
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        lowerCAmelCase__      :

						def __init__(			self       ,							a       ,							a=13       ,							a=30       ,							a=2       ,							a=3       ,							a=True       ,							a=True       ,							a=32       ,							a=5       ,							a=4       ,							a=37       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=10       ,							a=0.02       ,							a=None       ,							)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	parent
								_UpperCamelCase						    =	batch_size
								_UpperCamelCase						    =	image_size
								_UpperCamelCase						    =	patch_size
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	is_training
								_UpperCamelCase						    =	use_labels
								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	intermediate_size
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	type_sequence_label_size
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	scope

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

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

								_UpperCamelCase						    =	self.get_config()

								return config, pixel_values, labels

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								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  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	ViTMSNModel(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.type_sequence_label_size
								_UpperCamelCase						    =	ViTMSNForImageClassification(a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	1
								_UpperCamelCase						    =	ViTMSNForImageClassification(a				)
								model.to(a				)
								model.eval()

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







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


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	config_and_inputs
								_UpperCamelCase						    =	{"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_torch
class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: int	=		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						UpperCamelCase_							: Optional[Any]	=		(
						    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_							: Union[str, Any]	=		False
						UpperCamelCase_							: Tuple	=		False
						UpperCamelCase_							: Union[str, Any]	=		False
						UpperCamelCase_							: str	=		False

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


								'''simple docstring'''

								_UpperCamelCase						    =	ViTMSNModelTester(self				)
								_UpperCamelCase						    =	ConfigTester(self       ,							config_class=a       ,							has_text_modality=a       ,							hidden_size=37				)

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


								'''simple docstring'''

								self.config_tester.run_common_tests()

						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""				)
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

										_UpperCamelCase						    =	["""pixel_values"""]
										self.assertListEqual(arg_names[:1]       ,							a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a				)







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


								'''simple docstring'''

								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_UpperCamelCase						    =	ViTMSNModel.from_pretrained(a				)
										self.assertIsNotNone(a				)




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

		"""simple docstring"""






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







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

						@cached_property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""				) if is_vision_available() else None







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


								'''simple docstring'''

								torch.manual_seed(2				)
								_UpperCamelCase						    =	ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""				).to(a				)

								_UpperCamelCase						    =	self.default_image_processor
								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				).to(a				)

								# forward pass
								with torch.no_grad():
										_UpperCamelCase						    =	model(**a				)

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

								_UpperCamelCase						    =	torch.tensor([-0.0803, -0.4454, -0.2375]				).to(a				)

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





def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float]:

		"""simple docstring"""






		if not len(lowerCAmelCase ) == len(lowerCAmelCase ) == 3:
				raise ValueError("""Please enter a valid equation.""" )
		if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
				raise ValueError("""Both a & b of two equations can't be zero.""" )

		# Extract the coefficients
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa

		# Calculate the determinants of the matrices
		_UpperCamelCase						    =	aa * ba - aa * ba
		_UpperCamelCase						    =	ca * ba - ca * ba
		_UpperCamelCase						    =	aa * ca - aa * ca

		# Check if the system of linear equations has a solution (using Cramer's rule)
		if determinant == 0:
				if determinant_x == determinant_y == 0:
						raise ValueError("""Infinite solutions. (Consistent system)""" )
				else:
						raise ValueError("""No solution. (Inconsistent system)""" )
		else:
				if determinant_x == determinant_y == 0:
						# Trivial solution (Inconsistent system)
						return (0.0, 0.0)
				else:
						_UpperCamelCase						    =	determinant_x / determinant
						_UpperCamelCase						    =	determinant_y / determinant
						# Non-Trivial Solution (Consistent system)
						return (x, y)



