


'''simple docstring'''
import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
		from transformers.models.mobilevitva.modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    make_divisible,
		)


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor
class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.config_class(**self.inputs_dict							)
							self.parent.assertTrue(hasattr(UpperCamelCase_					,     '''width_multiplier'''							)							)



class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=13					,     UpperCamelCase_=64					,     UpperCamelCase_=2					,     UpperCamelCase_=3					,     UpperCamelCase_="swish"					,     UpperCamelCase_=3					,     UpperCamelCase_=32					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.02					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=10					,     UpperCamelCase_=None					,     UpperCamelCase_=0.25					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				parent
							UpperCamelCase__     :Any							      =				batch_size
							UpperCamelCase__     :Optional[Any]							      =				image_size
							UpperCamelCase__     :int							      =				patch_size
							UpperCamelCase__     :Tuple							      =				num_channels
							UpperCamelCase__     :str							      =				make_divisible(512 * width_multiplier					,     divisor=8							)
							UpperCamelCase__     :List[Any]							      =				hidden_act
							UpperCamelCase__     :List[str]							      =				conv_kernel_size
							UpperCamelCase__     :Optional[int]							      =				output_stride
							UpperCamelCase__     :Dict							      =				classifier_dropout_prob
							UpperCamelCase__     :Optional[int]							      =				use_labels
							UpperCamelCase__     :str							      =				is_training
							UpperCamelCase__     :Dict							      =				num_labels
							UpperCamelCase__     :Dict							      =				initializer_range
							UpperCamelCase__     :List[str]							      =				scope
							UpperCamelCase__     :Any							      =				width_multiplier
							UpperCamelCase__     :Optional[int]							      =				ffn_dropout
							UpperCamelCase__     :List[str]							      =				attn_dropout




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							UpperCamelCase__     :Tuple							      =				self.get_config()

							return config, pixel_values, labels, pixel_labels




		def 					lowerCAmelCase__       (	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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				MobileViTVaModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_							)
							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.num_labels
							UpperCamelCase__     :Optional[Any]							      =				MobileViTVaForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Tuple							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.num_labels
							UpperCamelCase__     :Optional[int]							      =				MobileViTVaForSemanticSegmentation(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.logits.shape					,     (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )					,     )
							UpperCamelCase__     :int							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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



@require_torch
class      lowercase			(							A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

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

		_a									=       False
		_a									=       False
		_a									=       False
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				MobileViTVaModelTester(self							)
							UpperCamelCase__     :Dict							      =				MobileViTVaConfigTester(self					,     config_class=UpperCamelCase_					,     has_text_modality=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.config_tester.run_common_tests()




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

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCamelCase__     :Dict							      =				model_class(UpperCamelCase_							)
												UpperCamelCase__     :Union[str, Any]							      =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__     :Optional[int]							      =				[*signature.parameters.keys()]

												UpperCamelCase__     :int							      =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							def check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :Dict							      =				model_class(UpperCamelCase_							)
												model.to(UpperCamelCase_							)
												model.eval()

												with torch.no_grad():
																	UpperCamelCase__     :int							      =				model(**self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)							)

												UpperCamelCase__     :Optional[Any]							      =				outputs.hidden_states

												UpperCamelCase__     :Tuple							      =				5
												self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

												# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
												# with the width and height being successively divided by 2.
												UpperCamelCase__     :Optional[Any]							      =				2
												for i in range(len(UpperCamelCase_							)							):
																	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__     :Dict							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCamelCase__     :List[str]							      =				True
												check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

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

												check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :int							      =				MobileViTVaModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)



def     a   (    )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
					return image







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

		"""simple docstring"""

		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''							).to(
							    UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				self.default_image_processor
							UpperCamelCase__     :Union[str, Any]							      =				prepare_img()
							UpperCamelCase__     :List[Any]							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Optional[int]							      =				model(**UpperCamelCase_							)

							# verify the logits
							UpperCamelCase__     :List[Any]							      =				torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape					,     UpperCamelCase_							)

							UpperCamelCase__     :Tuple							      =				torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]							).to(UpperCamelCase_							)

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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''							)
							UpperCamelCase__     :List[Any]							      =				model.to(UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''							)

							UpperCamelCase__     :int							      =				prepare_img()
							UpperCamelCase__     :Union[str, Any]							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Optional[Any]							      =				model(**UpperCamelCase_							)
							UpperCamelCase__     :str							      =				outputs.logits

							# verify the logits
							UpperCamelCase__     :Dict							      =				torch.Size((1, 21, 32, 32)							)
							self.assertEqual(logits.shape					,     UpperCamelCase_							)

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

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




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''							)
							UpperCamelCase__     :int							      =				model.to(UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''							)

							UpperCamelCase__     :List[str]							      =				prepare_img()
							UpperCamelCase__     :int							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Union[str, Any]							      =				model(**UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				outputs.logits.detach().cpu()

