.assertTrue(hasattr(_A ,						"""num_attention_heads"""       )       )






class       _UpperCAmelCase     :
			def __init__(						self ,						_A ,						_A=13 ,						_A=32 ,						_A=2 ,						_A=3 ,						_A=6_40 ,						_A=4 ,						_A="silu" ,						_A=3 ,						_A=32 ,						_A=0.1 ,						_A=0.1 ,						_A=0.1 ,						_A=0.02 ,						_A=True ,						_A=True ,						_A=10 ,						_A=None ,						)      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[Any]       =   parent
				_UpperCAmelCase      :							Optional[int]       =   batch_size
				_UpperCAmelCase      :							List[Any]       =   image_size
				_UpperCAmelCase      :							Optional[int]       =   patch_size
				_UpperCAmelCase      :							List[Any]       =   num_channels
				_UpperCAmelCase      :							Dict       =   last_hidden_size
				_UpperCAmelCase      :							Union[str, Any]       =   num_attention_heads
				_UpperCAmelCase      :							Union[str, Any]       =   hidden_act
				_UpperCAmelCase      :							List[Any]       =   conv_kernel_size
				_UpperCAmelCase      :							Any       =   output_stride
				_UpperCAmelCase      :							Optional[Any]       =   hidden_dropout_prob
				_UpperCAmelCase      :							List[str]       =   attention_probs_dropout_prob
				_UpperCAmelCase      :							Any       =   classifier_dropout_prob
				_UpperCAmelCase      :							str       =   use_labels
				_UpperCAmelCase      :							int       =   is_training
				_UpperCAmelCase      :							List[Any]       =   num_labels
				_UpperCAmelCase      :							str       =   initializer_range
				_UpperCAmelCase      :							Union[str, Any]       =   scope
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				_UpperCAmelCase      :							Optional[int]       =   None
				_UpperCAmelCase      :							Any       =   None
				if self.use_labels:
					_UpperCAmelCase      :							str       =   ids_tensor([self.batch_size] ,						self.num_labels       )
					_UpperCAmelCase      :							List[Any]       =   ids_tensor([self.batch_size, self.image_size, self.image_size] ,						self.num_labels       )

				_UpperCAmelCase      :							Optional[int]       =   self.get_config()

				return config, pixel_values, labels, pixel_labels
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				return MobileViTConfig(
				    image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						num_attention_heads=self.num_attention_heads ,						hidden_act=self.hidden_act ,						conv_kernel_size=self.conv_kernel_size ,						output_stride=self.output_stride ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						classifier_dropout_prob=self.classifier_dropout_prob ,						initializer_range=self.initializer_range ,						)
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   TFMobileViTModel(config=_A       )
				_UpperCAmelCase      :							Tuple       =   model(_A ,						training=_A       )
				_UpperCAmelCase      :							List[str]       =   self.image_size // self.output_stride
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,						(self.batch_size, self.last_hidden_size, expected_height, expected_width)       )
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.num_labels
				_UpperCAmelCase      :							Optional[Any]       =   TFMobileViTForImageClassification(_A       )
				_UpperCAmelCase      :							List[Any]       =   model(_A ,						labels=_A ,						training=_A       )
				self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)       )
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   self.num_labels
				_UpperCAmelCase      :							str       =   TFMobileViTForSemanticSegmentation(_A       )
				_UpperCAmelCase      :							int       =   self.image_size // self.output_stride

				_UpperCAmelCase      :							Optional[int]       =   model(_A ,						training=_A       )
				self.parent.assertEqual(
				    result.logits.shape ,						(self.batch_size, self.num_labels, expected_height, expected_width)       )

				_UpperCAmelCase      :							List[str]       =   model(_A ,						labels=_A ,						training=_A       )
				self.parent.assertEqual(
				    result.logits.shape ,						(self.batch_size, self.num_labels, expected_height, expected_width)       )
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   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_tf
class       _UpperCAmelCase     (						__a      ,       __a      ,       unittest.TestCase):
			__a	:    Union[str, Any]			   =							(
			    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
			    if is_tf_available()
			    else ()
			)
			__a	:    Union[str, Any]			   =							(
			    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
			    if is_tf_available()
			    else {}
			)

			__a	:    Union[str, Any]			   =							False
			__a	:    int			   =							False
			__a	:    List[str]			   =							False
			__a	:    Tuple			   =							False
			__a	:    Dict			   =							False
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   TFMobileViTModelTester(self       )
				_UpperCAmelCase      :							Union[str, Any]       =   TFMobileViTConfigTester(self ,						config_class=_A ,						has_text_modality=_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				self.config_tester.run_common_tests()
			@unittest.skip(reason="""MobileViT does not use inputs_embeds"""       )
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				pass
			@unittest.skip(reason="""MobileViT does not support input and output embeddings"""       )
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				pass
			@unittest.skip(reason="""MobileViT does not output attentions"""       )
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				pass
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase  ,				_UpperCAmelCase      :							Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

