




'''simple docstring'''
import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
							from transformers.models.mobilevitva.modeling_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
							    make_divisible,
							)


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						Optional[int])    ->							Optional[int]:
								A_									= self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(_lowercase      ,       'width_multiplier'))





class   __UpperCAmelCase				:



					'''simple docstring'''





					def __init__(	self     :						Tuple      ,       _lowercase     :						int      ,       _lowercase     :						Tuple=13      ,       _lowercase     :						Optional[Any]=64      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						List[str]=3      ,       _lowercase     :						Optional[int]="swish"      ,       _lowercase     :						Tuple=3      ,       _lowercase     :						str=32      ,       _lowercase     :						Tuple=0.1      ,       _lowercase     :						Any=0.02      ,       _lowercase     :						int=True      ,       _lowercase     :						Dict=True      ,       _lowercase     :						Union[str, Any]=10      ,       _lowercase     :						List[str]=None      ,       _lowercase     :						int=0.25      ,       _lowercase     :						Optional[int]=0.0      ,       _lowercase     :						str=0.0      ,       )    ->							str:
								A_									= parent
								A_									= batch_size
								A_									= image_size
								A_									= patch_size
								A_									= num_channels
								A_									= make_divisible(512 * width_multiplier      ,       divisor=8)
								A_									= hidden_act
								A_									= conv_kernel_size
								A_									= output_stride
								A_									= classifier_dropout_prob
								A_									= use_labels
								A_									= is_training
								A_									= num_labels
								A_									= initializer_range
								A_									= scope
								A_									= width_multiplier
								A_									= ffn_dropout
								A_									= attn_dropout



					def     __snake_case						(	self     :						Any)    ->							Optional[Any]:
								A_									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								A_									= None
								A_									= None
								if self.use_labels:
											A_									= ids_tensor([self.batch_size]      ,       self.num_labels)
											A_									= ids_tensor([self.batch_size, self.image_size, self.image_size]      ,       self.num_labels)

								A_									= self.get_config()

								return config, pixel_values, labels, pixel_labels



					def     __snake_case						(	self     :						str)    ->							str:
								return MobileViTVaConfig(
								    image_size=self.image_size      ,       patch_size=self.patch_size      ,       num_channels=self.num_channels      ,       hidden_act=self.hidden_act      ,       conv_kernel_size=self.conv_kernel_size      ,       output_stride=self.output_stride      ,       classifier_dropout_prob=self.classifier_dropout_prob      ,       initializer_range=self.initializer_range      ,       width_multiplier=self.width_multiplier      ,       ffn_dropout=self.ffn_dropout_prob      ,       attn_dropout=self.attn_dropout_prob      ,       )



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						str      ,       _lowercase     :						int      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Union[str, Any])    ->							List[Any]:
								A_									= MobileViTVaModel(config=_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= model(_lowercase)
								self.parent.assertEqual(
								    result.last_hidden_state.shape      ,       (
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )      ,       )



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Dict      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any])    ->							Tuple:
								A_									= self.num_labels
								A_									= MobileViTVaForImageClassification(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= model(_lowercase      ,       labels=_lowercase)
								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels))



					def     __snake_case						(	self     :						int      ,       _lowercase     :						Tuple      ,       _lowercase     :						List[str]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Union[str, Any])    ->							int:
								A_									= self.num_labels
								A_									= MobileViTVaForSemanticSegmentation(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= model(_lowercase)
								self.parent.assertEqual(
								    result.logits.shape      ,       (
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )      ,       )
								A_									= model(_lowercase      ,       labels=_lowercase)
								self.parent.assertEqual(
								    result.logits.shape      ,       (
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )      ,       )



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





@require_torch
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		(
					    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)

