



'''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  (							lowerCamelCase__   ):


			'''simple docstring'''

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.config_class(**self.inputs_dict						)
					self.parent.assertTrue(hasattr(lowerCAmelCase__							,					"""hidden_sizes"""						)						)
					self.parent.assertTrue(hasattr(lowerCAmelCase__							,					"""neck_hidden_sizes"""						)						)
					self.parent.assertTrue(hasattr(lowerCAmelCase__							,					"""num_attention_heads"""						)						)






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Optional[Any]							,					lowerCAmelCase__					:			Union[str, Any]							,					lowerCAmelCase__					:			Dict=1_3							,					lowerCAmelCase__					:			str=3_2							,					lowerCAmelCase__					:			List[str]=2							,					lowerCAmelCase__					:			Any=3							,					lowerCAmelCase__					:			List[str]=6_4_0							,					lowerCAmelCase__					:			List[Any]=4							,					lowerCAmelCase__					:			Optional[int]="silu"							,					lowerCAmelCase__					:			Any=3							,					lowerCAmelCase__					:			Union[str, Any]=3_2							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Union[str, Any]=0.1							,					lowerCAmelCase__					:			int=0.02							,					lowerCAmelCase__					:			List[Any]=True							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			Dict=1_0							,					lowerCAmelCase__					:			Any=None							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		parent
					__SCREAMING_SNAKE_CASE	:		Dict   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		str   	=		image_size
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		patch_size
					__SCREAMING_SNAKE_CASE	:		Dict   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		str   	=		last_hidden_size
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		num_attention_heads
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		Any   	=		conv_kernel_size
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		output_stride
					__SCREAMING_SNAKE_CASE	:		Any   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		int   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		Any   	=		classifier_dropout_prob
					__SCREAMING_SNAKE_CASE	:		int   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		is_training
					__SCREAMING_SNAKE_CASE	:		str   	=		num_labels
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		str   	=		scope

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE	:		int   	=		None
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		ids_tensor([self.batch_size]							,					self.num_labels						)
							__SCREAMING_SNAKE_CASE	:		str   	=		ids_tensor([self.batch_size, self.image_size, self.image_size]							,					self.num_labels						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.get_config()

					return config, pixel_values, labels, pixel_labels

			def UpperCamelCase__ (							self					:			Tuple						):





					"""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 UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		TFMobileViTModel(config=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__							,					training=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		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 UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.num_labels
					__SCREAMING_SNAKE_CASE	:		int   	=		TFMobileViTForImageClassification(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(lowerCAmelCase__							,					labels=lowerCAmelCase__							,					training=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.num_labels
					__SCREAMING_SNAKE_CASE	:		int   	=		TFMobileViTForSemanticSegmentation(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.image_size // self.output_stride

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__							,					training=lowerCAmelCase__						)
					self.parent.assertEqual(
					    result.logits.shape							,					(self.batch_size, self.num_labels, expected_height, expected_width)						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(lowerCAmelCase__							,					labels=lowerCAmelCase__							,					training=lowerCAmelCase__						)
					self.parent.assertEqual(
					    result.logits.shape							,					(self.batch_size, self.num_labels, expected_height, expected_width)						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_tf
class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		List[Any]         =		(
			    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
			    if is_tf_available()
			    else ()
			)
			_A				:		Any         =		(
			    {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
			    if is_tf_available()
			    else {}
			)

			_A				:		Union[str, Any]         =		False
			_A				:		int         =		False
			_A				:		Any         =		False
			_A				:		Dict         =		False
			_A				:		Optional[int]         =		False

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		TFMobileViTModelTester(self						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		TFMobileViTConfigTester(self							,					config_class=lowerCAmelCase__							,					has_text_modality=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			@unittest.skip(reason="""MobileViT does not use inputs_embeds"""						)
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""MobileViT does not support input and output embeddings"""						)
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""MobileViT does not output attentions"""						)
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

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

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,					lowerCAmelCase__						)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					def check_hidden_states_output(lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Any						):
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model_class(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		Dict   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		outputs.hidden_states

							__SCREAMING_SNAKE_CASE	:		str   	=		5
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

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

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

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		True
							check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

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

							check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__						)

			@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 UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					super().test_dataset_conversion()

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Union[str, Any]=2E-1							,					lowerCAmelCase__					:			List[Any]=2E-1						):





					"""simple docstring"""






					self.assertTrue(np.allclose(lowerCAmelCase__							,					lowerCAmelCase__							,					atol=lowerCAmelCase__							,					rtol=lowerCAmelCase__						)						)

			@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 UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		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":
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class(lowerCAmelCase__						)
									if getattr(lowerCAmelCase__							,					"""hf_compute_loss"""							,					lowerCAmelCase__						):
											super().test_keras_fit()

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		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":
									__SCREAMING_SNAKE_CASE	:		Any   	=		5

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

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

									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(lowerCAmelCase__							,					**lowerCAmelCase__						)[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
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self._prepare_for_class(inputs_dict.copy()							,					lowerCAmelCase__							,					return_labels=lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		int   	=		{"""input_ids""", """pixel_values""", """input_features"""}
									__SCREAMING_SNAKE_CASE	:		int   	=		possible_input_names.intersection(set(lowerCAmelCase__						)						).pop()
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		prepared_for_class.pop(lowerCAmelCase__						)
									if "labels" in prepared_for_class:
											__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		prepared_for_class["""labels"""].numpy()
											if len(labels.shape						) > 1 and labels.shape[1] != 1:
													# labels[0] = -100
													__SCREAMING_SNAKE_CASE	:		List[Any]   	=		tf.convert_to_tensor(lowerCAmelCase__						)
													__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(lowerCAmelCase__							,					**lowerCAmelCase__						)[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
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self._prepare_for_class(inputs_dict.copy()							,					lowerCAmelCase__							,					return_labels=lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(lowerCAmelCase__						)[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
									__SCREAMING_SNAKE_CASE	:		Any   	=		self._prepare_for_class(inputs_dict.copy()							,					lowerCAmelCase__							,					return_labels=lowerCAmelCase__						)

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

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

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

									for index, value in sorted_tuple_index_mapping:
											__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		prepared_for_class[value]

									__SCREAMING_SNAKE_CASE	:		int   	=		tuple(lowerCAmelCase__						)

									# Send to model
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(tuple_input[:-1]						)[0]

									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)



			@slow
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		TFMobileViTModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






@require_tf
class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		prepare_img()
					__SCREAMING_SNAKE_CASE	:		int   	=		image_processor(images=lowerCAmelCase__							,					return_tensors="""tf"""						)

					# forward pass
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(**lowerCAmelCase__							,					training=lowerCAmelCase__						)

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		tf.TensorShape((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		tf.constant([-1.93_64, -1.23_27, -0.46_53]						)

					tf.debugging.assert_near(outputs.logits[0, :3]							,					lowerCAmelCase__							,					atol=1E-4							,					rtol=1E-04						)



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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		prepare_img()
					__SCREAMING_SNAKE_CASE	:		Dict   	=		image_processor(images=lowerCAmelCase__							,					return_tensors="""tf"""						)

					# forward pass
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(inputs.pixel_values							,					training=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		outputs.logits

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		Dict   	=		tf.TensorShape((1, 2_1, 3_2, 3_2)						)
					self.assertEqual(logits.shape							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		tf.constant(
					    [
					        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
					        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
					        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
					    ]						)

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




'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

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





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()