




"""simple docstring"""







import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, 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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
						from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		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       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	Tuple    ,   lowercase__					:	List[str]    ,   lowercase__					:	Dict=1_3    ,   lowercase__					:	Any=3_2    ,   lowercase__					:	List[Any]=2    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Any=6_4_0    ,   lowercase__					:	Any=4    ,   lowercase__					:	Dict="silu"    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Optional[int]=3_2    ,   lowercase__					:	int=0.1    ,   lowercase__					:	List[str]=0.1    ,   lowercase__					:	Optional[Any]=0.1    ,   lowercase__					:	str=0.0_2    ,   lowercase__					:	Dict=True    ,   lowercase__					:	int=True    ,   lowercase__					:	Dict=1_0    ,   lowercase__					:	Tuple=None    ,   ):
						__lowercase				:		Union[str, Any]												=				parent
						__lowercase				:		Optional[int]												=				batch_size
						__lowercase				:		Any												=				image_size
						__lowercase				:		List[Any]												=				patch_size
						__lowercase				:		Any												=				num_channels
						__lowercase				:		List[str]												=				last_hidden_size
						__lowercase				:		int												=				num_attention_heads
						__lowercase				:		Any												=				hidden_act
						__lowercase				:		Tuple												=				conv_kernel_size
						__lowercase				:		Any												=				output_stride
						__lowercase				:		Union[str, Any]												=				hidden_dropout_prob
						__lowercase				:		str												=				attention_probs_dropout_prob
						__lowercase				:		int												=				classifier_dropout_prob
						__lowercase				:		Optional[int]												=				use_labels
						__lowercase				:		int												=				is_training
						__lowercase				:		int												=				num_labels
						__lowercase				:		Dict												=				initializer_range
						__lowercase				:		Union[str, Any]												=				scope



				def       snake_case    (     self					:	int						):
						__lowercase				:		Optional[int]												=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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

						return config, pixel_values, labels, pixel_labels



				def       snake_case    (     self					:	int						):
						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					:	Tuple    ,   lowercase__					:	int    ,   lowercase__					:	Tuple    ,   lowercase__					:	Tuple    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		Dict												=				MobileViTModel(config=lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		int												=				model(lowercase__						)
						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       snake_case    (     self					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Any    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		str												=				self.num_labels
						__lowercase				:		Union[str, Any]												=				MobileViTForImageClassification(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		List[Any]												=				model(lowercase__    ,   labels=lowercase__						)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	List[str]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Optional[Any]						):
						__lowercase				:		Optional[Any]												=				self.num_labels
						__lowercase				:		int												=				MobileViTForSemanticSegmentation(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		int												=				model(lowercase__						)
						self.parent.assertEqual(
						    result.logits.shape    ,   (
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )    ,   )
						__lowercase				:		str												=				model(lowercase__    ,   labels=lowercase__						)
						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       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[int]												=				self.prepare_config_and_inputs()
						__lowercase				:		Tuple												=				config_and_inputs
						__lowercase				:		str												=				{"pixel_values": pixel_values}
						return config, inputs_dict





@require_torch
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      (
				    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__UpperCAmelCase :     str       =      (
				    {
				        "feature-extraction": MobileViTModel,
				        "image-classification": MobileViTForImageClassification,
				        "image-segmentation": MobileViTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCAmelCase :     Union[str, Any]       =      False
				__UpperCAmelCase :     Optional[int]       =      False
				__UpperCAmelCase :     str       =      False
				__UpperCAmelCase :     Any       =      False



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		int												=				MobileViTModelTester(self						)
						__lowercase				:		List[Any]												=				MobileViTConfigTester(self    ,   config_class=lowercase__    ,   has_text_modality=lowercase__						)



				def       snake_case    (     self					:	Any						):
						self.config_tester.run_common_tests()



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



				@unittest.skip(reason="MobileViT does not support input and output embeddings"						)
				def       snake_case    (     self					:	Optional[int]						):
						pass



				@unittest.skip(reason="MobileViT does not output attentions"						)
				def       snake_case    (     self					:	List[Any]						):
						pass



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		int												=				self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase				:		Any												=				["pixel_values"]
								self.assertListEqual(arg_names[:1]    ,   lowercase__						)



