
"""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_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.config_class(**self.inputs_dict					)
											self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """hidden_sizes"""					)					)
											self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """neck_hidden_sizes"""					)					)
											self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """num_attention_heads"""					)					)






class 			lowerCAmelCase_		:


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=13     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=6_40     , SCREAMING_SNAKE_CASE__=4     , SCREAMING_SNAKE_CASE__="silu"     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=None     , )      ->	List[Any]:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Any     =     None
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ids_tensor([self.batch_size]     , self.num_labels					)
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     ids_tensor([self.batch_size, self.image_size, self.image_size]     , self.num_labels					)

											SCREAMING_SNAKE_CASE__		: str     =     self.get_config()

											return config, pixel_values, labels, pixel_labels



				def 			__magic_name__					(self					)      ->	str:



											"""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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     MobileViTModel(config=SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: Any     =     model(SCREAMING_SNAKE_CASE__					)
											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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.num_labels
											SCREAMING_SNAKE_CASE__		: Tuple     =     MobileViTForImageClassification(SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__     , labels=SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.num_labels
											SCREAMING_SNAKE_CASE__		: List[str]     =     MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: str     =     model(SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(
											    result.logits.shape     , (
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     , )
											SCREAMING_SNAKE_CASE__		: List[Any]     =     model(SCREAMING_SNAKE_CASE__     , labels=SCREAMING_SNAKE_CASE__					)
											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 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




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






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


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     (
				    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase   :		Optional[int]       =     (
				    {
				        '''feature-extraction''': MobileViTModel,
				        '''image-classification''': MobileViTForImageClassification,
				        '''image-segmentation''': MobileViTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase   :		Tuple       =     False
				__UpperCamelCase   :		Optional[Any]       =     False
				__UpperCamelCase   :		Any       =     False
				__UpperCamelCase   :		Union[str, Any]       =     False



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     MobileViTModelTester(self					)
											SCREAMING_SNAKE_CASE__		: str     =     MobileViTConfigTester(self     , config_class=SCREAMING_SNAKE_CASE__     , has_text_modality=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											self.config_tester.run_common_tests()



				@unittest.skip(reason="""MobileViT does not use inputs_embeds"""					)
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											pass



				@unittest.skip(reason="""MobileViT does not support input and output embeddings"""					)
				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											pass



				@unittest.skip(reason="""MobileViT does not output attentions"""					)
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: int     =     self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE__		: List[Any]     =     ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]     , SCREAMING_SNAKE_CASE__					)



				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											def check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     model_class(SCREAMING_SNAKE_CASE__					)
																		model.to(SCREAMING_SNAKE_CASE__					)
																		model.eval()

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

																		SCREAMING_SNAKE_CASE__		: str     =     outputs.hidden_states

																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     5
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					)

																		# 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(SCREAMING_SNAKE_CASE__					)					):
																									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[Any]     =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

																		check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__					)



				@slow
				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     MobileViTModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)






def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							return image



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


				"""simple docstring"""



				@cached_property
				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""					) if is_vision_available() else None



				@slow
				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""					).to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.default_image_processor
											SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_img()
											SCREAMING_SNAKE_CASE__		: int     =     image_processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: int     =     model(**SCREAMING_SNAKE_CASE__					)

											# verify the logits
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.Size((1, 10_00)					)
											self.assertEqual(outputs.logits.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.tensor([-1.9364, -1.2327, -0.4653]					).to(SCREAMING_SNAKE_CASE__					)

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



				@slow
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)
											SCREAMING_SNAKE_CASE__		: str     =     model.to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

											SCREAMING_SNAKE_CASE__		: str     =     prepare_img()
											SCREAMING_SNAKE_CASE__		: Tuple     =     image_processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: List[str]     =     model(**SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     outputs.logits

											# verify the logits
											SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.Size((1, 21, 32, 32)					)
											self.assertEqual(logits.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.tensor(
											    [
											        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
											        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
											        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
											    ]     , device=SCREAMING_SNAKE_CASE__     , )

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



				@slow
				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     model.to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: int     =     MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

											SCREAMING_SNAKE_CASE__		: Any     =     prepare_img()
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: List[str]     =     outputs.logits.detach().cpu()

											SCREAMING_SNAKE_CASE__		: str     =     image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__     , target_sizes=[(50, 60)]					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.Size((50, 60)					)
											self.assertEqual(segmentation[0].shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.Size((32, 32)					)
											self.assertEqual(segmentation[0].shape     , SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




