



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     ):



	def 			__A							(       self		):
							A__							=       self.config_class(**self.inputs_dict		)
							self.parent.assertTrue(hasattr(UpperCAmelCase__			,	"width_multiplier"		)		)






class      UpperCamelCase    :



	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			,	):
							A__							=       parent
							A__							=       batch_size
							A__							=       image_size
							A__							=       patch_size
							A__							=       num_channels
							A__							=       make_divisible(512 * width_multiplier			,	divisor=8		)
							A__							=       hidden_act
							A__							=       conv_kernel_size
							A__							=       output_stride
							A__							=       classifier_dropout_prob
							A__							=       use_labels
							A__							=       is_training
							A__							=       num_labels
							A__							=       initializer_range
							A__							=       scope
							A__							=       width_multiplier
							A__							=       ffn_dropout
							A__							=       attn_dropout



	def 			__A							(       self		):
							A__							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

							A__							=       self.get_config()

							return config, pixel_values, labels, pixel_labels



	def 			__A							(       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       MobileViTVaModel(config=UpperCAmelCase__		)
							model.to(UpperCAmelCase__		)
							model.eval()
							A__							=       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.num_labels
							A__							=       MobileViTVaForImageClassification(UpperCAmelCase__		)
							model.to(UpperCAmelCase__		)
							model.eval()
							A__							=       model(UpperCAmelCase__			,	labels=UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.num_labels
							A__							=       MobileViTVaForSemanticSegmentation(UpperCAmelCase__		)
							model.to(UpperCAmelCase__		)
							model.eval()
							A__							=       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__							=       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 			__A							(       self		):
							A__							=       self.prepare_config_and_inputs()
							A__						,       A__						,       A__						,       A__							=       config_and_inputs
							A__							=       {"pixel_values": pixel_values}
							return config, inputs_dict






@require_torch
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	(
	    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)

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

	lowerCAmelCase	:   Tuple				=	False
	lowerCAmelCase	:   Optional[int]				=	False
	lowerCAmelCase	:   Union[str, Any]				=	False
	lowerCAmelCase	:   str				=	False



	def 			__A							(       self		):
							A__							=       MobileViTVaModelTester(self		)
							A__							=       MobileViTVaConfigTester(self			,	config_class=UpperCAmelCase__			,	has_text_modality=UpperCAmelCase__		)



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



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



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



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



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



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



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

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

													A__							=       ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	def 			__A							(       self		):
							def check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       model_class(UpperCAmelCase__		)
													model.to(UpperCAmelCase__		)
													model.eval()

													with torch.no_grad():
																			A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)

													A__							=       outputs.hidden_states

													A__							=       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.
													A__							=       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__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       True
													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

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

													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A__							=       MobileViTVaModel.from_pretrained(UpperCAmelCase__		)
													self.assertIsNotNone(UpperCAmelCase__		)





def  UpperCamelCase						(       )->       int:





						"""simple docstring"""





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






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



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



	@slow
	def 			__A							(       self		):
							A__							=       MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		).to(
							    UpperCAmelCase__		)

							A__							=       self.default_image_processor
							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="pt"		).to(UpperCAmelCase__		)

							# forward pass
							with torch.no_grad():
													A__							=       model(**UpperCAmelCase__		)

							# verify the logits
							A__							=       torch.Size((1, 1_000)		)
							self.assertEqual(outputs.logits.shape			,	UpperCAmelCase__		)

							A__							=       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 			__A							(       self		):
							A__							=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
							A__							=       model.to(UpperCAmelCase__		)

							A__							=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="pt"		).to(UpperCAmelCase__		)

							# forward pass
							with torch.no_grad():
													A__							=       model(**UpperCAmelCase__		)
							A__							=       outputs.logits

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

							A__							=       torch.tensor(
							    [
							        [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
							        [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
							        [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
							    ]			,	device=UpperCAmelCase__			,	)

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



	@slow
	def 			__A							(       self		):
							A__							=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
							A__							=       model.to(UpperCAmelCase__		)

							A__							=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="pt"		).to(UpperCAmelCase__		)

							# forward pass
							with torch.no_grad():
													A__							=       model(**UpperCAmelCase__		)

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

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

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










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





