




"""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        lowercase      :







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Tuple=13      ,     _lowerCamelCase   :					int=30      ,     _lowerCamelCase   :					List[Any]=2      ,     _lowerCamelCase   :					Dict=3      ,     _lowerCamelCase   :					Optional[Any]=True      ,     _lowerCamelCase   :					Any=True      ,     _lowerCamelCase   :					Tuple=32      ,     _lowerCamelCase   :					Optional[int]=5      ,     _lowerCamelCase   :					Any=4      ,     _lowerCamelCase   :					List[str]=37      ,     _lowerCamelCase   :					Optional[Any]="gelu"      ,     _lowerCamelCase   :					List[Any]=0.1      ,     _lowerCamelCase   :					List[str]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=10      ,     _lowerCamelCase   :					Dict=0.02      ,     _lowerCamelCase   :					Optional[int]=None      ,     ):



					"""simple docstring"""

					A_  :				int	   =		parent
					A_  :				Any	   =		batch_size
					A_  :				List[Any]	   =		image_size
					A_  :				int	   =		patch_size
					A_  :				Tuple	   =		num_channels
					A_  :				List[str]	   =		is_training
					A_  :				List[Any]	   =		use_labels
					A_  :				List[str]	   =		hidden_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				str	   =		intermediate_size
					A_  :				int	   =		hidden_act
					A_  :				str	   =		hidden_dropout_prob
					A_  :				List[str]	   =		attention_probs_dropout_prob
					A_  :				List[str]	   =		type_sequence_label_size
					A_  :				Any	   =		initializer_range
					A_  :				Tuple	   =		scope

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







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				int	   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

					A_  :				Tuple	   =		self.get_config()

					return config, pixel_values, labels







				def 					a_  (							self   :					Any       ):



					"""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   :					str      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					A_  :				Any	   =		ViTMSNModel(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				List[str]	   =		model(_lowerCamelCase       )
					self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)       )







				def 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.type_sequence_label_size
					A_  :				str	   =		ViTMSNForImageClassification(_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				int	   =		model(_lowerCamelCase      ,     labels=_lowerCamelCase       )
					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_  :				Optional[int]	   =		1
					A_  :				Dict	   =		ViTMSNForImageClassification(_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()

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







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.prepare_config_and_inputs()
					A_   , A_   , A_  :				Optional[Any]	   =		config_and_inputs
					A_  :				List[str]	   =		{'''pixel_values''': pixel_values}
					return config, inputs_dict

@require_torch
class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    List[str]         =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__lowerCAmelCase    :    Dict         =   (
				    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__lowerCAmelCase    :    List[Any]         =   False
				__lowerCAmelCase    :    str         =   False
				__lowerCAmelCase    :    Optional[Any]         =   False
				__lowerCAmelCase    :    Any         =   False







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		ViTMSNModelTester(self       )
					A_  :				Dict	   =		ConfigTester(self      ,     config_class=_lowerCamelCase      ,     has_text_modality=_lowerCamelCase      ,     hidden_size=37       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''       )
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_   , A_  :				Tuple	   =		self.model_tester.prepare_config_and_inputs_for_common()

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







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_   , A_  :				List[Any]	   =		self.model_tester.prepare_config_and_inputs_for_common()

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

						A_  :				Any	   =		['''pixel_values''']
						self.assertListEqual(arg_names[:1]      ,     _lowerCamelCase       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				str	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase       )







				@slow
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A_  :				List[Any]	   =		ViTMSNModel.from_pretrained(_lowerCamelCase       )
						self.assertIsNotNone(_lowerCamelCase       )

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				str	   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
	return image

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







				@cached_property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

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







				@slow
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(2       )
					A_  :				Dict	   =		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(_lowerCamelCase       )

					A_  :				Dict	   =		self.default_image_processor
					A_  :				int	   =		prepare_img()
					A_  :				Dict	   =		image_processor(images=_lowerCamelCase      ,     return_tensors='''pt'''       ).to(_lowerCamelCase       )

					# forward pass
					with torch.no_grad():
						A_  :				str	   =		model(**_lowerCamelCase       )

					# verify the logits
					A_  :				str	   =		torch.Size((1, 10_00)       )
					self.assertEqual(outputs.logits.shape      ,     _lowerCamelCase       )

					A_  :				Optional[int]	   =		torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(_lowerCamelCase       )

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











"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase  :			Optional[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Any        =       {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   """luke"""







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]=5_02_67      ,     _lowerCamelCase   :					Tuple=50_00_00      ,     _lowerCamelCase   :					int=7_68      ,     _lowerCamelCase   :					Union[str, Any]=2_56      ,     _lowerCamelCase   :					Dict=12      ,     _lowerCamelCase   :					str=12      ,     _lowerCamelCase   :					Dict=30_72      ,     _lowerCamelCase   :					Union[str, Any]="gelu"      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					int=2      ,     _lowerCamelCase   :					Optional[int]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					str=0      ,     _lowerCamelCase   :					Dict=2      ,     **_lowerCamelCase   :					Optional[int]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				str	   =		vocab_size
					A_  :				Optional[Any]	   =		entity_vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				List[str]	   =		entity_emb_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				Union[str, Any]	   =		hidden_act
					A_  :				Dict	   =		intermediate_size
					A_  :				int	   =		hidden_dropout_prob
					A_  :				str	   =		attention_probs_dropout_prob
					A_  :				List[Any]	   =		max_position_embeddings
					A_  :				Optional[Any]	   =		type_vocab_size
					A_  :				Optional[int]	   =		initializer_range
					A_  :				str	   =		layer_norm_eps
					A_  :				List[str]	   =		use_entity_aware_attention
					A_  :				Optional[int]	   =		classifier_dropout





