





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 					A_					(   unittest.TestCase						):







       """simple docstring"""





       def __init__(     self						:						Tuple    ,__A						:						Dict    ,__A						:						List[Any]=7    ,__A						:						Dict=3    ,__A						:						Tuple=30    ,__A						:						Dict=400    ,__A						:						Any=True    ,__A						:						List[Any]=None    ,__A						:						Any=True    ,__A						:						List[str]=[0.5, 0.5, 0.5]    ,__A						:						Union[str, Any]=[0.5, 0.5, 0.5]    ,__A						:						int=True    ,__A						:						List[str]=1 / 255    ,__A						:						Union[str, Any]=True    ,)    ->       List[Any]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         _lowercase						    =			size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         _lowercase						    =			parent
         _lowercase						    =			batch_size
         _lowercase						    =			num_channels
         _lowercase						    =			min_resolution
         _lowercase						    =			max_resolution
         _lowercase						    =			do_resize
         _lowercase						    =			size
         _lowercase						    =			do_normalize
         _lowercase						    =			image_mean
         _lowercase						    =			image_std
         _lowercase						    =			do_rescale
         _lowercase						    =			rescale_factor
         _lowercase						    =			do_pad







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

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

         return expected_height, expected_width





@require_torch
@require_vision
class 					A_					(   UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Dict  			=	DetaImageProcessor if is_vision_available() else None







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Tuple:
         _lowercase						    =			DetaImageProcessingTester(self	)







       @property
       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Any:
         return self.image_processor_tester.prepare_image_processor_dict()







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







       def  __UpperCAmelCase			(     self						:						str	)    ->       List[str]:
         _lowercase						    =			self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size    ,{'shortest_edge': 18, 'longest_edge': 1333}	)
         self.assertEqual(image_processor.do_pad    ,__A	)







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







       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Tuple:
         # Initialize image_processing
         _lowercase						    =			self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         _lowercase						    =			prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__A	)
         for image in image_inputs:
           self.assertIsInstance(__A    ,Image.Image	)

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

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

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

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

         _lowercase						    =			image_processing(__A    ,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  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       List[str]:
         # Initialize image_processing
         _lowercase						    =			self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         _lowercase						    =			prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__A    ,numpify=__A	)
         for image in image_inputs:
           self.assertIsInstance(__A    ,np.ndarray	)

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

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

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

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

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

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







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

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

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

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

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

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

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







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

         _lowercase						    =			{'image_id': 3_9769, 'annotations': target}

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

         # verify pixel values
         _lowercase						    =			torch.Size([1, 3, 800, 1066]	)
         self.assertEqual(encoding['pixel_values'].shape    ,__A	)

         _lowercase						    =			torch.tensor([0.2796, 0.3138, 0.3481]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,__A    ,atol=1e-4	)	)

         # verify area
         _lowercase						    =			torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,__A	)	)
         # verify boxes
         _lowercase						    =			torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__A	)
         _lowercase						    =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,__A    ,atol=1e-3	)	)
         # verify image_id
         _lowercase						    =			torch.tensor([3_9769]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__A	)	)
         # verify is_crowd
         _lowercase						    =			torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__A	)	)
         # verify class_labels
         _lowercase						    =			torch.tensor([75, 75, 63, 65, 17, 17]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__A	)	)
         # verify orig_size
         _lowercase						    =			torch.tensor([480, 640]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__A	)	)
         # verify size
         _lowercase						    =			torch.tensor([800, 1066]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__A	)	)







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

         _lowercase						    =			{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

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

         # verify pixel values
         _lowercase						    =			torch.Size([1, 3, 800, 1066]	)
         self.assertEqual(encoding['pixel_values'].shape    ,__A	)

         _lowercase						    =			torch.tensor([0.2796, 0.3138, 0.3481]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,__A    ,atol=1e-4	)	)

         # verify area
         _lowercase						    =			torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,__A	)	)
         # verify boxes
         _lowercase						    =			torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__A	)
         _lowercase						    =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,__A    ,atol=1e-3	)	)
         # verify image_id
         _lowercase						    =			torch.tensor([3_9769]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__A	)	)
         # verify is_crowd
         _lowercase						    =			torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__A	)	)
         # verify class_labels
         _lowercase						    =			torch.tensor([17, 17, 63, 75, 75, 93]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__A	)	)
         # verify masks
         _lowercase						    =			82_2873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,__A	)
         # verify orig_size
         _lowercase						    =			torch.tensor([480, 640]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__A	)	)
         # verify size
         _lowercase						    =			torch.tensor([800, 1066]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__A	)	)


from __future__ import annotations

import math






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			int						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							size
				# approximate the overall size of segment tree with given value
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]
				# create array to store lazy update
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[0 for i in range(0   ,   4 * size						)]
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]  # flag for lazy update
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2 + 1
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			list[int]						)				->							None:

				"""simple docstring"""

				if left_element == right_element:
					SCREAMING_SNAKE_CASE :   int					  =							a[left_element - 1]
				else:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							(left_element + right_element) // 2
					self.build(self.left(a						)   ,   a   ,   a   ,   a						)
					self.build(self.right(a						)   ,   mid + 1   ,   a   ,   a						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							max(
					    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							bool:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   Any					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[str]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Any					  =							True
						SCREAMING_SNAKE_CASE :   List[Any]					  =							True

				if right_element < a or left_element > b:
					return True
				if left_element >= a and right_element <= b:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							val
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   Tuple					  =							True
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
					return True
				SCREAMING_SNAKE_CASE :   int					  =							(left_element + right_element) // 2
				self.update(self.left(a						)   ,   a   ,   a   ,   a   ,   a   ,   a						)
				self.update(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max(
				    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
				return True
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							int | float:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[Any]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
				if right_element < a or left_element > b:
					return -math.inf
				if left_element >= a and right_element <= b:
					return self.segment_tree[idx]
				SCREAMING_SNAKE_CASE :   Dict					  =							(left_element + right_element) // 2
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.left(a						)   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a						)
				return max(a   ,   a						)






			def __str__(      self				:			str						)				->							str:

				"""simple docstring"""

				return str([self.query(1   ,   1   ,   self.size   ,   a   ,   a						) for i in range(1   ,   self.size + 1						)]						)


if __name__ == "__main__":
			a_      =     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			a_      =     15
			a_      =     SegmentTree(size)
			segt.build(1, 1, size, A)
			print(segt.query(1, 1, size, 4, 6))
			print(segt.query(1, 1, size, 7, 11))
			print(segt.query(1, 1, size, 7, 12))
			segt.update(1, 1, size, 1, 3, 111)
			print(segt.query(1, 1, size, 1, 15))
			segt.update(1, 1, size, 7, 8, 235)
			print(segt)