






"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

 from transformers import ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class        UpperCamelCase			:


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=13							,						UpperCAmelCase__		:						Optional[int]=30							,						UpperCAmelCase__		:						Optional[int]=2							,						UpperCAmelCase__		:						List[Any]=3							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						Tuple=32							,						UpperCAmelCase__		:						int=5							,						UpperCAmelCase__		:						Tuple=4							,						UpperCAmelCase__		:						Optional[int]=37							,						UpperCAmelCase__		:						List[Any]="gelu"							,						UpperCAmelCase__		:						Any=0.1							,						UpperCAmelCase__		:						int=0.1							,						UpperCAmelCase__		:						Dict=10							,						UpperCAmelCase__		:						List[Any]=0.0_2							,						UpperCAmelCase__		:						Union[str, Any]=3							,						UpperCAmelCase__		:						Optional[Any]=0.6							,						UpperCAmelCase__		:						Optional[int]=None							,						)					->       str:
   _a  :       Dict             =	parent
   _a  :       str             =	batch_size
   _a  :       Tuple             =	image_size
   _a  :       Any             =	patch_size
   _a  :       Union[str, Any]             =	num_channels
   _a  :       List[str]             =	is_training
   _a  :       Union[str, Any]             =	use_labels
   _a  :       Optional[int]             =	hidden_size
   _a  :       Optional[int]             =	num_hidden_layers
   _a  :       List[Any]             =	num_attention_heads
   _a  :       Dict             =	intermediate_size
   _a  :       str             =	hidden_act
   _a  :       Any             =	hidden_dropout_prob
   _a  :       Dict             =	attention_probs_dropout_prob
   _a  :       Any             =	type_sequence_label_size
   _a  :       List[str]             =	initializer_range
   _a  :       List[Any]             =	mask_ratio
   _a  :       List[str]             =	scope

   # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
   # (we add 1 for the [CLS] token)
   _a  :       Optional[int]             =	(image_size // patch_size) ** 2
   _a  :       Any             =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)


 def _lowercase	(     self		:						int							)					->       Union[str, Any]:
   _a  :       Union[str, Any]             =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   _a  :       List[Any]             =	None
   if self.use_labels:
     _a  :       List[Any]             =	ids_tensor([self.batch_size]							,						self.type_sequence_label_size							)

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

   return config, pixel_values, labels


 def _lowercase	(     self		:						Union[str, Any]							)					->       Tuple:
   return ViTMAEConfig(
       image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						is_decoder=__lowerCAmelCase							,						initializer_range=self.initializer_range							,						mask_ratio=self.mask_ratio							,						)


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						List[Any]							)					->       List[Any]:
   _a  :       int             =	ViTMAEModel(config=__lowerCAmelCase							)
   model.to(__lowerCAmelCase							)
   model.eval()
   _a  :       Tuple             =	model(__lowerCAmelCase							)
   self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)							)


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Union[str, Any]							,						UpperCAmelCase__		:						str							)					->       Any:
   _a  :       Tuple             =	ViTMAEForPreTraining(__lowerCAmelCase							)
   model.to(__lowerCAmelCase							)
   model.eval()
   _a  :       List[str]             =	model(__lowerCAmelCase							)
   _a  :       Optional[int]             =	(self.image_size // self.patch_size) ** 2
   _a  :       Dict             =	self.patch_size**2 * self.num_channels
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, num_patches, expected_num_channels)							)

   # test greyscale images
   _a  :       Union[str, Any]             =	1
   _a  :       Dict             =	ViTMAEForPreTraining(__lowerCAmelCase							)
   model.to(__lowerCAmelCase							)
   model.eval()
   _a  :       Tuple             =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
   _a  :       List[Any]             =	model(__lowerCAmelCase							)
   _a  :       Tuple             =	self.patch_size**2
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, num_patches, expected_num_channels)							)







 def _lowercase	(     self		:						Optional[int]							)					->       List[str]:
   _a  :       Tuple             =	self.prepare_config_and_inputs()
   _a      ,  _a      ,  _a  :       str             =	config_and_inputs
   _a  :       Optional[Any]             =	{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class        UpperCamelCase			(	snake_case_		,     snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       int			=					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 UpperCamelCase					:       Union[str, Any]			=					{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 UpperCamelCase					:       int			=					False
 UpperCamelCase					:       Optional[Any]			=					False
 UpperCamelCase					:       str			=					False
 UpperCamelCase					:       str			=					False


 def _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	ViTMAEModelTester(self							)
   _a  :       str             =	ConfigTester(self							,						config_class=__lowerCAmelCase							,						has_text_modality=__lowerCAmelCase							,						hidden_size=37							)


 def _lowercase	(     self		:						Any							)					->       Any:
   self.config_tester.run_common_tests()


