


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     ,       )					->			int:




									"""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       UpperCAmelCase__							(     self		)					->			Dict:




									"""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       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     ViTMSNModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.type_sequence_label_size
									_UpperCAmelCase											=     ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     1
									_UpperCAmelCase											=     ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()

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






		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""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 						__a (							UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    Tuple           =      (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
		_a      :    Optional[Any]           =      (
		    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_a      :    Dict           =      False
		_a      :    Optional[Any]           =      False
		_a      :    Tuple           =      False
		_a      :    Dict           =      False
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     ViTMSNModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE     ,       hidden_size=37		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		@unittest.skip(reason='ViTMSN does not use inputs_embeds'		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self		)					->			Optional[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(_SCREAMING_SNAKE_CASE		)
																self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)		)
																_UpperCAmelCase											=     model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE     ,       nn.Linear		)		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																_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]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





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



def 			lowerCAmelCase__					(     )    ->						Any:


							'''simple docstring'''





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







@require_torch
@require_vision
class 						__a (							unittest.TestCase     ):
		@cached_property
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





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






		@slow
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									torch.manual_seed(2		)
									_UpperCAmelCase											=     ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'		).to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.default_image_processor
									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)

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

									# verify the logits
									_UpperCAmelCase											=     torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     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		)		)




from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bool:


							'''simple docstring'''





							_UpperCAmelCase											=     get_failure_array(a__			)

							# 2) Step through text searching for pattern
							_UpperCAmelCase						,			_UpperCAmelCase											=     0, 0  # index into text, pattern
							while i < len(a__			):
														if pattern[j] == text[i]:
																					if j == (len(a__			) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					_UpperCAmelCase											=     failure[j - 1]
																					continue
														i += 1
							return False

def 			lowerCAmelCase__					(     a__:						str			)    ->						list[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     [0]
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1
							while j < len(a__			):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					_UpperCAmelCase											=     failure[i - 1]
																					continue
														j += 1
														failure.append(a__			)
							return failure


if __name__ == "__main__":
		# Test 1)
		lowerCAmelCase__    :List[Any]       =				'''abc1abc12'''
		lowerCAmelCase__    :Optional[int]       =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
		lowerCAmelCase__    :int       =				'''alskfjaldsk23adsfabcabc'''
		assert kmp(pattern, texta) and not kmp(pattern, texta)

		# Test 2)
		lowerCAmelCase__    :List[str]       =				'''ABABX'''
		lowerCAmelCase__    :int       =				'''ABABZABABYABABX'''
		assert kmp(pattern, text)

		# Test 3)
		lowerCAmelCase__    :int       =				'''AAAB'''
		lowerCAmelCase__    :Any       =				'''ABAAAAAB'''
		assert kmp(pattern, text)

		# Test 4)
		lowerCAmelCase__    :Union[str, Any]       =				'''abcdabcy'''
		lowerCAmelCase__    :Any       =				'''abcxabcdabxabcdabcdabcy'''
		assert kmp(pattern, text)

		# Test 5)
		lowerCAmelCase__    :List[Any]       =				'''aabaabaaa'''
		assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
