






'''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 _lowercase	(				unittest.TestCase		):




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




							def 		lowerCamelCase_				(							self:				int     ):
														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 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Any=False     ):
														if not batched:
																					lowerCamelCase__					:	List[Any]         =       image_inputs[0]
																					if isinstance(UpperCamelCase__			,							Image.Image     ):
																												lowerCamelCase__  ,		lowerCamelCase__					:	Tuple         =       image.size
																					else:
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       image.shape[1], image.shape[2]
																					if w < h:
																												lowerCamelCase__					:	Optional[int]         =       int(self.size["""shortest_edge"""] * h / w     )
																												lowerCamelCase__					:	int         =       self.size["""shortest_edge"""]
																					elif w > h:
																												lowerCamelCase__					:	Any         =       self.size["""shortest_edge"""]
																												lowerCamelCase__					:	Dict         =       int(self.size["""shortest_edge"""] * w / h     )
																					else:
																												lowerCamelCase__					:	Union[str, Any]         =       self.size["""shortest_edge"""]
																												lowerCamelCase__					:	Union[str, Any]         =       self.size["""shortest_edge"""]

														else:
																					lowerCamelCase__					:	Optional[Any]         =       []
																					for image in image_inputs:
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.get_expected_values([image]     )
																												expected_values.append((expected_height, expected_width)     )
																					lowerCamelCase__					:	Optional[Any]         =       max(UpperCamelCase__			,							key=lambda UpperCamelCase__     : item[0]     )[0]
																					lowerCamelCase__					:	Optional[int]         =       max(UpperCamelCase__			,							key=lambda UpperCamelCase__     : item[1]     )[1]

														return expected_height, expected_width



@require_torch
@require_vision
class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       DetaImageProcessor if is_vision_available() else None




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       DetaImageProcessingTester(self     )




							@property
							def 		lowerCamelCase_				(							self:				Any     ):
														return self.image_processor_tester.prepare_image_processor_dict()




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	str         =       self.image_processing_class(**self.image_processor_dict     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""image_mean"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""image_std"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_normalize"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_resize"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_rescale"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_pad"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""size"""     )     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	str         =       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			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														# Initialize image_processing
														lowerCamelCase__					:	Optional[int]         =       self.image_processing_class(**self.image_processor_dict     )
														# create random PIL images
														lowerCamelCase__					:	List[str]         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							Image.Image     )

														# Test not batched input
														lowerCamelCase__					:	int         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.image_processor_tester.get_expected_values(UpperCamelCase__     )

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

														# Test batched
														lowerCamelCase__  ,		lowerCamelCase__					:	int         =       self.image_processor_tester.get_expected_values(UpperCamelCase__			,							batched=UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       image_processing(UpperCamelCase__			,							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 		lowerCamelCase_				(							self:				List[str]     ):
														# Initialize image_processing
														lowerCamelCase__					:	str         =       self.image_processing_class(**self.image_processor_dict     )
														# create random numpy tensors
														lowerCamelCase__					:	int         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__			,							numpify=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							np.ndarray     )

														# Test not batched input
														lowerCamelCase__					:	Optional[Any]         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values

														lowerCamelCase__  ,		lowerCamelCase__					:	int         =       self.image_processor_tester.get_expected_values(UpperCamelCase__     )

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

														# Test batched
														lowerCamelCase__					:	str         =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""     ).pixel_values

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.image_processor_tester.get_expected_values(UpperCamelCase__			,							batched=UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Any     ):
														# Initialize image_processing
														lowerCamelCase__					:	str         =       self.image_processing_class(**self.image_processor_dict     )
														# create random PyTorch tensors
														lowerCamelCase__					:	Optional[Any]         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__			,							torchify=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							torch.Tensor     )

														# Test not batched input
														lowerCamelCase__					:	List[str]         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.image_processor_tester.get_expected_values(UpperCamelCase__     )

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

														# Test batched
														lowerCamelCase__					:	List[Any]         =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""     ).pixel_values

														lowerCamelCase__  ,		lowerCamelCase__					:	int         =       self.image_processor_tester.get_expected_values(UpperCamelCase__			,							batched=UpperCamelCase__     )

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




							@slow
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														# prepare image and target
														lowerCamelCase__					:	str         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
														with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""			,							"""r"""     ) as f:
																					lowerCamelCase__					:	Tuple         =       json.loads(f.read()     )

														lowerCamelCase__					:	Union[str, Any]         =       {"""image_id""": 39_769, """annotations""": target}

														# encode them
														lowerCamelCase__					:	List[Any]         =       DetaImageProcessor()
														lowerCamelCase__					:	Union[str, Any]         =       image_processing(images=UpperCamelCase__			,							annotations=UpperCamelCase__			,							return_tensors="""pt"""     )

														# verify pixel values
														lowerCamelCase__					:	Union[str, Any]         =       torch.Size([1, 3, 800, 1_066]     )
														self.assertEqual(encoding["""pixel_values"""].shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	str         =       torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
														self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )

