




'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def __init__(	self     :						Tuple      ,       _lowercase     :						Tuple      ,       _lowercase     :						Tuple=7      ,       _lowercase     :						str=3      ,       _lowercase     :						Optional[int]=30      ,       _lowercase     :						Dict=400      ,       _lowercase     :						Any=True      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						Optional[Any]=True      ,       _lowercase     :						List[Any]=[0.5, 0.5, 0.5]      ,       _lowercase     :						int=[0.5, 0.5, 0.5]      ,       _lowercase     :						Optional[Any]=True      ,       _lowercase     :						Dict=1 / 255      ,       _lowercase     :						List[str]=True      ,       )    ->							Tuple:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								A_									= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
								A_									= parent
								A_									= batch_size
								A_									= num_channels
								A_									= min_resolution
								A_									= max_resolution
								A_									= do_resize
								A_									= size
								A_									= do_normalize
								A_									= image_mean
								A_									= image_std
								A_									= do_rescale
								A_									= rescale_factor
								A_									= do_pad



					def     __snake_case						(	self     :						Tuple)    ->							Dict:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}



					def     __snake_case						(	self     :						int      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any=False)    ->							str:
								if not batched:
											A_									= image_inputs[0]
											if isinstance(_lowercase      ,       Image.Image):
														A_		, A_									= image.size
											else:
														A_		, A_									= image.shape[1], image.shape[2]
											if w < h:
														A_									= int(self.size['shortest_edge'] * h / w)
														A_									= self.size['shortest_edge']
											elif w > h:
														A_									= self.size['shortest_edge']
														A_									= int(self.size['shortest_edge'] * w / h)
											else:
														A_									= self.size['shortest_edge']
														A_									= self.size['shortest_edge']

								else:
											A_									= []
											for image in image_inputs:
														A_		, A_									= self.get_expected_values([image])
														expected_values.append((expected_height, expected_width))
											A_									= max(_lowercase      ,       key=lambda _lowercase: item[0])[0]
											A_									= max(_lowercase      ,       key=lambda _lowercase: item[1])[1]

								return expected_height, expected_width





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



					'''simple docstring'''





					_UpperCamelCase							=		DetaImageProcessor if is_vision_available() else None



					def     __snake_case						(	self     :						Any)    ->							List[str]:
								A_									= DetaImageProcessingTester(self)



					@property
					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase      ,       'image_mean'))
								self.assertTrue(hasattr(_lowercase      ,       'image_std'))
								self.assertTrue(hasattr(_lowercase      ,       'do_normalize'))
								self.assertTrue(hasattr(_lowercase      ,       'do_resize'))
								self.assertTrue(hasattr(_lowercase      ,       'do_rescale'))
								self.assertTrue(hasattr(_lowercase      ,       'do_pad'))
								self.assertTrue(hasattr(_lowercase      ,       'size'))



					def     __snake_case						(	self     :						Tuple)    ->							Any:
								A_									= self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size      ,       {'shortest_edge': 18, 'longest_edge': 1_333})
								self.assertEqual(image_processor.do_pad      ,       _lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							Any:
								pass



					def     __snake_case						(	self     :						Any)    ->							int:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       Image.Image)

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

								self.assertEqual(
								    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

								# Test batched
								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )      ,       )



					def     __snake_case						(	self     :						Optional[int])    ->							Optional[int]:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       np.ndarray)

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

								self.assertEqual(
								    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )      ,       )



					def     __snake_case						(	self     :						Any)    ->							List[str]:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       torch.Tensor)

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

								self.assertEqual(
								    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )      ,       )



					@slow
					def     __snake_case						(	self     :						Union[str, Any])    ->							Union[str, Any]:
								# prepare image and target
								A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
								with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,       'r') as f:
											A_									= json.loads(f.read())

								A_									= {'image_id': 39_769, 'annotations': target}

								# encode them
								A_									= DetaImageProcessor()
								A_									= image_processing(images=_lowercase      ,       annotations=_lowercase      ,       return_tensors='pt')

								# verify pixel values
								A_									= torch.Size([1, 3, 800, 1_066])
								self.assertEqual(encoding['pixel_values'].shape      ,       _lowercase)

								A_									= torch.tensor([0.27_96, 0.31_38, 0.34_81])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,       _lowercase      ,       atol=1E-4))

								# verify area
								A_									= torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,       _lowercase))
								# verify boxes
								A_									= torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       _lowercase)
								A_									= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       _lowercase      ,       atol=1E-3))
								# verify image_id
								A_									= torch.tensor([39_769])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       _lowercase))
								# verify is_crowd
								A_									= torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       _lowercase))
								# verify class_labels
								A_									= torch.tensor([75, 75, 63, 65, 17, 17])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       _lowercase))
								# verify orig_size
								A_									= torch.tensor([480, 640])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       _lowercase))
								# verify size
								A_									= torch.tensor([800, 1_066])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       _lowercase))



					@slow
					def     __snake_case						(	self     :						Any)    ->							Dict:
								# prepare image, target and masks_path
								A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
								with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,       'r') as f:
											A_									= json.loads(f.read())

								A_									= {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

								A_									= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

								# encode them
								A_									= DetaImageProcessor(format='coco_panoptic')
								A_									= image_processing(images=_lowercase      ,       annotations=_lowercase      ,       masks_path=_lowercase      ,       return_tensors='pt')

								# verify pixel values
								A_									= torch.Size([1, 3, 800, 1_066])
								self.assertEqual(encoding['pixel_values'].shape      ,       _lowercase)

								A_									= torch.tensor([0.27_96, 0.31_38, 0.34_81])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,       _lowercase      ,       atol=1E-4))

								# verify area
								A_									= torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,       _lowercase))
								# verify boxes
								A_									= torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       _lowercase)
								A_									= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       _lowercase      ,       atol=1E-3))
								# verify image_id
								A_									= torch.tensor([39_769])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       _lowercase))
								# verify is_crowd
								A_									= torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       _lowercase))
								# verify class_labels
								A_									= torch.tensor([17, 17, 63, 75, 75, 93])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       _lowercase))
								# verify masks
								A_									= 822_873
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,       _lowercase)
								# verify orig_size
								A_									= torch.tensor([480, 640])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       _lowercase))
								# verify size
								A_									= torch.tensor([800, 1_066])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       _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}""")




