




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    (		_lowerCamelCase						):






			'''simple docstring'''





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






			'''simple docstring'''





			def __init__( self			:   Dict       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Optional[Any]=13       ,						lowerCamelCase_			:   Tuple=32       ,						lowerCamelCase_			:   Any=2       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						lowerCamelCase_			:   Optional[int]=640       ,						lowerCamelCase_			:   Tuple=4       ,						lowerCamelCase_			:   str="silu"       ,						lowerCamelCase_			:   Optional[int]=3       ,						lowerCamelCase_			:   Tuple=32       ,						lowerCamelCase_			:   Dict=0.1       ,						lowerCamelCase_			:   Optional[int]=0.1       ,						lowerCamelCase_			:   str=0.1       ,						lowerCamelCase_			:   Optional[int]=0.0_2       ,						lowerCamelCase_			:   Union[str, Any]=True       ,						lowerCamelCase_			:   List[Any]=True       ,						lowerCamelCase_			:   Any=10       ,						lowerCamelCase_			:   str=None       ,						)						->   Union[str, Any]:
								__magic_name__							:	Dict    							=							parent
								__magic_name__							:	int    							=							batch_size
								__magic_name__							:	str    							=							image_size
								__magic_name__							:	Optional[Any]    							=							patch_size
								__magic_name__							:	Optional[int]    							=							num_channels
								__magic_name__							:	str    							=							last_hidden_size
								__magic_name__							:	List[Any]    							=							num_attention_heads
								__magic_name__							:	Optional[int]    							=							hidden_act
								__magic_name__							:	Dict    							=							conv_kernel_size
								__magic_name__							:	List[str]    							=							output_stride
								__magic_name__							:	Union[str, Any]    							=							hidden_dropout_prob
								__magic_name__							:	Optional[int]    							=							attention_probs_dropout_prob
								__magic_name__							:	Optional[Any]    							=							classifier_dropout_prob
								__magic_name__							:	Any    							=							use_labels
								__magic_name__							:	Tuple    							=							is_training
								__magic_name__							:	Union[str, Any]    							=							num_labels
								__magic_name__							:	str    							=							initializer_range
								__magic_name__							:	Any    							=							scope

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Union[str, Any]:
								__magic_name__							:	Union[str, Any]    							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								__magic_name__							:	str    							=							None
								__magic_name__							:	Optional[int]    							=							None
								if self.use_labels:
													__magic_name__							:	int    							=							ids_tensor([self.batch_size]       ,						self.num_labels  )
													__magic_name__							:	Dict    							=							ids_tensor([self.batch_size, self.image_size, self.image_size]       ,						self.num_labels  )

								__magic_name__							:	List[str]    							=							self.get_config()

								return config, pixel_values, labels, pixel_labels

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   List[str]:
								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 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   List[Any]  )						->   List[Any]:
								__magic_name__							:	Optional[Any]    							=							MobileViTModel(config=lowerCamelCase_  )
								model.to(lowerCamelCase_  )
								model.eval()
								__magic_name__							:	str    							=							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 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   int  )						->   int:
								__magic_name__							:	Any    							=							self.num_labels
								__magic_name__							:	Optional[Any]    							=							MobileViTForImageClassification(lowerCamelCase_  )
								model.to(lowerCamelCase_  )
								model.eval()
								__magic_name__							:	List[Any]    							=							model(lowerCamelCase_       ,						labels=lowerCamelCase_  )
								self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels)  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   List[str]  )						->   Optional[int]:
								__magic_name__							:	str    							=							self.num_labels
								__magic_name__							:	List[str]    							=							MobileViTForSemanticSegmentation(lowerCamelCase_  )
								model.to(lowerCamelCase_  )
								model.eval()
								__magic_name__							:	List[str]    							=							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,
								    )       ,						)
								__magic_name__							:	str    							=							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 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[int]:
								__magic_name__							:	Optional[Any]    							=							self.prepare_config_and_inputs()
								__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							:	str    							=							config_and_inputs
								__magic_name__							:	str    							=							{'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=(
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__								=(
			    {
			        '''feature-extraction''': MobileViTModel,
			        '''image-classification''': MobileViTForImageClassification,
			        '''image-segmentation''': MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase__								=False
			UpperCamelCase__								=False
			UpperCamelCase__								=False
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
								__magic_name__							:	Tuple    							=							MobileViTModelTester(self  )
								__magic_name__							:	List[str]    							=							MobileViTConfigTester(self       ,						config_class=lowerCamelCase_       ,						has_text_modality=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:
								self.config_tester.run_common_tests()

			@unittest.skip(reason='''MobileViT does not use inputs_embeds'''  )
			def 							UpperCAmelCase__       ( self			:   Dict  )						->   List[Any]:
								pass

			@unittest.skip(reason='''MobileViT does not support input and output embeddings'''  )
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								pass

			@unittest.skip(reason='''MobileViT does not output attentions'''  )
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Any:
								pass