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



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		str												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase__						)



				def       snake_case    (     self					:	int						):
						def check_hidden_states_output(lowercase__					:	Tuple    ,   lowercase__					:	Any    ,   lowercase__					:	Tuple						):
								__lowercase				:		Union[str, Any]												=				model_class(lowercase__						)
								model.to(lowercase__						)
								model.eval()

								with torch.no_grad():
										__lowercase				:		Dict												=				model(**self._prepare_for_class(lowercase__    ,   lowercase__						)						)

								__lowercase				:		Tuple												=				outputs.hidden_states

								__lowercase				:		Optional[int]												=				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.
								__lowercase				:		Union[str, Any]												=				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						)

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

						for model_class in self.all_model_classes:
								__lowercase				:		Dict												=				True
								check_hidden_states_output(lowercase__    ,   lowercase__    ,   lowercase__						)

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

								check_hidden_states_output(lowercase__    ,   lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Optional[int]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase__						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Any												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__						)



				@slow
				def       snake_case    (     self					:	Any						):
						for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase				:		str												=				MobileViTModel.from_pretrained(lowercase__						)
								self.assertIsNotNone(lowercase__						)





def 						snake_case__       (					)			->Dict:






		"""simple docstring"""


		__lowercase				:		Any												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image




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


				"""simple docstring"""





				@cached_property
				def       snake_case    (     self					:	int						):
						return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"						) if is_vision_available() else None



				@slow
				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"						).to(lowercase__						)

						__lowercase				:		Union[str, Any]												=				self.default_image_processor
						__lowercase				:		int												=				prepare_img()
						__lowercase				:		Dict												=				image_processor(images=lowercase__    ,   return_tensors="pt"						).to(lowercase__						)

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

						# verify the logits
						__lowercase				:		Tuple												=				torch.Size((1, 1_0_0_0)						)
						self.assertEqual(outputs.logits.shape    ,   lowercase__						)

						__lowercase				:		Optional[int]												=				torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]						).to(lowercase__						)

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



				@slow
				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Any												=				MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
						__lowercase				:		List[str]												=				model.to(lowercase__						)

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

						__lowercase				:		Any												=				prepare_img()
						__lowercase				:		Optional[Any]												=				image_processor(images=lowercase__    ,   return_tensors="pt"						).to(lowercase__						)

						# forward pass
						with torch.no_grad():
								__lowercase				:		Optional[Any]												=				model(**lowercase__						)
						__lowercase				:		Tuple												=				outputs.logits

						# verify the logits
						__lowercase				:		Optional[Any]												=				torch.Size((1, 2_1, 3_2, 3_2)						)
						self.assertEqual(logits.shape    ,   lowercase__						)

						__lowercase				:		List[str]												=				torch.tensor(
						    [
						        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
						        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
						        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
						    ]    ,   device=lowercase__    ,   )

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



				@slow
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		int												=				MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
						__lowercase				:		Optional[int]												=				model.to(lowercase__						)

						__lowercase				:		Union[str, Any]												=				MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)

						__lowercase				:		Union[str, Any]												=				prepare_img()
						__lowercase				:		Dict												=				image_processor(images=lowercase__    ,   return_tensors="pt"						).to(lowercase__						)

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

						__lowercase				:		Union[str, Any]												=				outputs.logits.detach().cpu()

						__lowercase				:		Union[str, Any]												=				image_processor.post_process_semantic_segmentation(outputs=lowercase__    ,   target_sizes=[(5_0, 6_0)]						)
						__lowercase				:		str												=				torch.Size((5_0, 6_0)						)
						self.assertEqual(segmentation[0].shape    ,   lowercase__						)

						__lowercase				:		List[Any]												=				image_processor.post_process_semantic_segmentation(outputs=lowercase__						)
						__lowercase				:		List[Any]												=				torch.Size((3_2, 3_2)						)
						self.assertEqual(segmentation[0].shape    ,   lowercase__						)






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->list:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				[0] * len(_lowerCamelCase				)

		for i in range(1,  len(_lowerCamelCase				)				):
				# use last results for better performance - dynamic programming
				__lowercase				:		Optional[Any]												=				prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						__lowercase				:		List[str]												=				prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				__lowercase				:		Any												=				j

		return prefix_result

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		return max(prefix_function(_lowerCamelCase				)				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
