




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     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.config_class(**self.inputs_dict )
						self.parent.assertTrue(hasattr(__lowercase			,	'hidden_sizes' ) )
						self.parent.assertTrue(hasattr(__lowercase			,	'neck_hidden_sizes' ) )
						self.parent.assertTrue(hasattr(__lowercase			,	'num_attention_heads' ) )


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=32			,	__lowercase=2			,	__lowercase=3			,	__lowercase=640			,	__lowercase=4			,	__lowercase="silu"			,	__lowercase=3			,	__lowercase=32			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.02			,	__lowercase=True			,	__lowercase=True			,	__lowercase=10			,	__lowercase=None			,	):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




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

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

						lowerCamelCase_							:		str							       =							self.get_config()

						return config, pixel_values, labels, pixel_labels

					def      lowercase_							(    self ):







						'''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      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							TFMobileViTModel(config=__lowercase )
						lowerCamelCase_							:		int							       =							model(__lowercase			,	training=__lowercase )
						lowerCamelCase_							:		int							       =							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      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMobileViTForImageClassification(__lowercase )
						lowerCamelCase_							:		Dict							       =							model(__lowercase			,	labels=__lowercase			,	training=__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.num_labels
						lowerCamelCase_							:		List[str]							       =							TFMobileViTForSemanticSegmentation(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.image_size // self.output_stride

						lowerCamelCase_							:		int							       =							model(__lowercase			,	training=__lowercase )
						self.parent.assertEqual(
						    result.logits.shape			,	(self.batch_size, self.num_labels, expected_height, expected_width) )

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





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							self.prepare_config_and_inputs()
						lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'pixel_values': pixel_values}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
					    if is_tf_available()
					    else {}
					)

					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMobileViTModelTester(self )
						lowerCamelCase_							:		Any							       =							TFMobileViTConfigTester(self			,	config_class=__lowercase			,	has_text_modality=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					@unittest.skip(reason='MobileViT does not use inputs_embeds' )
					def      lowercase_							(    self ):







						'''simple docstring'''




						pass

					@unittest.skip(reason='MobileViT does not support input and output embeddings' )
					def      lowercase_							(    self ):







						'''simple docstring'''




						pass

					@unittest.skip(reason='MobileViT does not output attentions' )
					def      lowercase_							(    self ):







						'''simple docstring'''




						pass

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_       ,      lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCamelCase_							:		str							       =							['pixel_values']
							self.assertListEqual(arg_names[:1]			,	__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						def check_hidden_states_output(__lowercase			,	__lowercase			,	__lowercase ):
							lowerCamelCase_							:		Dict							       =							model_class(__lowercase )

							lowerCamelCase_							:		str							       =							model(**self._prepare_for_class(__lowercase			,	__lowercase ) )

							lowerCamelCase_							:		Optional[int]							       =							outputs.hidden_states

							lowerCamelCase_							:		Union[str, Any]							       =							5
							self.assertEqual(len(__lowercase )			,	__lowercase )

							# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
							# with the width and height being successively divided by 2.
							lowerCamelCase_							:		str							       =							2
							for i in range(len(__lowercase ) ):
								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_							:		Optional[int]							       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCamelCase_							:		Union[str, Any]							       =							True
							check_hidden_states_output(__lowercase			,	__lowercase			,	__lowercase )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCamelCase_							:		Optional[Any]							       =							True

							check_hidden_states_output(__lowercase			,	__lowercase			,	__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase )

					@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      lowercase_							(    self ):







						'''simple docstring'''




						super().test_dataset_conversion()

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase=2E-1			,	__lowercase=2E-1 ):







						'''simple docstring'''




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

					@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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_       ,      lowerCamelCase_							:		str							       =							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":
								lowerCamelCase_							:		Any							       =							model_class(__lowercase )
								if getattr(__lowercase			,	'hf_compute_loss'			,	__lowercase ):
									super().test_keras_fit()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_       ,      lowerCamelCase_							:		List[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":
								lowerCamelCase_							:		Tuple							       =							5

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

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

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

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

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

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

								for index, value in sorted_tuple_index_mapping:
									lowerCamelCase_							:		str							       =							prepared_for_class[value]

								lowerCamelCase_							:		Any							       =							tuple(__lowercase )

								# Send to model
								lowerCamelCase_							:		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      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:		Optional[Any]							       =							TFMobileViTModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )






def  snake_case (							)							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image



