





'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor






class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=7       ,_lowerCamelCase=3       ,_lowerCamelCase=30       ,_lowerCamelCase=400       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,_lowerCamelCase=0.9       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,_lowerCamelCase=[0.5, 0.5, 0.5]       ,)							->							int:







				'''simple docstring'''





				__lowercase			  =							size if size is not None else {'''shortest_edge''': 30}
				__lowercase			  =							crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							num_channels
				__lowercase			  =							min_resolution
				__lowercase			  =							max_resolution
				__lowercase			  =							do_resize_and_center_crop
				__lowercase			  =							size
				__lowercase			  =							crop_pct
				__lowercase			  =							crop_size
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean
				__lowercase			  =							image_std


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







				'''simple docstring'''





				return {
				    "size": self.size,
				    "do_resize_and_center_crop": self.do_resize_and_center_crop,
				    "crop_pct": self.crop_pct,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






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







	'''simple docstring'''

	a      :			Optional[Any]     =    PoolFormerImageProcessor if is_vision_available() else None


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







				'''simple docstring'''





				__lowercase			  =							PoolFormerImageProcessingTester(self	)


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''do_resize_and_center_crop'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''size'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''crop_pct'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''do_normalize'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''image_mean'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''image_std'''	)	)


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size       ,{'''shortest_edge''': 30}	)
				self.assertEqual(image_processor.crop_size       ,{'''height''': 30, '''width''': 30}	)

				__lowercase			  =							self.image_processing_class.from_dict(self.image_processor_dict       ,size=42       ,crop_size=84	)
				self.assertEqual(image_processor.size       ,{'''shortest_edge''': 42}	)
				self.assertEqual(image_processor.crop_size       ,{'''height''': 84, '''width''': 84}	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,Image.Image	)

				# Test not batched input
				__lowercase			  =							image_processing(image_inputs[0]       ,return_tensors='''pt'''	).pixel_values

				self.assertEqual(
				    encoded_images.shape       ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)


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







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase       ,numpify=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,np.ndarray	)

				# Test not batched input
				__lowercase			  =							image_processing(image_inputs[0]       ,return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				__lowercase			  =							prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_lowerCamelCase       ,torchify=_lowerCamelCase	)
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase       ,torch.Tensor	)

				# Test not batched input
				__lowercase			  =							image_processing(image_inputs[0]       ,return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)

				# Test batched
				__lowercase			  =							image_processing(_lowerCamelCase       ,return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,)












'''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}''')