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

					_UpperCAmelCase      :							int       =   ["""pixel_values"""]
					self.assertListEqual(arg_names[:1] ,						_A       )
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				def check_hidden_states_output(_A ,						_A ,						_A       ):
					_UpperCAmelCase      :							Any       =   model_class(_A       )

					_UpperCAmelCase      :							Optional[int]       =   model(**self._prepare_for_class(_A ,						_A       )       )

					_UpperCAmelCase      :							Optional[Any]       =   outputs.hidden_states

					_UpperCAmelCase      :							str       =   5
					self.assertEqual(len(_A       ) ,						_A       )

					# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
					# with the width and height being successively divided by 2.
					_UpperCAmelCase      :							Union[str, Any]       =   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       )

				_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase      :							Optional[int]       =   True
					check_hidden_states_output(_A ,						_A ,						_A       )

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

					check_hidden_states_output(_A ,						_A ,						_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A       )
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*_A       )
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0 ,						reason="""TF does not support backprop for grouped convolutions on CPU.""" ,						)
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				super().test_dataset_conversion()
			def 							__snake_case	(						self ,						_A ,						_A ,						_A=2e-1 ,						_A=2e-1       )      ->      int:






				'''simple docstring'''







				self.assertTrue(np.allclose(_A ,						_A ,						atol=_A ,						rtol=_A       )       )
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0 ,						reason="""TF does not support backprop for grouped convolutions on CPU.""" ,						)
			@slow
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase  ,				_UpperCAmelCase      :							Tuple       =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
					if model_class.__name__ != "TFMobileViTModel":
						_UpperCAmelCase      :							Union[str, Any]       =   model_class(_A       )
						if getattr(_A ,						"""hf_compute_loss""" ,						_A       ):
							super().test_keras_fit()
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase  ,				_UpperCAmelCase      :							Union[str, Any]       =   self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					# set an ignore index to correctly test the masked loss used in
					# `TFMobileViTForSemanticSegmentation`.
					if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
						_UpperCAmelCase      :							Tuple       =   5

					_UpperCAmelCase      :							Tuple       =   model_class(_A       )
					if getattr(_A ,						"""hf_compute_loss""" ,						_A       ):
						# The number of elements in the loss should be the same as the number of elements in the label
						_UpperCAmelCase      :							List[str]       =   self._prepare_for_class(inputs_dict.copy() ,						_A ,						return_labels=_A       )
						_UpperCAmelCase      :							List[Any]       =   prepared_for_class[
						    sorted(prepared_for_class.keys() - inputs_dict.keys() ,						reverse=_A       )[0]
						]
						_UpperCAmelCase      :							List[str]       =   added_label.shape.as_list()[:1]

						# Test that model correctly compute the loss with kwargs
						_UpperCAmelCase      :							Dict       =   self._prepare_for_class(inputs_dict.copy() ,						_A ,						return_labels=_A       )
						_UpperCAmelCase      :							Optional[Any]       =   {"""input_ids""", """pixel_values""", """input_features"""}
						_UpperCAmelCase      :							Union[str, Any]       =   possible_input_names.intersection(set(_A       )       ).pop()
						_UpperCAmelCase      :							int       =   prepared_for_class.pop(_A       )

						_UpperCAmelCase      :							Dict       =   model(_A ,						**_A       )[0]
						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

						# Test that model correctly compute the loss when we mask some positions
						_UpperCAmelCase      :							Any       =   self._prepare_for_class(inputs_dict.copy() ,						_A ,						return_labels=_A       )
						_UpperCAmelCase      :							Union[str, Any]       =   {"""input_ids""", """pixel_values""", """input_features"""}
						_UpperCAmelCase      :							Dict       =   possible_input_names.intersection(set(_A       )       ).pop()
						_UpperCAmelCase      :							List[Any]       =   prepared_for_class.pop(_A       )
						if "labels" in prepared_for_class:
							_UpperCAmelCase      :							Union[str, Any]       =   prepared_for_class["""labels"""].numpy()
							if len(labels.shape       ) > 1 and labels.shape[1] != 1:
								# labels[0] = -100
								_UpperCAmelCase      :							Any       =   tf.convert_to_tensor(_A       )
								_UpperCAmelCase      :							Dict       =   model(_A ,						**_A       )[0]
								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
								self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
						_UpperCAmelCase      :							List[str]       =   self._prepare_for_class(inputs_dict.copy() ,						_A ,						return_labels=_A       )
						_UpperCAmelCase      :							List[Any]       =   model(_A       )[0]
						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

						# Test that model correctly compute the loss with a tuple
						_UpperCAmelCase      :							List[str]       =   self._prepare_for_class(inputs_dict.copy() ,						_A ,						return_labels=_A       )