							UpperCamelCase__     :Tuple							      =				image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_					,     target_sizes=[(50, 60)]							)
							UpperCamelCase__     :List[Any]							      =				torch.Size((50, 60)							)
							self.assertEqual(segmentation[0].shape					,     UpperCamelCase_							)

							UpperCamelCase__     :Tuple							      =				image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				torch.Size((32, 32)							)
							self.assertEqual(segmentation[0].shape					,     UpperCamelCase_							)



'''simple docstring'''
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = ""					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__     :dict[str, RadixNode]							      =				{}

							# A node will be a leaf if the tree contains its word
							UpperCamelCase__     :List[Any]							      =				is_leaf

							UpperCamelCase__     :Dict							      =				prefix




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				0
							for q, w in zip(self.prefix					,     UpperCamelCase_							):
												if q != w:
																	break

												x += 1

							return self.prefix[:x], self.prefix[x:], word[x:]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							for word in words:
												self.insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.prefix == word:
												UpperCamelCase__     :Tuple							      =				True

							# Case 2: The node has no edges that have a prefix to the word
							# Solution: We create an edge from the current node to a new one
							# containing the word
							elif word[0] not in self.nodes:
												UpperCamelCase__     :Optional[Any]							      =				RadixNode(prefix=UpperCamelCase_					,     is_leaf=UpperCamelCase_							)

							else:
												UpperCamelCase__     :Optional[Any]							      =				self.nodes[word[0]]
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				incoming_node.match(
												    UpperCamelCase_							)

												# Case 3: The node prefix is equal to the matching
												# Solution: We insert remaining word on the next node
												if remaining_prefix == "":
																	self.nodes[matching_string[0]].insert(UpperCamelCase_							)

												# Case 4: The word is greater equal to the matching
												# Solution: Create a node in between both nodes, change
												# prefixes and add the new node for the remaining word
												else:
																	UpperCamelCase__     :str							      =				remaining_prefix

																	UpperCamelCase__     :Optional[Any]							      =				self.nodes[matching_string[0]]
																	UpperCamelCase__     :Dict							      =				RadixNode(UpperCamelCase_					,     UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				aux_node

																	if remaining_word == "":
																						UpperCamelCase__     :List[Any]							      =				True
																	else:
																						self.nodes[matching_string[0]].insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																	return False
												# This applies when the word and the prefix are equal
												elif remaining_word == "":
																	return incoming_node.is_leaf
												# We have word remaining so we check the next node
												else:
																	return incoming_node.find(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																	return False
												# We have word remaining so we check the next node
												elif remaining_word != "":
																	return incoming_node.delete(UpperCamelCase_							)
												else:
																	# If it is not a leaf, we don't have to delete
																	if not incoming_node.is_leaf:
																						return False
																	else:
																						# We delete the nodes if no edges go from it
																						if len(incoming_node.nodes							) == 0:
																											del self.nodes[word[0]]
																											# We merge the current node with its only child
																											if len(self.nodes							) == 1 and not self.is_leaf:
																																UpperCamelCase__     :Tuple							      =				list(self.nodes.values()							)[0]
																																UpperCamelCase__     :Union[str, Any]							      =				merging_node.is_leaf
																																self.prefix += merging_node.prefix
																																UpperCamelCase__     :Dict							      =				merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																						elif len(incoming_node.nodes							) > 1:
																											UpperCamelCase__     :List[str]							      =				False
																						# If there is 1 edge, we merge it with its child
																						else:
																											UpperCamelCase__     :Optional[int]							      =				list(incoming_node.nodes.values()							)[0]
																											UpperCamelCase__     :int							      =				merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											UpperCamelCase__     :Optional[int]							      =				merging_node.nodes

																						return True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_ = 0							):

							'''simple docstring'''


							if self.prefix != "":
												print('''-''' * height					,     self.prefix					,     '''  (leaf)''' if self.is_leaf else ''''''							)

							for value in self.nodes.values():
												value.print_tree(height + 1							)



def     a   (    )						->			bool:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				'''banana bananas bandana band apple all beast'''.split()
					UpperCamelCase__     :Tuple							      =				RadixNode()
					root.insert_many(__a     )

					assert all(root.find(__a     ) for word in words     )
					assert not root.find('''bandanas'''     )
					assert not root.find('''apps'''     )
					root.delete('''all'''     )
					assert not root.find('''all'''     )
					root.delete('''banana'''     )
					assert not root.find('''banana'''     )
					assert root.find('''bananas'''     )

					return True


def     a   (    )						->			None:



					'''simple docstring'''






					assert test_trie()


def     a   (    )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				RadixNode()
					UpperCamelCase__     :List[Any]							      =				'''banana bananas bandanas bandana band apple all beast'''.split()
					root.insert_many(__a     )

					print('''Words:'''				,      __a     )
					print('''Tree:'''     )
					root.print_tree()


if __name__ == "__main__":
		main()