 @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
 def _lowercase	(     self		:						Optional[Any]							)					->       Any:
   pass


 def _lowercase	(     self		:						int							)					->       int:
   _a      ,  _a  :       Optional[Any]             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       Optional[int]             =	model_class(__lowerCAmelCase							)
     self.assertIsInstance(model.get_input_embeddings()							,						(nn.Module)							)
     _a  :       List[str]             =	model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(__lowerCAmelCase							,						nn.Linear							)							)


 def _lowercase	(     self		:						str							)					->       Any:
   _a      ,  _a  :       List[Any]             =	self.model_tester.prepare_config_and_inputs_for_common()

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

     _a  :       Optional[Any]             =	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]							,						__lowerCAmelCase							)


 def _lowercase	(     self		:						Dict							)					->       Tuple:
   _a  :       Optional[int]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__lowerCAmelCase							)


 def _lowercase	(     self		:						List[Any]							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase							)


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						Optional[int]							)					->       str:
   np.random.seed(2							)

   _a  :       List[str]             =	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
   _a  :       List[str]             =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
   _a  :       Dict             =	torch.from_numpy(__lowerCAmelCase							)

   # Add `noise` argument.
   # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
   _a  :       List[Any]             =	pt_noise

   super().check_pt_tf_models(__lowerCAmelCase							,						__lowerCAmelCase							,						__lowerCAmelCase							)


 def _lowercase	(     self		:						Optional[Any]							)					->       int:
   _a      ,  _a  :       Dict             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       List[str]             =	model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.eval()
     # make random mask reproducible
     torch.manual_seed(2							)
     with torch.no_grad():
       _a  :       int             =	model(**self._prepare_for_class(__lowerCAmelCase							,						__lowerCAmelCase							)							)

     _a  :       List[str]             =	outputs[0].cpu().numpy()
     _a  :       Tuple             =	0

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(__lowerCAmelCase							)
       _a  :       Dict             =	model_class.from_pretrained(__lowerCAmelCase							)
       model.to(__lowerCAmelCase							)
       # make random mask reproducible
       torch.manual_seed(2							)
       with torch.no_grad():
         _a  :       Dict             =	model(**self._prepare_for_class(__lowerCAmelCase							,						__lowerCAmelCase							)							)

       # Make sure we don't have nans
       _a  :       List[str]             =	after_outputs[0].cpu().numpy()
       _a  :       Optional[int]             =	0
       _a  :       List[Any]             =	np.amax(np.abs(out_a - out_a							)							)
       self.assertLessEqual(__lowerCAmelCase							,						1E-5							)


 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   pass


 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def _lowercase	(     self		:						str							)					->       Dict:
   pass


 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def _lowercase	(     self		:						int							)					->       List[Any]:
   pass


 @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
 def _lowercase	(     self		:						Optional[int]							)					->       Union[str, Any]:
   pass


 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def _lowercase	(     self		:						int							)					->       Union[str, Any]:
   pass







 @slow
 def _lowercase	(     self		:						Dict							)					->       List[Any]:






   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _a  :       Optional[int]             =	ViTMAEModel.from_pretrained(__lowerCAmelCase							)
     self.assertIsNotNone(__lowerCAmelCase							)







def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       str             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
  return image






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


 @cached_property
 def _lowercase	(     self		:						Optional[Any]							)					->       int:
   return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None







 @slow
 def _lowercase	(     self		:						int							)					->       Union[str, Any]:
   np.random.seed(2							)

   _a  :       Dict             =	ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(__lowerCAmelCase							)

   _a  :       Any             =	self.default_image_processor
   _a  :       Tuple             =	prepare_img()
   _a  :       Dict             =	image_processor(images=__lowerCAmelCase							,						return_tensors="""pt"""							).to(__lowerCAmelCase							)

   # prepare a noise vector that will be also used for testing the TF model
   # (this way we can ensure that the PT and TF models operate on the same inputs)
   _a  :       List[Any]             =	ViTMAEConfig()
   _a  :       Union[str, Any]             =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
   _a  :       Any             =	np.random.uniform(size=(1, num_patches)							)

   # forward pass
   with torch.no_grad():
     _a  :       Optional[Any]             =	model(**__lowerCAmelCase							,						noise=torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)							)

   # verify the logits
   _a  :       int             =	torch.Size((1, 196, 768)							)
   self.assertEqual(outputs.logits.shape							,						__lowerCAmelCase							)

   _a  :       Optional[int]             =	torch.tensor(
       [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]							)

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








from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      BeautifulSoup(requests.get(url).content, '''html.parser''')
					# The image URL is in the content field of the first meta tag with property og:image
					A    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
					with open(file_name, '''wb''') as fp:
										fp.write(image_data)
					print(F'''Done. Image saved to disk as {file_name}.''')



