




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       a__   ( snake_case__				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






class       a__   :






						def __init__(       self		,      _A		,      _A=1_3		,      _A=3_2		,      _A=2		,      _A=3		,      _A=6_4_0		,      _A=4		,      _A="silu"		,      _A=3		,      _A=3_2		,      _A=0.1		,      _A=0.1		,      _A=0.1		,      _A=0.02		,      _A=True		,      _A=True		,      _A=1_0		,      _A=None		,      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__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     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTModel(config=_A      )
											model.to(_A      )
											model.eval()
											__lowerCAmelCase           =	model(_A      )
											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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.num_labels
											__lowerCAmelCase           =	MobileViTForSemanticSegmentation(_A      )
											model.to(_A      )
											model.eval()
											__lowerCAmelCase           =	model(_A      )
											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(_A		,      labels=_A      )
											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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__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       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   Any =   (
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_a							:   Union[str, Any] =   (
						    {
						        """feature-extraction""": MobileViTModel,
						        """image-classification""": MobileViTForImageClassification,
						        """image-segmentation""": MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_a							:   int =   False
						_a							:   Union[str, Any] =   False
						_a							:   List[str] =   False
						_a							:   Dict =   False






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTModelTester(self      )
											__lowerCAmelCase           =	MobileViTConfigTester(self		,      config_class=_A		,      has_text_modality=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileViT does not use inputs_embeds"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@unittest.skip(reason="MobileViT does not support input and output embeddings"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@unittest.skip(reason="MobileViT does not output attentions"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase           =	model_class(_A      )
																__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]		,      _A      )






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




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											def check_hidden_states_output(_A		,      _A		,      _A      ):
																__lowerCAmelCase           =	model_class(_A      )
																model.to(_A      )
																model.eval()

																with torch.no_grad():
																					__lowerCAmelCase           =	model(**self._prepare_for_class(_A		,      _A      )      )

																__lowerCAmelCase           =	outputs.hidden_states

																__lowerCAmelCase           =	5
																self.assertEqual(len(_A      )		,      _A      )

																# MobileViT'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(_A      )      ):
																					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(_A		,      _A		,      _A      )

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

																check_hidden_states_output(_A		,      _A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*_A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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







def      _a				(						):
					__lowerCAmelCase           =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


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






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"      ).to(_A      )

											__lowerCAmelCase           =	self.default_image_processor
											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )

											# verify the logits
											__lowerCAmelCase           =	torch.Size((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape		,      _A      )

											__lowerCAmelCase           =	torch.tensor([-1.93_64, -1.23_27, -0.46_53]      ).to(_A      )

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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

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

											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )
											__lowerCAmelCase           =	outputs.logits

											# verify the logits
											__lowerCAmelCase           =	torch.Size((1, 2_1, 3_2, 3_2)      )
											self.assertEqual(logits.shape		,      _A      )

											__lowerCAmelCase           =	torch.tensor(
											    [
											        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
											        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
											        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
											    ]		,      device=_A		,      )

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






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

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

											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="pt"      ).to(_A      )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase           =	model(**_A      )

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

											__lowerCAmelCase           =	image_processor.post_process_semantic_segmentation(outputs=_A		,      target_sizes=[(5_0, 6_0)]      )
											__lowerCAmelCase           =	torch.Size((5_0, 6_0)      )
											self.assertEqual(segmentation[0].shape		,      _A      )

											__lowerCAmelCase           =	image_processor.post_process_semantic_segmentation(outputs=_A      )
											__lowerCAmelCase           =	torch.Size((3_2, 3_2)      )
											self.assertEqual(segmentation[0].shape		,      _A      )










import math







def      _a				(						SCREAMING_SNAKE_CASE_							:    int					):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5				,				int(math.sqrt(SCREAMING_SNAKE_CASE_					) + 1					)				,				6					):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True







def      _a				(						SCREAMING_SNAKE_CASE_							:    float = 0.1					):
					__lowerCAmelCase           =	3
					__lowerCAmelCase           =	3

					while primes / (2 * j - 1) >= ratio:
										for i in range(j * j + j + 1				,				(j + 2) * (j + 2)				,				j + 1					):
															primes += is_prime(SCREAMING_SNAKE_CASE_					)
										j += 2
					return j


if __name__ == "__main__":
					import doctest

					doctest.testmod()




