





'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					List[Any]					,				__a     :					List[str]					,				__a     :					Tuple=13					,				__a     :					List[Any]=30					,				__a     :					int=2					,				__a     :					Optional[Any]=3					,				__a     :					List[str]=True					,				__a     :					List[Any]=True					,				__a     :					Tuple=32					,				__a     :					Optional[int]=5					,				__a     :					Any=4					,				__a     :					Dict=37					,				__a     :					Dict="gelu"					,				__a     :					int=0.1					,				__a     :					Dict=0.1					,				__a     :					List[Any]=10					,				__a     :					Union[str, Any]=0.02					,				__a     :					Dict=None					,				):
								_a								= parent
								_a								= batch_size
								_a								= image_size
								_a								= patch_size
								_a								= num_channels
								_a								= is_training
								_a								= use_labels
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= type_sequence_label_size
								_a								= initializer_range
								_a								= scope

								# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								_a								= (image_size // patch_size) ** 2
								_a								= num_patches + 1




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

								_a								= self.get_config()

								return config, pixel_values, labels




				def   UpperCamelCase__   (  self     :					Tuple					):
								return ViTMSNConfig(
								    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					,				initializer_range=self.initializer_range					,				)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					str					,				__a     :					Dict					,				__a     :					Any					):
								_a								= ViTMSNModel(config=__a					)
								model.to(__a					)
								model.eval()
								_a								= model(__a					)
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					Tuple					,				__a     :					str					,				__a     :					Tuple					):
								_a								= self.type_sequence_label_size
								_a								= ViTMSNForImageClassification(__a					)
								model.to(__a					)
								model.eval()
								_a								= model(__a					,				labels=__a					)
								print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"					)
								print("Labels: {labels}"					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# test greyscale images
								_a								= 1
								_a								= ViTMSNForImageClassification(__a					)
								model.to(__a					)
								model.eval()

								_a								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
								_a								= model(__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.prepare_config_and_inputs()
								_a	,					_a	,					_a								= config_and_inputs
								_a								= {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__a            =(
				    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__a            =False
				__a            =False
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= ViTMSNModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				has_text_modality=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								self.config_tester.run_common_tests()




				@unittest.skip(reason="ViTMSN does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					List[str]					):
								pass




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												self.assertIsInstance(model.get_input_embeddings()					,				(nn.Module)					)
												_a								= model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a					,				nn.Linear					)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												_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]					,				__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a					)




				def   UpperCamelCase__   (  self     :					int					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					List[str]					):
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_a								= ViTMSNModel.from_pretrained(__a					)
												self.assertIsNotNone(__a					)






def  _lowerCamelCase				( )	->	Optional[Any]:
				_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image



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






				"""simple docstring"""







				@cached_property
				def   UpperCamelCase__   (  self     :					Dict					):
								return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"					) if is_vision_available() else None




				@slow
				def   UpperCamelCase__   (  self     :					str					):
								torch.manual_seed(2					)
								_a								= ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"					).to(__a					)

								_a								= self.default_image_processor
								_a								= prepare_img()
								_a								= image_processor(images=__a					,				return_tensors="pt"					).to(__a					)

								# forward pass
								with torch.no_grad():
												_a								= model(**__a					)

								# verify the logits
								_a								= torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape					,				__a					)

								_a								= torch.tensor([-0.0803, -0.4454, -0.2375]					).to(__a					)

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







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int = 100						)	->	int:
				_a								= 0
				_a								= 0
				for i in range(1    ,							n + 1						):
								sum_of_squares += i**2
								sum_of_ints += i
				return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
					print(f"""{solution() = }""")
