




import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
		from transformers.models.mobilevitva.modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    make_divisible,
		)


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor


class 							__UpperCamelCase			( __UpperCAmelCase  ):





							'''simple docstring'''



							def       __snake_case		( self						):
														lowerCAmelCase   =		self.config_class(**self.inputs_dict						)
														self.parent.assertTrue(hasattr(UpperCAmelCase_       , '''width_multiplier'''						)						)




class 							__UpperCamelCase			:





							'''simple docstring'''



							def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=64       , UpperCAmelCase_=2       , UpperCAmelCase_=3       , UpperCAmelCase_="swish"       , UpperCAmelCase_=3       , UpperCAmelCase_=32       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.02       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_=10       , UpperCAmelCase_=None       , UpperCAmelCase_=0.25       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , ):
														lowerCAmelCase   =		parent
														lowerCAmelCase   =		batch_size
														lowerCAmelCase   =		image_size
														lowerCAmelCase   =		patch_size
														lowerCAmelCase   =		num_channels
														lowerCAmelCase   =		make_divisible(5_12 * width_multiplier       , divisor=8						)
														lowerCAmelCase   =		hidden_act
														lowerCAmelCase   =		conv_kernel_size
														lowerCAmelCase   =		output_stride
														lowerCAmelCase   =		classifier_dropout_prob
														lowerCAmelCase   =		use_labels
														lowerCAmelCase   =		is_training
														lowerCAmelCase   =		num_labels
														lowerCAmelCase   =		initializer_range
														lowerCAmelCase   =		scope
														lowerCAmelCase   =		width_multiplier
														lowerCAmelCase   =		ffn_dropout
														lowerCAmelCase   =		attn_dropout



							def       __snake_case		( self						):
														lowerCAmelCase   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

														lowerCAmelCase   =		self.get_config()

														return config, pixel_values, labels, pixel_labels



							def       __snake_case		( self						):
														return MobileViTVaConfig(
														    image_size=self.image_size       , patch_size=self.patch_size       , num_channels=self.num_channels       , hidden_act=self.hidden_act       , conv_kernel_size=self.conv_kernel_size       , output_stride=self.output_stride       , classifier_dropout_prob=self.classifier_dropout_prob       , initializer_range=self.initializer_range       , width_multiplier=self.width_multiplier       , ffn_dropout=self.ffn_dropout_prob       , attn_dropout=self.attn_dropout_prob       , )



							def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
														lowerCAmelCase   =		MobileViTVaModel(config=UpperCAmelCase_						)
														model.to(UpperCAmelCase_						)
														model.eval()
														lowerCAmelCase   =		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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
														lowerCAmelCase   =		self.num_labels
														lowerCAmelCase   =		MobileViTVaForImageClassification(UpperCAmelCase_						)
														model.to(UpperCAmelCase_						)
														model.eval()
														lowerCAmelCase   =		model(UpperCAmelCase_       , labels=UpperCAmelCase_						)
														self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)						)



							def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
														lowerCAmelCase   =		self.num_labels
														lowerCAmelCase   =		MobileViTVaForSemanticSegmentation(UpperCAmelCase_						)
														model.to(UpperCAmelCase_						)
														model.eval()
														lowerCAmelCase   =		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,
														    )       , )
														lowerCAmelCase   =		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       __snake_case		( self						):
														lowerCAmelCase   =		self.prepare_config_and_inputs()
														lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		config_and_inputs
														lowerCAmelCase   =		{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





							'''simple docstring'''



							__a							: List[str]										=(
							    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)

							__a							: Union[str, Any]										=(
							    {
							        """feature-extraction""": MobileViTVaModel,
							        """image-classification""": MobileViTVaForImageClassification,
							        """image-segmentation""": MobileViTVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__a							: str										=False
							__a							: Dict										=False
							__a							: Union[str, Any]										=False
							__a							: Optional[int]										=False



							def       __snake_case		( self						):
														lowerCAmelCase   =		MobileViTVaModelTester(self						)
														lowerCAmelCase   =		MobileViTVaConfigTester(self       , config_class=UpperCAmelCase_       , has_text_modality=UpperCAmelCase_						)



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



							@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''						)
							def       __snake_case		( self						):
														pass



							@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''						)
							def       __snake_case		( self						):
														pass



							@unittest.skip(reason='''MobileViTV2 does not output attentions'''						)
							def       __snake_case		( self						):
														pass



							@require_torch_multi_gpu
							@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''						)
							def       __snake_case		( self						):
														pass



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



							def       __snake_case		( self						):
														lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase   =		['''pixel_values''']
																					self.assertListEqual(arg_names[:1]       , UpperCAmelCase_						)



							def       __snake_case		( self						):
														lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_						)



							def       __snake_case		( self						):
														def check_hidden_states_output(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
																					lowerCAmelCase   =		model_class(UpperCAmelCase_						)
																					model.to(UpperCAmelCase_						)
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase   =		model(**self._prepare_for_class(UpperCAmelCase_       , UpperCAmelCase_						)						)

																					lowerCAmelCase   =		outputs.hidden_states

																					lowerCAmelCase   =		5
																					self.assertEqual(len(UpperCAmelCase_						)       , UpperCAmelCase_						)

																					# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																					# with the width and height being successively divided by 2.
																					lowerCAmelCase   =		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						)

														lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase   =		True
																					check_hidden_states_output(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)

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

																					check_hidden_states_output(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)



							def       __snake_case		( self						):
														lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_						)



							def       __snake_case		( self						):
														lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_						)






							@slow
							def       __snake_case		( self						):
														for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase   =		MobileViTVaModel.from_pretrained(UpperCAmelCase_						)
																					self.assertIsNotNone(UpperCAmelCase_						)




def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							return image


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





							'''simple docstring'''



							@cached_property
							def       __snake_case		( self						):
														return (
														    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''						)
														    if is_vision_available()
														    else None
														)



							@slow
							def       __snake_case		( self						):
														lowerCAmelCase   =		MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''						).to(
														    UpperCAmelCase_						)

														lowerCAmelCase   =		self.default_image_processor
														lowerCAmelCase   =		prepare_img()
														lowerCAmelCase   =		image_processor(images=UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase   =		model(**UpperCAmelCase_						)

														# verify the logits
														lowerCAmelCase   =		torch.Size((1, 10_00)						)
														self.assertEqual(outputs.logits.shape       , UpperCAmelCase_						)

														lowerCAmelCase   =		torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]						).to(UpperCAmelCase_						)

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



							@slow
							def       __snake_case		( self						):
														lowerCAmelCase   =		MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''						)
														lowerCAmelCase   =		model.to(UpperCAmelCase_						)

														lowerCAmelCase   =		MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''						)

														lowerCAmelCase   =		prepare_img()
														lowerCAmelCase   =		image_processor(images=UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase   =		model(**UpperCAmelCase_						)
														lowerCAmelCase   =		outputs.logits

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

														lowerCAmelCase   =		torch.tensor(
														    [
														        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
														        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
														        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
														    ]       , device=UpperCAmelCase_       , )

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






							@slow
							def       __snake_case		( self						):
														lowerCAmelCase   =		MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''						)
														lowerCAmelCase   =		model.to(UpperCAmelCase_						)

														lowerCAmelCase   =		MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''						)

														lowerCAmelCase   =		prepare_img()
														lowerCAmelCase   =		image_processor(images=UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase   =		model(**UpperCAmelCase_						)

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

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

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









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



