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 		a__						(					unittest.TestCase					):

					def __init__(    self      :       Any,_A      :       List[Any],_A      :       List[Any]=7,_A      :       Tuple=3,_A      :       Optional[Any]=30,_A      :       Optional[Any]=400,_A      :       Union[str, Any]=True,_A      :       Optional[int]=None,_A      :       str=0.9,_A      :       str=None,_A      :       str=True,_A      :       int=[0.5, 0.5, 0.5],_A      :       Dict=[0.5, 0.5, 0.5],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    size if size is not None else {"shortest_edge": 30}
							SCREAMING_SNAKE_CASE_  :		int				      =    crop_size if crop_size is not None else {"height": 30, "width": 30}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Any				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		int				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    min_resolution
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_resolution
							SCREAMING_SNAKE_CASE_  :		int				      =    do_resize_and_center_crop
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    crop_pct
							SCREAMING_SNAKE_CASE_  :		str				      =    crop_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_normalize
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_mean
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_std






					def 				__UpperCamelCase   (    self      :       int    ):







							"""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 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							PoolFormerImageProcessor if is_vision_available() else None

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    PoolFormerImageProcessingTester(self    )

					@property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(_A,"do_resize_and_center_crop"    )    )
							self.assertTrue(hasattr(_A,"size"    )    )
							self.assertTrue(hasattr(_A,"crop_pct"    )    )
							self.assertTrue(hasattr(_A,"do_normalize"    )    )
							self.assertTrue(hasattr(_A,"image_mean"    )    )
							self.assertTrue(hasattr(_A,"image_std"    )    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    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}    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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      :       Union[str, Any]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    prepare_image_inputs(self.image_processor_tester,equal_resolution=_A    )
							for image in image_inputs:
									self.assertIsInstance(_A,Image.Image    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    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
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)




from __future__ import annotations

import math







class  snake_case__:







						'''simple docstring'''




						def __init__( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					str			 = size
										# approximate the overall size of segment tree with given value
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										# create array to store lazy update
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										lowerCAmelCase_       :					Optional[int]			 = [0 for i in range(0      ,  4 * size							)]  # flag for lazy update




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2 + 1




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			None:
										if left_element == right_element:
														lowerCAmelCase_       :					Tuple			 = a[left_element - 1]
										else:
														lowerCAmelCase_       :					int			 = (left_element + right_element) // 2
														self.build(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
														self.build(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase							)
														lowerCAmelCase_       :					Any			 = max(
														    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			bool:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Union[str, Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Any			 = self.lazy[idx]
																		lowerCAmelCase_       :					List[str]			 = True
																		lowerCAmelCase_       :					Optional[Any]			 = True

										if right_element < a or left_element > b:
														return True
										if left_element >= a and right_element <= b:
														lowerCAmelCase_       :					Dict			 = val
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = val
																		lowerCAmelCase_       :					List[Any]			 = val
																		lowerCAmelCase_       :					Optional[Any]			 = True
																		lowerCAmelCase_       :					List[str]			 = True
														return True
										lowerCAmelCase_       :					Optional[Any]			 = (left_element + right_element) // 2
										self.update(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										self.update(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					int			 = max(
										    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)
										return True




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			int | float:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Optional[Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					List[Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Dict			 = self.lazy[idx]
																		lowerCAmelCase_       :					Optional[int]			 = True
																		lowerCAmelCase_       :					Optional[int]			 = 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]
										lowerCAmelCase_       :					List[Any]			 = (left_element + right_element) // 2
										lowerCAmelCase_       :					Tuple			 = self.query(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					List[Any]			 = self.query(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										return max(__lowercase      ,  __lowercase							)




						def __str__( self							)    ->			str:
										return str([self.query(1      ,  1      ,  self.size      ,  __lowercase      ,  __lowercase							) for i in range(1      ,  self.size + 1							)]							)


if __name__ == "__main__":
						_UpperCAmelCase	:     str   =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
						_UpperCAmelCase	:     List[str]   =15
						_UpperCAmelCase	:     Any   =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)