@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' )

						lowerCamelCase_							:		List[Any]							       =							MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' )
						lowerCamelCase_							:		Optional[Any]							       =							prepare_img()
						lowerCamelCase_							:		Optional[int]							       =							image_processor(images=__lowercase			,	return_tensors='tf' )

						# forward pass
						lowerCamelCase_							:		List[Any]							       =							model(**__lowercase			,	training=__lowercase )

						# verify the logits
						lowerCamelCase_							:		List[str]							       =							tf.TensorShape((1, 1_000) )
						self.assertEqual(outputs.logits.shape			,	__lowercase )

						lowerCamelCase_							:		Union[str, Any]							       =							tf.constant([-1.93_64, -1.23_27, -0.46_53] )

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





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

						lowerCamelCase_							:		int							       =							MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

						lowerCamelCase_							:		List[str]							       =							prepare_img()
						lowerCamelCase_							:		List[Any]							       =							image_processor(images=__lowercase			,	return_tensors='tf' )

						# forward pass
						lowerCamelCase_							:		Union[str, Any]							       =							model(inputs.pixel_values			,	training=__lowercase )
						lowerCamelCase_							:		Any							       =							outputs.logits

						# verify the logits
						lowerCamelCase_							:		str							       =							tf.TensorShape((1, 21, 32, 32) )
						self.assertEqual(logits.shape			,	__lowercase )

						lowerCamelCase_							:		List[Any]							       =							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]			,	__lowercase			,	rtol=1E-4			,	atol=1E-4 )






from __future__ import annotations






def  snake_case (							A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							0
	lowerCamelCase_							:		str							       =							len(A      ) - 1
	while left <= right:
		lowerCamelCase_							:		str							       =							(left + right) // 2
		if left == right:
			if sorted_list[middle] < item:
				lowerCamelCase_							:		List[Any]							       =							middle + 1
			break
		elif sorted_list[middle] < item:
			lowerCamelCase_							:		Any							       =							middle + 1
		else:
			lowerCamelCase_							:		Optional[int]							       =							middle - 1
	sorted_list.insert(A						,       A      )
	return sorted_list






def  snake_case (							A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							[]
	while left and right:
		if left[0][0] < right[0][0]:
			result.append(left.pop(0      )      )
		else:
			result.append(right.pop(0      )      )
	return result + left + right






def  snake_case (							A      )							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							len(A      )
	if length <= 1:
		return list_ad
	lowerCamelCase_							:		Any							       =							length // 2
	return merge(sortlist_ad(list_ad[:middle]      )						,       sortlist_ad(list_ad[middle:]      )      )






def  snake_case (							A      )							->							list[int]:




	'''simple docstring'''


	if len(A      ) <= 1:
		return collection
	lowerCamelCase_							:		Tuple							       =							[]
	lowerCamelCase_							:		int							       =							False
	for i in range(0						,       len(A      )						,       2      ):
		if i == len(A      ) - 1:
			lowerCamelCase_							:		Any							       =							True
		else:
			if collection[i] < collection[i + 1]:
				two_paired_list.append([collection[i], collection[i + 1]]      )
			else:
				two_paired_list.append([collection[i + 1], collection[i]]      )
	lowerCamelCase_							:		Union[str, Any]							       =							sortlist_ad(A      )
	lowerCamelCase_							:		int							       =							[i[0] for i in sorted_list_ad]
	result.append(sorted_list_ad[-1][1]      )
	if has_last_odd_item:
		lowerCamelCase_							:		List[str]							       =							collection[-1]
		lowerCamelCase_							:		Optional[Any]							       =							binary_search_insertion(A						,       A      )
	lowerCamelCase_							:		Optional[Any]							       =							False
	for i in range(len(A      ) - 1      ):
		if result[i] == collection[-1] and has_last_odd_item:
			lowerCamelCase_							:		Optional[Any]							       =							True
		lowerCamelCase_							:		Dict							       =							sorted_list_ad[i][1]
		# If last_odd_item is inserted before the item's index,
		# you should forward index one more.
		if is_last_odd_item_inserted_before_this_index:
			lowerCamelCase_							:		Dict							       =							result[: i + 2] + binary_search_insertion(result[i + 2 :]						,       A      )
		else:
			lowerCamelCase_							:		str							       =							result[: i + 1] + binary_search_insertion(result[i + 1 :]						,       A      )

	return result


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