





class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					int						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[0] * size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[0] * size






							@staticmethod
							def  A_     ( _UpperCAmelCase     :					int						)				->			int:





														"""simple docstring"""
														return index | (index + 1)






							@staticmethod
							def  A_     ( _UpperCAmelCase     :					int						)				->			int:





														"""simple docstring"""
														return (index & (index + 1)) - 1






							def  A_     ( self     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						value
														while index < self.size:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_prev(_UpperCAmelCase						) + 1
																					if current_left_border == index:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						value
																					else:
																												SCREAMING_SNAKE_CASE__       :      str     =						max(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_next(_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int						)				->			int:





														"""simple docstring"""
														right -= 1  # Because of right is exclusive
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0
														while left <= right:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.get_prev(_UpperCAmelCase						)
																					if left <= current_left:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						max(_UpperCAmelCase,			self.tree[right]						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						current_left
																					else:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						max(_UpperCAmelCase,			self.arr[right]						)
																												right -= 1
														return result


if __name__ == "__main__":
		import doctest

		doctest.testmod()













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       :







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      str     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
														SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						scope

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






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						None
														if self.use_labels:
																					SCREAMING_SNAKE_CASE__       :      int     =						ids_tensor([self.batch_size],			self.type_sequence_label_size						)

														SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

														return config, pixel_values, labels






							def  A_     ( self     :					int						)				->			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     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase,			labels=_UpperCAmelCase						)
														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
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
														return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




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

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






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





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

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






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






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





														"""simple docstring"""
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

							'''simple docstring'''






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







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





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






							@slow
							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														torch.manual_seed(2						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

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






