





from collections.abc import Sequence
from queue import Queue

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	):
														UpperCamelCase__					  =      start
														UpperCamelCase__					  =      end
														UpperCamelCase__					  =      val
														UpperCamelCase__					  =      (start + end) // 2
														UpperCamelCase__					  =      left
														UpperCamelCase__					  =      right







							def __repr__(    self	):
														return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      collection
														UpperCamelCase__					  =      function
														if self.collection:
																					UpperCamelCase__					  =      self._build_tree(0	,			len(__lowerCAmelCase	) - 1	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														self._update_tree(self.root	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														return self._query_range(self.root	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														if start == end:
																					return SegmentTreeNode(__lowerCAmelCase	,			__lowerCAmelCase	,			self.collection[start]	)
														UpperCamelCase__					  =      (start + end) // 2
														UpperCamelCase__					  =      self._build_tree(__lowerCAmelCase	,			__lowerCAmelCase	)
														UpperCamelCase__					  =      self._build_tree(mid + 1	,			__lowerCAmelCase	)
														return SegmentTreeNode(__lowerCAmelCase	,			__lowerCAmelCase	,			self.fn(left.val	,			right.val	)	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														if node.start == i and node.end == i:
																					UpperCamelCase__					  =      val
																					return
														if i <= node.mid:
																					self._update_tree(node.left	,			__lowerCAmelCase	,			__lowerCAmelCase	)
														else:
																					self._update_tree(node.right	,			__lowerCAmelCase	,			__lowerCAmelCase	)
														UpperCamelCase__					  =      self.fn(node.left.val	,			node.right.val	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														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	,			__lowerCAmelCase	,			__lowerCAmelCase	)
																					else:
																												# range in left child tree and right child tree
																												return self.fn(
																												    self._query_range(node.left	,			__lowerCAmelCase	,			node.mid	)	,			self._query_range(node.right	,			node.mid + 1	,			__lowerCAmelCase	)	,			)
														else:
																					# range in right child tree
																					return self._query_range(node.right	,			__lowerCAmelCase	,			__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	):
														if self.root is not None:
																					UpperCamelCase__					  =      Queue()
																					queue.put(self.root	)
																					while not queue.empty():
																												UpperCamelCase__					  =      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)
										UpperCamelCase__           =			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()











import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	).to(__lowerCAmelCase	)

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

														model.to(__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**self.dummy_input	)

														assert image is not None, "Make sure output is not None"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	)
														model.to(__lowerCAmelCase	).eval()

														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														UpperCamelCase__					  =      torch.randn(1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	)
														UpperCamelCase__					  =      image.to(__lowerCAmelCase	)
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	).sample

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




