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 	__snake_case      (   unittest.TestCase      ):
 def __init__(     self						,      snake_case__						,      snake_case__=7						,      snake_case__=3						,      snake_case__=30						,      snake_case__=400						,      snake_case__=True						,      snake_case__=None						,      snake_case__=0.9						,      snake_case__=None						,      snake_case__=True						,      snake_case__=[0.5, 0.5, 0.5]						,      snake_case__=[0.5, 0.5, 0.5]						,      )		->							Any:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =size if size is not None else {"shortest_edge": 30}
       UpperCAmelCase			:       int			     =crop_size if crop_size is not None else {"height": 30, "width": 30}
       UpperCAmelCase			:       Optional[Any]			     =parent
       UpperCAmelCase			:       Any			     =batch_size
       UpperCAmelCase			:       int			     =num_channels
       UpperCAmelCase			:       Any			     =min_resolution
       UpperCAmelCase			:       Union[str, Any]			     =max_resolution
       UpperCAmelCase			:       int			     =do_resize_and_center_crop
       UpperCAmelCase			:       Union[str, Any]			     =size
       UpperCAmelCase			:       List[str]			     =crop_pct
       UpperCAmelCase			:       str			     =crop_size
       UpperCAmelCase			:       List[str]			     =do_normalize
       UpperCAmelCase			:       Dict			     =image_mean
       UpperCAmelCase			:       Any			     =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 	__snake_case      (   A__    ,      unittest.TestCase      ):
 __lowerCamelCase			:      List[Any]           =					PoolFormerImageProcessor if is_vision_available() else None
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Any			     =PoolFormerImageProcessingTester(self  )
 @property
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       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'''





       UpperCAmelCase			:       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}  )

       UpperCAmelCase			:       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  )		->							List[str]:
       '''simple docstring'''





       pass
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Any			     =self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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
       UpperCAmelCase			:       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 collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()