						# Get keys that were added with the _prepare_for_class function
						_UpperCAmelCase      :							Any       =   prepared_for_class.keys() - inputs_dict.keys()
						_UpperCAmelCase      :							Optional[Any]       =   inspect.signature(model.call       ).parameters
						_UpperCAmelCase      :							Optional[int]       =   list(signature.keys()       )

						# Create a dictionary holding the location of the tensors in the tuple
						_UpperCAmelCase      :							List[Any]       =   {0: input_name}
						for label_key in label_keys:
							_UpperCAmelCase      :							Dict       =   signature_names.index(_A       )
							_UpperCAmelCase      :							Any       =   label_key
						_UpperCAmelCase      :							Tuple       =   sorted(tuple_index_mapping.items()       )
						# Initialize a list with their default values, update the values and convert to a tuple
						_UpperCAmelCase      :							List[str]       =   []

						for name in signature_names:
							if name != "kwargs":
								list_input.append(signature[name].default       )

						for index, value in sorted_tuple_index_mapping:
							_UpperCAmelCase      :							Dict       =   prepared_for_class[value]

						_UpperCAmelCase      :							Optional[int]       =   tuple(_A       )

						# Send to model
						_UpperCAmelCase      :							int       =   model(tuple_input[:-1]       )[0]

						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
			@slow
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_UpperCAmelCase      :							Optional[Any]       =   TFMobileViTModel.from_pretrained(_A       )
					self.assertIsNotNone(_A       )
def  UpperCamelCase   (     )			->     List[str]:
	_UpperCAmelCase      :							List[Any]       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
	return image






@require_tf
class       _UpperCAmelCase     (						unittest.TestCase):
			@slow
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""       )

				_UpperCAmelCase      :							List[str]       =   MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""       )
				_UpperCAmelCase      :							List[str]       =   prepare_img()
				_UpperCAmelCase      :							str       =   image_processor(images=_A ,						return_tensors="""tf"""       )

				# forward pass
				_UpperCAmelCase      :							Tuple       =   model(**_A ,						training=_A       )

				# verify the logits
				_UpperCAmelCase      :							Tuple       =   tf.TensorShape((1, 10_00)       )
				self.assertEqual(outputs.logits.shape ,						_A       )

				_UpperCAmelCase      :							Dict       =   tf.constant([-1.9364, -1.2327, -0.4653]       )

				tf.debugging.assert_near(outputs.logits[0, :3] ,						_A ,						atol=1e-4 ,						rtol=1e-04       )
			@slow
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )

				_UpperCAmelCase      :							str       =   MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )

				_UpperCAmelCase      :							int       =   prepare_img()
				_UpperCAmelCase      :							Any       =   image_processor(images=_A ,						return_tensors="""tf"""       )

				# forward pass
				_UpperCAmelCase      :							Dict       =   model(inputs.pixel_values ,						training=_A       )
				_UpperCAmelCase      :							Any       =   outputs.logits

				# verify the logits
				_UpperCAmelCase      :							Optional[int]       =   tf.TensorShape((1, 21, 32, 32)       )
				self.assertEqual(logits.shape ,						_A       )

				_UpperCAmelCase      :							str       =   tf.constant(
				    [
				        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
				        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
				        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
				    ]       )

				tf.debugging.assert_near(logits[0, :3, :3, :3] ,						_A ,						rtol=1e-4 ,						atol=1e-4       )








"""simple docstring"""






def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     str:
	_UpperCAmelCase      :							Any       =   len(_lowerCAmelCase	)
	for _ in range(
	    0,					int(((no_of_elements - 1) / 2) + 1	)	):  # we don't need to traverse to end of list as
		for j in range(0,					no_of_elements - 1	):
			if (
			    lst[j + 1] < lst[j]
			):  # applying bubble sort algorithm from left to right (or forwards)
				_UpperCAmelCase      :							List[str]       =   lst[j + 1]
				_UpperCAmelCase      :							Optional[int]       =   lst[j]
				_UpperCAmelCase      :							Optional[int]       =   temp
			if (
			    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
			):  # applying bubble sort algorithm from right to left (or backwards)
				_UpperCAmelCase      :							Any       =   lst[no_of_elements - 1 - j]
				_UpperCAmelCase      :							Optional[Any]       =   lst[no_of_elements - 2 - j]
				_UpperCAmelCase      :							Optional[Any]       =   temp
	return lst


if __name__ == "__main__":
						print('''enter the list to be sorted''')
						lowerCamelCase__			:					List[str]							  =					[int(x) for x in input().split()]  # inputing elements of the list in one line
						lowerCamelCase__			:					int							  =					double_sort(lst)
						print('''the sorted list is''')
						print(sorted_lst)