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   str:
								__magic_name__							,    __magic_name__							:	int    							=							self.model_tester.prepare_config_and_inputs_for_common()

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

													__magic_name__							:	Union[str, Any]    							=							['''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 							UpperCAmelCase__       ( self			:   Any  )						->   List[Any]:
								pass

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Any:
								__magic_name__							:	str    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Union[str, Any]:
								def check_hidden_states_output(lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any  ):
													__magic_name__							:	Union[str, Any]    							=							model_class(lowerCamelCase_  )
													model.to(lowerCamelCase_  )
													model.eval()

													with torch.no_grad():
																		__magic_name__							:	Optional[int]    							=							model(**self._prepare_for_class(lowerCamelCase_       ,						lowerCamelCase_  )  )

													__magic_name__							:	Optional[int]    							=							outputs.hidden_states

													__magic_name__							:	List[Any]    							=							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.
													__magic_name__							:	List[str]    							=							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  )

								__magic_name__							,    __magic_name__							:	Tuple    							=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__magic_name__							:	List[str]    							=							True
													check_hidden_states_output(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__magic_name__							:	Union[str, Any]    							=							True

													check_hidden_states_output(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any  )						->   Any:
								__magic_name__							:	List[Any]    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Union[str, Any]:
								__magic_name__							:	Tuple    							=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_  )


			@slow
			def 							UpperCAmelCase__       ( self			:   Any  )						->   Any:
								for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__							:	Union[str, Any]    							=							MobileViTModel.from_pretrained(lowerCamelCase_  )
													self.assertIsNotNone(lowerCamelCase_  )







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   int:
								return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''  ) if is_vision_available() else None

			@slow
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   List[str]:
								__magic_name__							:	Union[str, Any]    							=							MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''  ).to(lowerCamelCase_  )

								__magic_name__							:	str    							=							self.default_image_processor
								__magic_name__							:	Optional[int]    							=							prepare_img()
								__magic_name__							:	Tuple    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''pt'''  ).to(lowerCamelCase_  )

								# forward pass
								with torch.no_grad():
													__magic_name__							:	Dict    							=							model(**lowerCamelCase_  )

								# verify the logits
								__magic_name__							:	Any    							=							torch.Size((1, 1000)  )
								self.assertEqual(outputs.logits.shape       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]  ).to(lowerCamelCase_  )

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

			@slow
			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Optional[Any]:
								__magic_name__							:	Any    							=							MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''  )
								__magic_name__							:	Any    							=							model.to(lowerCamelCase_  )

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

								__magic_name__							:	List[Any]    							=							prepare_img()
								__magic_name__							:	int    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''pt'''  ).to(lowerCamelCase_  )

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

								# verify the logits
								__magic_name__							:	str    							=							torch.Size((1, 21, 32, 32)  )
								self.assertEqual(logits.shape       ,						lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							torch.tensor(
								    [
								        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
								        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
								        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
								    ]       ,						device=lowerCamelCase_       ,						)

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


			@slow
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:
								__magic_name__							:	List[Any]    							=							MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''  )
								__magic_name__							:	Union[str, Any]    							=							model.to(lowerCamelCase_  )

								__magic_name__							:	int    							=							MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''  )

								__magic_name__							:	Union[str, Any]    							=							prepare_img()
								__magic_name__							:	Union[str, Any]    							=							image_processor(images=lowerCamelCase_       ,						return_tensors='''pt'''  ).to(lowerCamelCase_  )

								# forward pass
								with torch.no_grad():
													__magic_name__							:	Dict    							=							model(**lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							outputs.logits.detach().cpu()

								__magic_name__							:	Optional[Any]    							=							image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_       ,						target_sizes=[(50, 60)]  )
								__magic_name__							:	Any    							=							torch.Size((50, 60)  )
								self.assertEqual(segmentation[0].shape       ,						lowerCamelCase_  )

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









import functools







def 		lowercase__   (		__A:	list[int]							,__A:	list[int]				):





					'''simple docstring'''






					if not isinstance(__A							,__A				) or not all(isinstance(__A							,__A				) for day in days				):
										raise ValueError('''The parameter days should be a list of integers'''				)

					if len(__A				) != 3 or not all(isinstance(__A							,__A				) for cost in costs				):
										raise ValueError('''The parameter costs should be a list of three integers'''				)

					if len(__A				) == 0:
										return 0

					if min(__A				) <= 0:
										raise ValueError('''All days elements should be greater than 0'''				)

					if max(__A				) >= 3_6_6:
										raise ValueError('''All days elements should be less than 366'''				)

					__magic_name__							:	Tuple    							=							set(__A				)

					@functools.cache
					def dynamic_programming(__A:	int				) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1				)

										return min(
										    costs[0] + dynamic_programming(index + 1				)							,costs[1] + dynamic_programming(index + 7				)							,costs[2] + dynamic_programming(index + 3_0				)							,)

					return dynamic_programming(1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



