




"""simple docstring"""







import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor




class       lowerCAmelCase__       (			_A    ):


				"""simple docstring"""





				def       snake_case    (     self					:	int						):
						__lowercase				:		int												=				self.config_class(**self.inputs_dict						)
						self.parent.assertTrue(hasattr(lowercase__    ,   "tf_padding"						)						)
						self.parent.assertTrue(hasattr(lowercase__    ,   "depth_multiplier"						)						)





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	Any    ,   lowercase__					:	int    ,   lowercase__					:	str=1_3    ,   lowercase__					:	List[Any]=3    ,   lowercase__					:	Optional[int]=3_2    ,   lowercase__					:	Optional[Any]=0.2_5    ,   lowercase__					:	Optional[int]=8    ,   lowercase__					:	Dict=8    ,   lowercase__					:	Tuple=6    ,   lowercase__					:	Union[str, Any]=3_2    ,   lowercase__					:	List[str]=True    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	Optional[Any]=True    ,   lowercase__					:	Union[str, Any]="relu6"    ,   lowercase__					:	Optional[int]=1_2_8_0    ,   lowercase__					:	Any=0.1    ,   lowercase__					:	int=0.0_2    ,   lowercase__					:	str=True    ,   lowercase__					:	Dict=True    ,   lowercase__					:	str=1_0    ,   lowercase__					:	Optional[int]=None    ,   ):
						__lowercase				:		Tuple												=				parent
						__lowercase				:		str												=				batch_size
						__lowercase				:		Union[str, Any]												=				num_channels
						__lowercase				:		Optional[int]												=				image_size
						__lowercase				:		str												=				depth_multiplier
						__lowercase				:		List[Any]												=				depth_divisible_by
						__lowercase				:		Union[str, Any]												=				min_depth
						__lowercase				:		Dict												=				expand_ratio
						__lowercase				:		Any												=				tf_padding
						__lowercase				:		Optional[Any]												=				output_stride
						__lowercase				:		Optional[int]												=				first_layer_is_expansion
						__lowercase				:		List[Any]												=				finegrained_output
						__lowercase				:		str												=				hidden_act
						__lowercase				:		List[Any]												=				last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier						)
						__lowercase				:		Union[str, Any]												=				classifier_dropout_prob
						__lowercase				:		Optional[Any]												=				use_labels
						__lowercase				:		Optional[Any]												=				is_training
						__lowercase				:		Dict												=				num_labels
						__lowercase				:		Union[str, Any]												=				initializer_range
						__lowercase				:		Dict												=				scope



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

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

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

						return config, pixel_values, labels, pixel_labels



				def       snake_case    (     self					:	Tuple						):
						return MobileNetVaConfig(
						    num_channels=self.num_channels    ,   image_size=self.image_size    ,   depth_multiplier=self.depth_multiplier    ,   depth_divisible_by=self.depth_divisible_by    ,   min_depth=self.min_depth    ,   expand_ratio=self.expand_ratio    ,   output_stride=self.output_stride    ,   first_layer_is_expansion=self.first_layer_is_expansion    ,   finegrained_output=self.finegrained_output    ,   hidden_act=self.hidden_act    ,   tf_padding=self.tf_padding    ,   classifier_dropout_prob=self.classifier_dropout_prob    ,   initializer_range=self.initializer_range    ,   )



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	List[Any]    ,   lowercase__					:	List[Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	Any						):
						__lowercase				:		Dict												=				MobileNetVaModel(config=lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		Union[str, Any]												=				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,
						    )    ,   )
						self.parent.assertEqual(
						    result.pooler_output.shape    ,   (self.batch_size, self.last_hidden_size)    ,   )



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



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	List[str]						):
						__lowercase				:		str												=				self.num_labels
						__lowercase				:		List[str]												=				MobileNetVaForSemanticSegmentation(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		Optional[Any]												=				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					:	Optional[int]						):
						__lowercase				:		List[Any]												=				self.prepare_config_and_inputs()
						__lowercase				:		int												=				config_and_inputs
						__lowercase				:		List[str]												=				{"pixel_values": pixel_values}
						return config, inputs_dict





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


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      (
				    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__UpperCAmelCase :     Dict       =      (
				    {
				        "feature-extraction": MobileNetVaModel,
				        "image-classification": MobileNetVaForImageClassification,
				        "image-segmentation": MobileNetVaForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCAmelCase :     Any       =      False
				__UpperCAmelCase :     Dict       =      False
				__UpperCAmelCase :     Any       =      False
				__UpperCAmelCase :     Union[str, Any]       =      False



				def       snake_case    (     self					:	int						):
						__lowercase				:		List[str]												=				MobileNetVaModelTester(self						)
						__lowercase				:		int												=				MobileNetVaConfigTester(self    ,   config_class=lowercase__    ,   has_text_modality=lowercase__						)



				def       snake_case    (     self					:	List[str]						):
						self.config_tester.run_common_tests()



				@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"						)
				def       snake_case    (     self					:	Dict						):
						pass



				@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"						)
				def       snake_case    (     self					:	Any						):
						pass



				@unittest.skip(reason="MobileNetV2 does not output attentions"						)
				def       snake_case    (     self					:	List[Any]						):
						pass



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		str												=				self.model_tester.prepare_config_and_inputs_for_common()

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

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



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



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

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

								__lowercase				:		Dict												=				outputs.hidden_states

								__lowercase				:		List[Any]												=				1_6
								self.assertEqual(len(lowercase__						)    ,   lowercase__						)

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

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

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

								check_hidden_states_output(lowercase__    ,   lowercase__    ,   lowercase__						)



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



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



				@slow
				def       snake_case    (     self					:	Optional[Any]						):
						for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase				:		str												=				MobileNetVaModel.from_pretrained(lowercase__						)
								self.assertIsNotNone(lowercase__						)





def 						snake_case__       (					)			->Tuple:






		"""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					:	List[Any]						):
						return (
						    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"						) if is_vision_available() else None
						)



				@slow
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Union[str, Any]												=				MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"						).to(lowercase__						)

						__lowercase				:		int												=				self.default_image_processor
						__lowercase				:		Any												=				prepare_img()
						__lowercase				:		int												=				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				:		Optional[Any]												=				torch.Size((1, 1_0_0_1)						)
						self.assertEqual(outputs.logits.shape    ,   lowercase__						)

						__lowercase				:		Any												=				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]						).to(lowercase__						)

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



				@slow
				def       snake_case    (     self					:	Dict						):
						__lowercase				:		List[Any]												=				MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)
						__lowercase				:		Dict												=				model.to(lowercase__						)

						__lowercase				:		List[str]												=				MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)

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

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

						# verify the logits
						__lowercase				:		Dict												=				torch.Size((1, 2_1, 6_5, 6_5)						)
						self.assertEqual(logits.shape    ,   lowercase__						)

						__lowercase				:		Union[str, Any]												=				torch.tensor(
						    [
						        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
						        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
						        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
						    ]    ,   device=lowercase__    ,   )

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






"""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()