					_UpperCamelCase							=		(
					    {
					        """feature-extraction""": MobileViTVaModel,
					        """image-classification""": MobileViTVaForImageClassification,
					        """image-segmentation""": MobileViTVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					_UpperCamelCase							=		False
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						int)    ->							List[Any]:
								A_									= MobileViTVaModelTester(self)
								A_									= MobileViTVaConfigTester(self      ,       config_class=_lowercase      ,       has_text_modality=_lowercase)



					def     __snake_case						(	self     :						str)    ->							Tuple:
								self.config_tester.run_common_tests()



					@unittest.skip(reason='MobileViTV2 does not use inputs_embeds')
					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								pass



					@unittest.skip(reason='MobileViTV2 does not support input and output embeddings')
					def     __snake_case						(	self     :						List[Any])    ->							Optional[int]:
								pass



					@unittest.skip(reason='MobileViTV2 does not output attentions')
					def     __snake_case						(	self     :						Tuple)    ->							List[str]:
								pass



					@require_torch_multi_gpu
					@unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.')
					def     __snake_case						(	self     :						Dict)    ->							Dict:
								pass



					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								pass



					def     __snake_case						(	self     :						int)    ->							int:
								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

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

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



					def     __snake_case						(	self     :						str)    ->							Tuple:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase)



					def     __snake_case						(	self     :						Optional[Any])    ->							str:
								def check_hidden_states_output(_lowercase     :						Optional[int]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str]):
											A_									= model_class(_lowercase)
											model.to(_lowercase)
											model.eval()

											with torch.no_grad():
														A_									= model(**self._prepare_for_class(_lowercase      ,       _lowercase))

											A_									= outputs.hidden_states

											A_									= 5
											self.assertEqual(len(_lowercase)      ,       _lowercase)

											# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
											# with the width and height being successively divided by 2.
											A_									= 2
											for i in range(len(_lowercase)):
														self.assertListEqual(
														    list(hidden_states[i].shape[-2:])      ,       [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,       )
														divisor *= 2

											self.assertEqual(self.model_tester.output_stride      ,       divisor // 2)

								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A_									= True
											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											A_									= True

											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						Tuple)    ->							Dict:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase)



					def     __snake_case						(	self     :						Any)    ->							List[str]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase)



					@slow
					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A_									= MobileViTVaModel.from_pretrained(_lowercase)
											self.assertIsNotNone(_lowercase)





def 			lowerCamelCase(							)		->		Union[str, Any]:
			A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image






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



					'''simple docstring'''





					@cached_property
					def     __snake_case						(	self     :						Any)    ->							Union[str, Any]:
								return (
								    MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256')
								    if is_vision_available()
								    else None
								)



					@slow
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								A_									= MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256').to(
								    _lowercase)

								A_									= self.default_image_processor
								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

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

								# verify the logits
								A_									= torch.Size((1, 1_000))
								self.assertEqual(outputs.logits.shape      ,       _lowercase)

								A_									= torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]).to(_lowercase)

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



					@slow
					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
								A_									= model.to(_lowercase)

								A_									= MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

								# forward pass
								with torch.no_grad():
											A_									= model(**_lowercase)
								A_									= outputs.logits

								# verify the logits
								A_									= torch.Size((1, 21, 32, 32))
								self.assertEqual(logits.shape      ,       _lowercase)

								A_									= torch.tensor(
								    [
								        [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
								        [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
								        [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
								    ]      ,       device=_lowercase      ,       )

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



					@slow
					def     __snake_case						(	self     :						int)    ->							Any:
								A_									= MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
								A_									= model.to(_lowercase)

								A_									= MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='pt').to(_lowercase)

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

								A_									= outputs.logits.detach().cpu()

								A_									= image_processor.post_process_semantic_segmentation(outputs=_lowercase      ,       target_sizes=[(50, 60)])
								A_									= torch.Size((50, 60))
								self.assertEqual(segmentation[0].shape      ,       _lowercase)

								A_									= image_processor.post_process_semantic_segmentation(outputs=_lowercase)
								A_									= torch.Size((32, 32))
								self.assertEqual(segmentation[0].shape      ,       _lowercase)










'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 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.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

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

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

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 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       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

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




