
"""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 				A__ :

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

			__lowerCAmelCase    :		int		    =   self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   ViTMSNModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Union[str, Any]		    =   model(_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.type_sequence_label_size
			__lowerCAmelCase    :		Optional[int]		    =   ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE				)
			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
			__lowerCAmelCase    :		Optional[int]		    =   1
			__lowerCAmelCase    :		Tuple		    =   ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()

			__lowerCAmelCase    :		List[Any]		    =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
			__lowerCAmelCase    :		Optional[int]		    =   model(_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   config_and_inputs
			__lowerCAmelCase    :		Any		    =   {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: Tuple            =			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
		A_		: Optional[int]            =			(
		    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A_		: Dict            =			False
		A_		: Tuple            =			False
		A_		: Optional[Any]            =			False
		A_		: List[Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   ViTMSNModelTester(self				)
			__lowerCAmelCase    :		Optional[int]		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE ,	hidden_size=37				)

		def 							__lowerCamelCase							(   self				):
			self.config_tester.run_common_tests()

		@unittest.skip(reason='ViTMSN does not use inputs_embeds'				)
		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase    :		str		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Any		    =   ViTMSNModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




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

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







		@slow
		def 							__lowerCamelCase							(   self				):
			torch.manual_seed(2				)
			__lowerCAmelCase    :		Optional[Any]		    =   ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[Any]		    =   self.default_image_processor
			__lowerCAmelCase    :		int		    =   prepare_img()
			__lowerCAmelCase    :		Union[str, Any]		    =   image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).to(_SCREAMING_SNAKE_CASE				)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase    :		Union[str, Any]		    =   model(**_SCREAMING_SNAKE_CASE				)

			# verify the logits
			__lowerCAmelCase    :		Optional[int]		    =   torch.Size((1, 10_00)				)
			self.assertEqual(outputs.logits.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   torch.tensor([-0.0803, -0.4454, -0.2375]				).to(_SCREAMING_SNAKE_CASE				)

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()