





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  A_     ( self     :					Dict						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.config_class(**self.inputs_dict						)
														self.parent.assertTrue(hasattr(_UpperCAmelCase,			"hidden_sizes"						)						)
														self.parent.assertTrue(hasattr(_UpperCAmelCase,			"neck_hidden_sizes"						)						)
														self.parent.assertTrue(hasattr(_UpperCAmelCase,			"num_attention_heads"						)						)


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Any=1_3,			_UpperCAmelCase     :					str=3_2,			_UpperCAmelCase     :					str=2,			_UpperCAmelCase     :					Any=3,			_UpperCAmelCase     :					List[Any]=6_4_0,			_UpperCAmelCase     :					str=4,			_UpperCAmelCase     :					Optional[int]="silu",			_UpperCAmelCase     :					Any=3,			_UpperCAmelCase     :					Optional[int]=3_2,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					Tuple=0.1,			_UpperCAmelCase     :					Optional[Any]=0.1,			_UpperCAmelCase     :					str=0.02,			_UpperCAmelCase     :					Dict=True,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					Tuple=1_0,			_UpperCAmelCase     :					Optional[Any]=None,			)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						parent
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						num_channels
														SCREAMING_SNAKE_CASE__       :      Tuple     =						last_hidden_size
														SCREAMING_SNAKE_CASE__       :      Any     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						conv_kernel_size
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						output_stride
														SCREAMING_SNAKE_CASE__       :      Any     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      List[str]     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Dict     =						classifier_dropout_prob
														SCREAMING_SNAKE_CASE__       :      List[str]     =						use_labels
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						is_training
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						num_labels
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						scope






							def  A_     ( self     :					List[Any]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      str     =						None
														if self.use_labels:
																					SCREAMING_SNAKE_CASE__       :      str     =						ids_tensor([self.batch_size],			self.num_labels						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ids_tensor([self.batch_size, self.image_size, self.image_size],			self.num_labels						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_config()

														return config, pixel_values, labels, pixel_labels






							def  A_     ( self     :					Dict						)				->			List[str]:





														"""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  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Optional[Any]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						MobileViTModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						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     :					Optional[int],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					int						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.num_labels
														SCREAMING_SNAKE_CASE__       :      Any     =						MobileViTForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      str     =						model(_UpperCAmelCase,			labels=_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)						)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.num_labels
														SCREAMING_SNAKE_CASE__       :      str     =						MobileViTForSemanticSegmentation(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      str     =						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,
														    ),			)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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     :					Union[str, Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						{"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  A_     ( self     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						MobileViTModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						MobileViTConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			Any:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="MobileViT does not use inputs_embeds"						)
							def  A_     ( self     :					str						)				->			Optional[int]:





														"""simple docstring"""
														pass






							@unittest.skip(reason="MobileViT does not support input and output embeddings"						)
							def  A_     ( self     :					Dict						)				->			int:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
							def  A_     ( self     :					int						)				->			Optional[int]:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[Any]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														def check_hidden_states_output(_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str]						):
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model_class(_UpperCAmelCase						)
																					model.to(_UpperCAmelCase						)
																					model.eval()

																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						model(**self._prepare_for_class(_UpperCAmelCase,			_UpperCAmelCase						)						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						outputs.hidden_states

																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						5
																					self.assertEqual(len(_UpperCAmelCase						),			_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						True
																					check_hidden_states_output(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

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

																					check_hidden_states_output(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)






							def  A_     ( self     :					int						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






							def  A_     ( self     :					Dict						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						MobileViTModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[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  A_     ( self     :					Any						)				->			Dict:





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






							@slow
							def  A_     ( self     :					Tuple						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Any     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-1.9364, -1.2327, -0.4653]						).to(_UpperCAmelCase						)

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






							@slow
							def  A_     ( self     :					str						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
														SCREAMING_SNAKE_CASE__       :      str     =						model.to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)

														SCREAMING_SNAKE_CASE__       :      str     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Any     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						outputs.logits

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Any     =						torch.Size((1, 2_1, 3_2, 3_2)						)
														self.assertEqual(logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.tensor(
														    [
														        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
														        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
														        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
														    ],			device=_UpperCAmelCase,			)

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






							@slow
							def  A_     ( self     :					List[str]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
														SCREAMING_SNAKE_CASE__       :      str     =						model.to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      str     =						MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      str     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      int     =						model(**_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						outputs.logits.detach().cpu()

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase,			target_sizes=[(5_0, 6_0)]						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.Size((5_0, 6_0)						)
														self.assertEqual(segmentation[0].shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.Size((3_2, 3_2)						)
														self.assertEqual(segmentation[0].shape,			_UpperCAmelCase						)













import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		_lowerCamelCase :    Union[str, Any]  							= '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

		print('''Googling.....''')

		_lowerCamelCase :    Any  							= f"https://www.google.com/search?q={query}&num=100"

		_lowerCamelCase :    Optional[Any]  							= requests.get(
		    url,
		    headers={'''User-Agent''': str(UserAgent().random)},
		)

		try:
				_lowerCamelCase :    Union[str, Any]  							= (
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''yuRUbf'''})
				    .find('''a''')
				    .get('''href''')
				)

		except AttributeError:
				_lowerCamelCase :    Optional[Any]  							= parse_qs(
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''kCrYT'''})
				    .find('''a''')
				    .get('''href''')
				)['''url'''][0]

		webbrowser.open(link)






