






"""simple docstring"""





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       __A		(   SCREAMING_SNAKE_CASE_			):

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											__magic_name__:	int							=       self.config_class(**self.inputs_dict					)
											self.parent.assertTrue(hasattr(__snake_case     ,		"""width_multiplier"""					)					)



class       __A		:

					def __init__(			self  :      Tuple     ,		__snake_case  :      List[str]     ,		__snake_case  :      int=1_3     ,		__snake_case  :      Optional[int]=6_4     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      Tuple=3     ,		__snake_case  :      Any="swish"     ,		__snake_case  :      int=3     ,		__snake_case  :      int=3_2     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      int=0.02     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      Optional[Any]=1_0     ,		__snake_case  :      Tuple=None     ,		__snake_case  :      Any=0.25     ,		__snake_case  :      List[Any]=0.0     ,		__snake_case  :      Dict=0.0     ,		)			->							Optional[int]:
											__magic_name__:	int							=       parent
											__magic_name__:	Union[str, Any]							=       batch_size
											__magic_name__:	Optional[Any]							=       image_size
											__magic_name__:	Optional[Any]							=       patch_size
											__magic_name__:	Union[str, Any]							=       num_channels
											__magic_name__:	Optional[Any]							=       make_divisible(5_1_2 * width_multiplier     ,		divisor=8					)
											__magic_name__:	List[Any]							=       hidden_act
											__magic_name__:	int							=       conv_kernel_size
											__magic_name__:	Tuple							=       output_stride
											__magic_name__:	Dict							=       classifier_dropout_prob
											__magic_name__:	Optional[Any]							=       use_labels
											__magic_name__:	List[str]							=       is_training
											__magic_name__:	List[str]							=       num_labels
											__magic_name__:	str							=       initializer_range
											__magic_name__:	Dict							=       scope
											__magic_name__:	str							=       width_multiplier
											__magic_name__:	str							=       ffn_dropout
											__magic_name__:	Optional[Any]							=       attn_dropout

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Dict							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

											__magic_name__:	List[str]							=       None
											__magic_name__:	Tuple							=       None
											if self.use_labels:
																	__magic_name__:	List[str]							=       ids_tensor([self.batch_size]     ,		self.num_labels					)
																	__magic_name__:	Any							=       ids_tensor([self.batch_size, self.image_size, self.image_size]     ,		self.num_labels					)

											__magic_name__:	Dict							=       self.get_config()

											return config, pixel_values, labels, pixel_labels

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Any:
											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   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      str					)			->							Optional[Any]:
											__magic_name__:	Optional[int]							=       MobileViTVaModel(config=__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	Optional[int]							=       model(__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   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							Any:
											__magic_name__:	Union[str, Any]							=       self.num_labels
											__magic_name__:	List[str]							=       MobileViTVaForImageClassification(__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	Optional[int]							=       model(__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Union[str, Any]					)			->							Dict:
											__magic_name__:	Tuple							=       self.num_labels
											__magic_name__:	Any							=       MobileViTVaForSemanticSegmentation(__snake_case					)
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	List[Any]							=       model(__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,
											    )     ,		)
											__magic_name__:	Any							=       model(__snake_case     ,		labels=__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   lowerCamelCase__   (			self  :      Any					)			->							Union[str, Any]:
											__magic_name__:	str							=       self.prepare_config_and_inputs()
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Optional[int]							=       config_and_inputs
											__magic_name__:	Tuple							=       {"""pixel_values""": pixel_values}
											return config, inputs_dict



@require_torch
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				(
					    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)

					UpperCAmelCase__								=				(
					    {
					        "feature-extraction": MobileViTVaModel,
					        "image-classification": MobileViTVaForImageClassification,
					        "image-segmentation": MobileViTVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Any:
											__magic_name__:	List[str]							=       MobileViTVaModelTester(self					)
											__magic_name__:	str							=       MobileViTVaConfigTester(self     ,		config_class=__snake_case     ,		has_text_modality=__snake_case					)

					def   lowerCamelCase__   (			self  :      int					)			->							Dict:
											self.config_tester.run_common_tests()

					@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""					)
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											pass

					@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""					)
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											pass

					@unittest.skip(reason="""MobileViTV2 does not output attentions"""					)
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											pass

					@require_torch_multi_gpu
					@unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""					)
					def   lowerCamelCase__   (			self  :      str					)			->							str:
											pass

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

					def   lowerCamelCase__   (			self  :      Dict					)			->							List[str]:
											__magic_name__,			__magic_name__:	Dict							=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	__magic_name__:	Dict							=       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[Any]:
											__magic_name__:	Any							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[Any]:
											def check_hidden_states_output(__snake_case  :      Union[str, Any]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]					):
																	__magic_name__:	Union[str, Any]							=       model_class(__snake_case					)
																	model.to(__snake_case					)
																	model.eval()

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

																	__magic_name__:	List[Any]							=       outputs.hidden_states

																	__magic_name__:	List[Any]							=       5
																	self.assertEqual(len(__snake_case					)     ,		__snake_case					)

																	# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																	# with the width and height being successively divided by 2.
																	__magic_name__:	int							=       2
																	for i in range(len(__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					)

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

											for model_class in self.all_model_classes:
																	__magic_name__:	Dict							=       True
																	check_hidden_states_output(__snake_case     ,		__snake_case     ,		__snake_case					)

