





'''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    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=13       ,_lowerCamelCase=30       ,_lowerCamelCase=2       ,_lowerCamelCase=3       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=32       ,_lowerCamelCase=5       ,_lowerCamelCase=4       ,_lowerCamelCase=37       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=10       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=None       ,)							->							Dict:







				'''simple docstring'''





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

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


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







				'''simple docstring'''





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

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

				__lowercase			  =							self.get_config()

				return config, pixel_values, labels


	def   _UpperCAmelCase       (self	)							->							Optional[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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							self.type_sequence_label_size
				__lowercase			  =							ViTMSNForImageClassification(_lowerCamelCase	)
				model.to(_lowerCamelCase	)
				model.eval()
				__lowercase			  =							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
				__lowercase			  =							1
				__lowercase			  =							ViTMSNForImageClassification(_lowerCamelCase	)
				model.to(_lowerCamelCase	)
				model.eval()

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


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				__lowercase  ,						__lowercase  ,						__lowercase			  =							config_and_inputs
				__lowercase			  =							{'''pixel_values''': pixel_values}
				return config, inputs_dict






@require_torch
class 							__lowercase    ( lowerCAmelCase__	, lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

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

	a      :			Optional[int]     =    False
	a      :			str     =    False
	a      :			Optional[Any]     =    False
	a      :			List[Any]     =    False


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							ViTMSNModelTester(self	)
				__lowercase			  =							ConfigTester(self       ,config_class=_lowerCamelCase       ,has_text_modality=_lowerCamelCase       ,hidden_size=37	)


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







				'''simple docstring'''





				self.config_tester.run_common_tests()


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







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()

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


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()

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

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


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







				'''simple docstring'''





				__lowercase			  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase	)


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







				'''simple docstring'''





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





def 				_lowerCAmelCase  (			):
			__lowercase			  =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
			return image






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







	'''simple docstring'''

	@cached_property
	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





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


	@slow
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





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

				__lowercase			  =							self.default_image_processor
				__lowercase			  =							prepare_img()
				__lowercase			  =							image_processor(images=_lowerCamelCase       ,return_tensors='''pt'''	).to(_lowerCamelCase	)

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

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

				__lowercase			  =							torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]	).to(_lowerCamelCase	)

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












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			__lowercase			  =							len(lowerCamelCase_			) + 1
			__lowercase			  =							len(lowerCamelCase_			) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			__lowercase			  =							[[0 for i in range(lowerCamelCase_			)] for j in range(lowerCamelCase_			)]

			# since string of zero length match pattern of zero length
			__lowercase			  =							1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1						,    lowerCamelCase_			):
						__lowercase			  =							0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1						,    lowerCamelCase_			):
						__lowercase			  =							dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1						,    lowerCamelCase_			):
						for j in range(1						,    lowerCamelCase_			):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												__lowercase			  =							dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															__lowercase			  =							1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															__lowercase			  =							dp[i - 1][j]
												else:
															__lowercase			  =							0
									else:
												__lowercase			  =							0

			return bool(dp[-1][-1]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	_SCREAMING_SNAKE_CASE   =      '''aab'''
	_SCREAMING_SNAKE_CASE   =      '''c*a*b'''

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(f'''{input_string} matches the given pattern {pattern}''')
	else:
		print(f'''{input_string} does not match with the given pattern {pattern}''')





