






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      SCREAMING_SNAKE_CASE  (				lowercase_						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.config_class(**self.inputs_dict							)
		self.parent.assertTrue(hasattr(UpperCamelCase__     ,     """hidden_sizes"""							)							)
		self.parent.assertTrue(hasattr(UpperCamelCase__     ,     """neck_hidden_sizes"""							)							)
		self.parent.assertTrue(hasattr(UpperCamelCase__     ,     """num_attention_heads"""							)							)






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=640     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_="silu"     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=None     ,     ):
		A_		:						Optional[Any]	    =   parent
		A_		:						Tuple	    =   batch_size
		A_		:						Union[str, Any]	    =   image_size
		A_		:						List[Any]	    =   patch_size
		A_		:						List[str]	    =   num_channels
		A_		:						Any	    =   last_hidden_size
		A_		:						Any	    =   num_attention_heads
		A_		:						Union[str, Any]	    =   hidden_act
		A_		:						Union[str, Any]	    =   conv_kernel_size
		A_		:						Optional[int]	    =   output_stride
		A_		:						Any	    =   hidden_dropout_prob
		A_		:						List[Any]	    =   attention_probs_dropout_prob
		A_		:						Any	    =   classifier_dropout_prob
		A_		:						Optional[int]	    =   use_labels
		A_		:						int	    =   is_training
		A_		:						Any	    =   num_labels
		A_		:						List[str]	    =   initializer_range
		A_		:						Union[str, Any]	    =   scope





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

		A_		:						str	    =   self.get_config()

		return config, pixel_values, labels, pixel_labels





	def    lowerCamelCase(self							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   MobileViTModel(config=UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						int	    =   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     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   self.num_labels
		A_		:						Union[str, Any]	    =   MobileViTForImageClassification(UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						Optional[Any]	    =   model(UpperCamelCase__     ,     labels=UpperCamelCase__							)
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[int]	    =   self.num_labels
		A_		:						List[str]	    =   MobileViTForSemanticSegmentation(UpperCamelCase__							)
		model.to(UpperCamelCase__							)
		model.eval()
		A_		:						List[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,
		    )     ,     )
		A_		:						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							):
		A_		:						Tuple	    =   self.prepare_config_and_inputs()
		A_    ,     A_    ,     A_    ,     A_		:						Optional[int]	    =   config_and_inputs
		A_		:						List[Any]	    =   {"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE  (				lowercase_       ,       lowercase_       ,       unittest.TestCase						):





	"""simple docstring"""




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

	_A       :				int    =				False
	_A       :				int    =				False
	_A       :				Optional[int]    =				False
	_A       :				List[str]    =				False





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   MobileViTModelTester(self							)
		A_		:						List[Any]	    =   MobileViTConfigTester(self     ,     config_class=UpperCamelCase__     ,     has_text_modality=UpperCamelCase__							)





	def    lowerCamelCase(self							):
		self.config_tester.run_common_tests()





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





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





	@unittest.skip(reason="""MobileViT does not output attentions"""							)
	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		A_    ,     A_		:						List[Any]	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

			A_		:						Optional[int]	    =   ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]     ,     UpperCamelCase__							)





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





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase__							)





	def    lowerCamelCase(self							):







		def check_hidden_states_output(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
			A_		:						Optional[Any]	    =   model_class(UpperCamelCase__							)
			model.to(UpperCamelCase__							)
			model.eval()

			with torch.no_grad():
				A_		:						Union[str, Any]	    =   model(**self._prepare_for_class(UpperCamelCase__     ,     UpperCamelCase__							)							)

			A_		:						List[Any]	    =   outputs.hidden_states

			A_		:						Union[str, Any]	    =   5
			self.assertEqual(len(UpperCamelCase__							)     ,     UpperCamelCase__							)

			# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
			# with the width and height being successively divided by 2.
			A_		:						Tuple	    =   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							)

		A_    ,     A_		:						Dict	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

			check_hidden_states_output(UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__							)





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__							)





	@slow
	def    lowerCamelCase(self							):







		for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:						Any	    =   MobileViTModel.from_pretrained(UpperCamelCase__							)
			self.assertIsNotNone(UpperCamelCase__							)






def 			__UpperCamelCase	(		):
	A_		:						List[str]	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




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





	"""simple docstring"""




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





	@slow
	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""							).to(UpperCamelCase__							)

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

		# forward pass
		with torch.no_grad():
			A_		:						List[Any]	    =   model(**UpperCamelCase__							)

		# verify the logits
		A_		:						Dict	    =   torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape     ,     UpperCamelCase__							)

		A_		:						Optional[Any]	    =   torch.tensor([-1.9364, -1.2327, -0.4653]							).to(UpperCamelCase__							)

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





	@slow
	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)
		A_		:						Any	    =   model.to(UpperCamelCase__							)

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

		A_		:						List[Any]	    =   prepare_img()
		A_		:						Optional[int]	    =   image_processor(images=UpperCamelCase__     ,     return_tensors="""pt"""							).to(UpperCamelCase__							)

		# forward pass
		with torch.no_grad():
			A_		:						List[str]	    =   model(**UpperCamelCase__							)
		A_		:						int	    =   outputs.logits

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

		A_		:						str	    =   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=UpperCamelCase__     ,     )

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





	@slow
	def    lowerCamelCase(self							):
		A_		:						Any	    =   MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)
		A_		:						Optional[Any]	    =   model.to(UpperCamelCase__							)

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

		A_		:						Optional[int]	    =   prepare_img()
		A_		:						Optional[int]	    =   image_processor(images=UpperCamelCase__     ,     return_tensors="""pt"""							).to(UpperCamelCase__							)

		# forward pass
		with torch.no_grad():
			A_		:						Tuple	    =   model(**UpperCamelCase__							)

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

		A_		:						Union[str, Any]	    =   image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__     ,     target_sizes=[(50, 60)]							)
		A_		:						Union[str, Any]	    =   torch.Size((50, 60)							)
		self.assertEqual(segmentation[0].shape     ,     UpperCamelCase__							)

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









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