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

																	check_hidden_states_output(__snake_case     ,		__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Union[str, Any]:
											__magic_name__:	Any							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							str:
											__magic_name__:	Dict							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case					)






					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Optional[int]:
											for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Union[str, Any]							=       MobileViTVaModel.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)



def   a    (     )    ->							Any:
						__magic_name__:	Any							=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
						return image



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

					@cached_property
					def   lowerCamelCase__   (			self  :      Dict					)			->							str:
											return (
											    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					)
											    if is_vision_available()
											    else None
											)

					@slow
					def   lowerCamelCase__   (			self  :      Dict					)			->							int:
											__magic_name__:	Any							=       MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					).to(
											    __snake_case					)

											__magic_name__:	Optional[Any]							=       self.default_image_processor
											__magic_name__:	Tuple							=       prepare_img()
											__magic_name__:	str							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Any							=       model(**__snake_case					)

											# verify the logits
											__magic_name__:	List[Any]							=       torch.Size((1, 1_0_0_0)					)
											self.assertEqual(outputs.logits.shape     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]					).to(__snake_case					)

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

					@slow
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Any							=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
											__magic_name__:	List[Any]							=       model.to(__snake_case					)

											__magic_name__:	str							=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

											__magic_name__:	str							=       prepare_img()
											__magic_name__:	str							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Any							=       model(**__snake_case					)
											__magic_name__:	Optional[Any]							=       outputs.logits

											# verify the logits
											__magic_name__:	List[str]							=       torch.Size((1, 2_1, 3_2, 3_2)					)
											self.assertEqual(logits.shape     ,		__snake_case					)

											__magic_name__:	Optional[Any]							=       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=__snake_case     ,		)

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






					@slow
					def   lowerCamelCase__   (			self  :      int					)			->							Optional[Any]:
											__magic_name__:	List[str]							=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
											__magic_name__:	Union[str, Any]							=       model.to(__snake_case					)

											__magic_name__:	List[Any]							=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

											__magic_name__:	str							=       prepare_img()
											__magic_name__:	List[str]							=       image_processor(images=__snake_case     ,		return_tensors="""pt"""					).to(__snake_case					)

											# forward pass
											with torch.no_grad():
																	__magic_name__:	Tuple							=       model(**__snake_case					)

											__magic_name__:	Union[str, Any]							=       outputs.logits.detach().cpu()

											__magic_name__:	Any							=       image_processor.post_process_semantic_segmentation(outputs=__snake_case     ,		target_sizes=[(5_0, 6_0)]					)
											__magic_name__:	Tuple							=       torch.Size((5_0, 6_0)					)
											self.assertEqual(segmentation[0].shape     ,		__snake_case					)

											__magic_name__:	str							=       image_processor.post_process_semantic_segmentation(outputs=__snake_case					)
											__magic_name__:	Optional[int]							=       torch.Size((3_2, 3_2)					)
											self.assertEqual(segmentation[0].shape     ,		__snake_case					)










'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class      UpperCamelCase__					:




					"""simple docstring"""


					@property
					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							return self.get_dummy_input()


					@property
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							if self.block_type == "down":
									return (4, 32, 16, 16)
							elif self.block_type == "mid":
									return (4, 32, 32, 32)
							elif self.block_type == "up":
									return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''						)


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			Dict=True      ,						lowerCamelCase_				:			List[Any]=False      ,						lowerCamelCase_				:			str=False      ,						lowerCamelCase_				:			Optional[int]=False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			4
							SCREAMING_SNAKE_CASE :				Any		=			32
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(32, 32)

							SCREAMING_SNAKE_CASE :				int		=			torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			(batch_size, num_channels) + sizes
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{"""hidden_states""": hidden_states}

							if include_temb:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1_28
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			randn_tensor((batch_size, temb_channels)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							if include_res_hidden_states_tuple:
									SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(1						)
									SCREAMING_SNAKE_CASE :				int		=			(randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						),)

							if include_encoder_hidden_states:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			floats_tensor((batch_size, 32, 32)						).to(lowerCamelCase_						)

							if include_skip_sample:
									SCREAMING_SNAKE_CASE :				str		=			randn_tensor(((batch_size, 3) + sizes)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							return dummy_input


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			{
							    """in_channels""": 32,
							    """out_channels""": 32,
							    """temb_channels""": 1_28,
							}
							if self.block_type == "up":
									SCREAMING_SNAKE_CASE :				List[str]		=			32

							if self.block_type == "mid":
									init_dict.pop("""out_channels"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_input
							return init_dict, inputs_dict


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.block_class(**lowerCamelCase_						)
							unet_block.to(lowerCamelCase_						)
							unet_block.eval()

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				List[str]		=			unet_block(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Dict		=			output[0]

							self.assertEqual(output.shape      ,						self.output_shape						)

							SCREAMING_SNAKE_CASE :				Dict		=			output[0, -1, -3:, -3:]
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.tensor(lowerCamelCase_						).to(lowerCamelCase_						)
							assert torch_all_close(output_slice.flatten()      ,						lowerCamelCase_      ,						atol=5e-3						)



					@unittest.skipIf(torch_device == """mps"""      ,						"""Training is not supported in mps"""						)
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.block_class(**lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.train()
							SCREAMING_SNAKE_CASE :				Tuple		=			model(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			output[0]

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			randn_tensor(output.shape      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.nn.functional.mse_loss(lowerCamelCase_      ,						lowerCamelCase_						)
							loss.backward()