														# verify area
														lowerCamelCase__					:	Optional[int]         =       torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,							UpperCamelCase__     )     )
														# verify boxes
														lowerCamelCase__					:	Optional[Any]         =       torch.Size([6, 4]     )
														self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,							UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,							UpperCamelCase__			,							atol=1e-3     )     )
														# verify image_id
														lowerCamelCase__					:	str         =       torch.tensor([39_769]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,							UpperCamelCase__     )     )
														# verify is_crowd
														lowerCamelCase__					:	Tuple         =       torch.tensor([0, 0, 0, 0, 0, 0]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,							UpperCamelCase__     )     )
														# verify class_labels
														lowerCamelCase__					:	str         =       torch.tensor([75, 75, 63, 65, 17, 17]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,							UpperCamelCase__     )     )
														# verify orig_size
														lowerCamelCase__					:	Optional[Any]         =       torch.tensor([480, 640]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,							UpperCamelCase__     )     )
														# verify size
														lowerCamelCase__					:	List[str]         =       torch.tensor([800, 1_066]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,							UpperCamelCase__     )     )




							@slow
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														# prepare image, target and masks_path
														lowerCamelCase__					:	Dict         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
														with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""			,							"""r"""     ) as f:
																					lowerCamelCase__					:	List[Any]         =       json.loads(f.read()     )

														lowerCamelCase__					:	Dict         =       {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

														lowerCamelCase__					:	Optional[int]         =       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""     )

														# encode them
														lowerCamelCase__					:	str         =       DetaImageProcessor(format="""coco_panoptic"""     )
														lowerCamelCase__					:	Union[str, Any]         =       image_processing(images=UpperCamelCase__			,							annotations=UpperCamelCase__			,							masks_path=UpperCamelCase__			,							return_tensors="""pt"""     )

														# verify pixel values
														lowerCamelCase__					:	List[str]         =       torch.Size([1, 3, 800, 1_066]     )
														self.assertEqual(encoding["""pixel_values"""].shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	int         =       torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
														self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )

														# verify area
														lowerCamelCase__					:	Any         =       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,							UpperCamelCase__     )     )
														# verify boxes
														lowerCamelCase__					:	Optional[Any]         =       torch.Size([6, 4]     )
														self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,							UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,							UpperCamelCase__			,							atol=1e-3     )     )
														# verify image_id
														lowerCamelCase__					:	Tuple         =       torch.tensor([39_769]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,							UpperCamelCase__     )     )
														# verify is_crowd
														lowerCamelCase__					:	List[Any]         =       torch.tensor([0, 0, 0, 0, 0, 0]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,							UpperCamelCase__     )     )
														# verify class_labels
														lowerCamelCase__					:	Tuple         =       torch.tensor([17, 17, 63, 75, 75, 93]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,							UpperCamelCase__     )     )
														# verify masks
														lowerCamelCase__					:	List[str]         =       822_873
														self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()			,							UpperCamelCase__     )
														# verify orig_size
														lowerCamelCase__					:	Any         =       torch.tensor([480, 640]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,							UpperCamelCase__     )     )
														# verify size
														lowerCamelCase__					:	Union[str, Any]         =       torch.tensor([800, 1_066]     )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,							UpperCamelCase__     )     )









'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Optional[Any]     ):
														lowerCamelCase__					:	dict[str, TrieNode]         =       {}  # Mapping from char to TrieNode
														lowerCamelCase__					:	List[str]         =       False




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				list[str]     ):
														for word in words:
																					self.insert(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	List[Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												lowerCamelCase__					:	Tuple         =       TrieNode()
																					lowerCamelCase__					:	List[Any]         =       curr.nodes[char]
														lowerCamelCase__					:	Any         =       True




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												return False
																					lowerCamelCase__					:	Any         =       curr.nodes[char]
														return curr.is_leaf




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):

														def _delete(UpperCamelCase__:				TrieNode			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ) -> bool:
																					if index == len(UpperCamelCase__     ):
																												# If word does not exist
																												if not curr.is_leaf:
																																			return False
																												lowerCamelCase__					:	str         =       False
																												return len(curr.nodes     ) == 0
																					lowerCamelCase__					:	List[str]         =       word[index]
																					lowerCamelCase__					:	Dict         =       curr.nodes.get(UpperCamelCase__     )
																					# If char not in current trie node
																					if not char_node:
																												return False
																					# Flag to check if node can be deleted
																					lowerCamelCase__					:	List[Any]         =       _delete(UpperCamelCase__			,							UpperCamelCase__			,							index + 1     )
																					if delete_curr:
																												del curr.nodes[char]
																												return len(curr.nodes     ) == 0
																					return delete_curr

														_delete(self			,							UpperCamelCase__			,							0     )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							if node.is_leaf:
														print(UpperCamelCase				,			end=""" """      )

							for key, value in node.nodes.items():
														print_words(UpperCamelCase				,			word + key      )





def  SCREAMING_SNAKE_CASE_       ()      ->       bool:
							lowerCamelCase__					:	str         =       """banana bananas bandana band apple all beast""".split()
							lowerCamelCase__					:	Union[str, Any]         =       TrieNode()
							root.insert_many(UpperCamelCase      )
							# print_words(root, "")
							assert all(root.find(UpperCamelCase      ) for word in words      )
							assert root.find("""banana"""      )
							assert not root.find("""bandanas"""      )
							assert not root.find("""apps"""      )
							assert root.find("""apple"""      )
							assert root.find("""all"""      )
							root.delete("""all"""      )
							assert not root.find("""all"""      )
							root.delete("""banana"""      )
							assert not root.find("""banana"""      )
							assert root.find("""bananas"""      )
							return True





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							print(str(UpperCamelCase      )				,			"""works!""" if passes else """doesn't work :("""      )





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							assert test_trie()





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							print_results("""Testing trie functionality"""				,			test_trie()      )


if __name__ == "__main__":
			main()

