






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        __UpperCamelCase    (     _lowerCAmelCase   ):







	def  _a   (  self      :							Dict			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.config_class(**self.inputs_dict			)
		self.parent.assertTrue(hasattr(_lowerCAmelCase       ,				"""hidden_sizes"""			)			)
		self.parent.assertTrue(hasattr(_lowerCAmelCase       ,				"""neck_hidden_sizes"""			)			)
		self.parent.assertTrue(hasattr(_lowerCAmelCase       ,				"""num_attention_heads"""			)			)


class        __UpperCamelCase    :







	def __init__(  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							List[str]=13       ,				_lowerCAmelCase      :							int=32       ,				_lowerCAmelCase      :							str=2       ,				_lowerCAmelCase      :							List[Any]=3       ,				_lowerCAmelCase      :							List[str]=640       ,				_lowerCAmelCase      :							List[str]=4       ,				_lowerCAmelCase      :							int="silu"       ,				_lowerCAmelCase      :							Tuple=3       ,				_lowerCAmelCase      :							Union[str, Any]=32       ,				_lowerCAmelCase      :							Tuple=0.1       ,				_lowerCAmelCase      :							Any=0.1       ,				_lowerCAmelCase      :							Optional[int]=0.1       ,				_lowerCAmelCase      :							Union[str, Any]=0.02       ,				_lowerCAmelCase      :							Union[str, Any]=True       ,				_lowerCAmelCase      :							Optional[int]=True       ,				_lowerCAmelCase      :							str=10       ,				_lowerCAmelCase      :							Optional[Any]=None       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  image_size
		__lowercase             =  patch_size
		__lowercase             =  num_channels
		__lowercase             =  last_hidden_size
		__lowercase             =  num_attention_heads
		__lowercase             =  hidden_act
		__lowercase             =  conv_kernel_size
		__lowercase             =  output_stride
		__lowercase             =  hidden_dropout_prob
		__lowercase             =  attention_probs_dropout_prob
		__lowercase             =  classifier_dropout_prob
		__lowercase             =  use_labels
		__lowercase             =  is_training
		__lowercase             =  num_labels
		__lowercase             =  initializer_range
		__lowercase             =  scope







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

		__lowercase             =  self.get_config()

		return config, pixel_values, labels, pixel_labels







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  MobileViTModel(config=_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase			)
		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  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Dict			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  self.num_labels
		__lowercase             =  MobileViTForImageClassification(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							str			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.num_labels
		__lowercase             =  MobileViTForSemanticSegmentation(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase			)
		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             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase			)
		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  _a   (  self      :							List[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  config_and_inputs
		__lowercase             =  {"""pixel_values""": pixel_values}
		return config, inputs_dict


@require_torch
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:List[Any]   =       (
	    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	__snake_case	:List[Any]   =       (
	    {
	        'feature-extraction': MobileViTModel,
	        'image-classification': MobileViTForImageClassification,
	        'image-segmentation': MobileViTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	__snake_case	:List[str]   =       False
	__snake_case	:Tuple   =       False
	__snake_case	:Union[str, Any]   =       False
	__snake_case	:int   =       False







	def  _a   (  self      :							int			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  MobileViTModelTester(self			)
		__lowercase             =  MobileViTConfigTester(self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	@unittest.skip(reason="""MobileViT does not use inputs_embeds"""			)
	def  _a   (  self      :							str			)		-> Dict:
		"""simple docstring"""







		pass







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







		pass







	@unittest.skip(reason="""MobileViT does not output attentions"""			)
	def  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

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

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







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Dict			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> str:
		"""simple docstring"""







		def check_hidden_states_output(_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Any			):
			__lowercase             =  model_class(_lowerCAmelCase			)
			model.to(_lowerCAmelCase			)
			model.eval()

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

			__lowercase             =  outputs.hidden_states

			__lowercase             =  5
			self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)

			# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
			# with the width and height being successively divided by 2.
			__lowercase             =  2
			for i in range(len(_lowerCAmelCase			)			):
				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       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							Any			)		-> Union[str, Any]:
		"""simple docstring"""







		for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__lowercase             =  MobileViTModel.from_pretrained(_lowerCAmelCase			)
			self.assertIsNotNone(_lowerCAmelCase			)





def 				snake_case  (     ):




	'''simple docstring'''







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




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







	@cached_property
	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







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







	@slow
	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""			).to(_lowerCAmelCase			)

		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

		# forward pass
		with torch.no_grad():
			__lowercase             =  model(**_lowerCAmelCase			)

		# verify the logits
		__lowercase             =  torch.Size((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)

		__lowercase             =  torch.tensor([-1.9_364, -1.2_327, -0.4_653]			).to(_lowerCAmelCase			)

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







	@slow
	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""			)
		__lowercase             =  model.to(_lowerCAmelCase			)

		__lowercase             =  MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""			)

		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

		# forward pass
		with torch.no_grad():
			__lowercase             =  model(**_lowerCAmelCase			)
		__lowercase             =  outputs.logits

		# verify the logits
		__lowercase             =  torch.Size((1, 21, 32, 32)			)
		self.assertEqual(logits.shape       ,				_lowerCAmelCase			)

		__lowercase             =  torch.tensor(
		    [
		        [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
		        [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
		        [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
		    ]       ,				device=_lowerCAmelCase       ,				)

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







	@slow
	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""			)
		__lowercase             =  model.to(_lowerCAmelCase			)

		__lowercase             =  MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""			)

		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

		# forward pass
		with torch.no_grad():
			__lowercase             =  model(**_lowerCAmelCase			)

		__lowercase             =  outputs.logits.detach().cpu()

		__lowercase             =  image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase       ,				target_sizes=[(50, 60)]			)
		__lowercase             =  torch.Size((50, 60)			)
		self.assertEqual(segmentation[0].shape       ,				_lowerCAmelCase			)

		__lowercase             =  image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase			)
		__lowercase             =  torch.Size((32, 32)			)
		self.assertEqual(segmentation[0].shape       ,				_lowerCAmelCase			)









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# distance from center
			__lowercase             =  x - ksize // 2
			__lowercase             =  y - ksize // 2

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

			# get kernel x
			__lowercase             =  cos_theta * px + sin_theta * py

			# get kernel y
			__lowercase             =  -sin_theta * px + cos_theta * py

			# fill kernel
			__lowercase             =  np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

	return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						__UpperCamelCase      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

						imshow("""Original""", gray)
						imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

						waitKey(0)

