
"""simple docstring"""







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__           =	False




class 				A__ (			unittest.TestCase):
		pass




@nightly
@require_torch_gpu
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' ,	torch_dtype=torch.floataa				)
			pipe.to(_SCREAMING_SNAKE_CASE				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'				)

			__lowerCAmelCase    :		int		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		Dict		    =   pipe.dual_guided(
			    prompt='first prompt' ,	image=_SCREAMING_SNAKE_CASE ,	text_to_image_strength=0.75 ,	generator=_SCREAMING_SNAKE_CASE ,	guidance_scale=7.5 ,	num_inference_steps=2 ,	output_type='numpy' ,	).images

			with tempfile.TemporaryDirectory() as tmpdirname:
				pipe.save_pretrained(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   VersatileDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,	torch_dtype=torch.floataa				)
			pipe.to(_SCREAMING_SNAKE_CASE				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   generator.manual_seed(0				)
			__lowerCAmelCase    :		Any		    =   pipe.dual_guided(
			    prompt='first prompt' ,	image=_SCREAMING_SNAKE_CASE ,	text_to_image_strength=0.75 ,	generator=_SCREAMING_SNAKE_CASE ,	guidance_scale=7.5 ,	num_inference_steps=2 ,	output_type='numpy' ,	).images

			assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' ,	torch_dtype=torch.floataa				)
			pipe.to(_SCREAMING_SNAKE_CASE				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   'cyberpunk 2077'
			__lowerCAmelCase    :		int		    =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'				)
			__lowerCAmelCase    :		int		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		str		    =   pipe.dual_guided(
			    prompt=_SCREAMING_SNAKE_CASE ,	image=_SCREAMING_SNAKE_CASE ,	text_to_image_strength=0.75 ,	generator=_SCREAMING_SNAKE_CASE ,	guidance_scale=7.5 ,	num_inference_steps=50 ,	output_type='numpy' ,	).images

			__lowerCAmelCase    :		Dict		    =   image[0, 2_53:2_56, 2_53:2_56, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		List[Any]		    =   np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]				)

			assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

			__lowerCAmelCase    :		Any		    =   'A painting of a squirrel eating a burger '
			__lowerCAmelCase    :		Any		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		Tuple		    =   pipe.text_to_image(
			    prompt=_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	guidance_scale=7.5 ,	num_inference_steps=50 ,	output_type='numpy'				).images

			__lowerCAmelCase    :		Dict		    =   image[0, 2_53:2_56, 2_53:2_56, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		Tuple		    =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]				)

			assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

			__lowerCAmelCase    :		List[Any]		    =   pipe.image_variation(_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	output_type='numpy'				).images

			__lowerCAmelCase    :		Union[str, Any]		    =   image[0, 2_53:2_56, 2_53:2_56, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		int		    =   np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]				)

			assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

"""simple docstring"""







from collections.abc import Sequence
from queue import Queue




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   start
			__lowerCAmelCase    :		Union[str, Any]		    =   end
			__lowerCAmelCase    :		Union[str, Any]		    =   val
			__lowerCAmelCase    :		Dict		    =   (start + end) // 2
			__lowerCAmelCase    :		Dict		    =   left
			__lowerCAmelCase    :		List[Any]		    =   right







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




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   collection
			__lowerCAmelCase    :		Optional[int]		    =   function
			if self.collection:
				__lowerCAmelCase    :		str		    =   self._build_tree(0 ,	len(_SCREAMING_SNAKE_CASE				) - 1				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self._update_tree(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return self._query_range(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if start == end:
				return SegmentTreeNode(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.collection[start]				)
			__lowerCAmelCase    :		Tuple		    =   (start + end) // 2
			__lowerCAmelCase    :		List[str]		    =   self._build_tree(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   self._build_tree(mid + 1 ,	_SCREAMING_SNAKE_CASE				)
			return SegmentTreeNode(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.fn(left.val ,	right.val				) ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if node.start == i and node.end == i:
				__lowerCAmelCase    :		int		    =   val
				return
			if i <= node.mid:
				self._update_tree(node.left ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			else:
				self._update_tree(node.right ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   self.fn(node.left.val ,	node.right.val				)

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







		def 							__lowerCamelCase							(   self				):
			if self.root is not None:
				__lowerCAmelCase    :		Optional[Any]		    =   Queue()
				queue.put(self.root				)
				while not queue.empty():
					__lowerCAmelCase    :		str		    =   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__           =	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()