import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 	__snake_case      (   unittest.TestCase      ):
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     =1
       UpperCAmelCase			:       Optional[Any]			     =3
       UpperCAmelCase			:       Union[str, Any]			     =(32, 32)

       UpperCAmelCase			:       List[Any]			     =floats_tensor((batch_size, num_channels) + sizes						,      rng=random.Random(0  )  ).to(_A  )
       return image
 @property
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       torch.manual_seed(0  )
       UpperCAmelCase			:       Dict			     =UNetaDConditionModel(
           block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=4						,      out_channels=4						,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,      cross_attention_dim=32						,      )
       return model
 @property
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       torch.manual_seed(0  )
       UpperCAmelCase			:       List[Any]			     =AutoencoderKL(
           block_out_channels=[32, 64]						,      in_channels=3						,      out_channels=3						,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,      latent_channels=4						,      )
       return model
 @property
 def 		UpperCAmelCase__				(     self  )		->							Tuple:
       '''simple docstring'''





       torch.manual_seed(0  )
       UpperCAmelCase			:       int			     =RobertaSeriesConfig(
           hidden_size=32						,      project_dim=32						,      intermediate_size=37						,      layer_norm_eps=1e-05						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=5006						,      )
       return RobertaSeriesModelWithTransformation(_A  )
 @property
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





       def extract(*snake_case__						,      **snake_case__  ):







             class 	__snake_case      :
              def __init__(     self  )		->							Union[str, Any]:
                    '''simple docstring'''





                    UpperCAmelCase			:       Any			     =torch.ones([0]  )






              def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							List[str]:
                    '''simple docstring'''





                    self.pixel_values.to(_A  )
                    return self



             return Out()

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





       UpperCAmelCase			:       Optional[Any]			     ="cpu"  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase			:       Optional[int]			     =self.dummy_cond_unet
       UpperCAmelCase			:       Optional[int]			     =PNDMScheduler(skip_prk_steps=_A  )
       UpperCAmelCase			:       int			     =self.dummy_vae
       UpperCAmelCase			:       Dict			     =self.dummy_text_encoder
       UpperCAmelCase			:       str			     =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
       UpperCAmelCase			:       Dict			     =77

       UpperCAmelCase			:       Optional[Any]			     =self.dummy_image.to(_A  )
       UpperCAmelCase			:       Union[str, Any]			     =init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase			:       Any			     =AltDiffusionImgaImgPipeline(
           unet=_A						,      scheduler=_A						,      vae=_A						,      text_encoder=_A						,      tokenizer=_A						,      safety_checker=_A						,      feature_extractor=self.dummy_extractor						,      )
       UpperCAmelCase			:       Optional[Any]			     =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,      do_normalize=_A  )
       UpperCAmelCase			:       str			     =alt_pipe.to(_A  )
       alt_pipe.set_progress_bar_config(disable=_A  )

       UpperCAmelCase			:       Union[str, Any]			     ="A painting of a squirrel eating a burger"
       UpperCAmelCase			:       Optional[Any]			     =torch.Generator(device=_A  ).manual_seed(0  )
       UpperCAmelCase			:       Optional[Any]			     =alt_pipe(
           [prompt]						,      generator=_A						,      guidance_scale=6.0						,      num_inference_steps=2						,      output_type='''np'''						,      image=_A						,      )

       UpperCAmelCase			:       List[str]			     =output.images

       UpperCAmelCase			:       int			     =torch.Generator(device=_A  ).manual_seed(0  )
       UpperCAmelCase			:       str			     =alt_pipe(
           [prompt]						,      generator=_A						,      guidance_scale=6.0						,      num_inference_steps=2						,      output_type='''np'''						,      image=_A						,      return_dict=_A						,      )[0]

       UpperCAmelCase			:       List[str]			     =image[0, -3:, -3:, -1]
       UpperCAmelCase			:       Dict			     =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase			:       Dict			     =np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 5e-3
 @unittest.skipIf(torch_device != '''cuda'''						,      '''This test requires a GPU'''  )
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =self.dummy_cond_unet
       UpperCAmelCase			:       Optional[int]			     =PNDMScheduler(skip_prk_steps=_A  )
       UpperCAmelCase			:       List[Any]			     =self.dummy_vae
       UpperCAmelCase			:       str			     =self.dummy_text_encoder
       UpperCAmelCase			:       str			     =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
       UpperCAmelCase			:       Tuple			     =77

       UpperCAmelCase			:       Tuple			     =self.dummy_image.to(_A  )

       # put models in fp16
       UpperCAmelCase			:       Dict			     =unet.half()
       UpperCAmelCase			:       str			     =vae.half()
       UpperCAmelCase			:       int			     =bert.half()

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase			:       int			     =AltDiffusionImgaImgPipeline(
           unet=_A						,      scheduler=_A						,      vae=_A						,      text_encoder=_A						,      tokenizer=_A						,      safety_checker=_A						,      feature_extractor=self.dummy_extractor						,      )
       UpperCAmelCase			:       List[str]			     =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,      do_normalize=_A  )
       UpperCAmelCase			:       Any			     =alt_pipe.to(_A  )
       alt_pipe.set_progress_bar_config(disable=_A  )

       UpperCAmelCase			:       Any			     ="A painting of a squirrel eating a burger"
       UpperCAmelCase			:       Tuple			     =torch.manual_seed(0  )
       UpperCAmelCase			:       int			     =alt_pipe(
           [prompt]						,      generator=_A						,      num_inference_steps=2						,      output_type='''np'''						,      image=_A						,      ).images

       assert image.shape == (1, 32, 32, 3)






 @unittest.skipIf(torch_device != '''cuda'''						,      '''This test requires a GPU'''  )
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[Any]			     =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''  )
       # resize to resolution that is divisible by 8 but not 16 or 32
       UpperCAmelCase			:       int			     =init_image.resize((760, 504)  )

       UpperCAmelCase			:       Tuple			     ="BAAI/AltDiffusion"
       UpperCAmelCase			:       Dict			     =AltDiffusionImgaImgPipeline.from_pretrained(
           _A						,      safety_checker=_A						,      )
       pipe.to(_A  )
       pipe.set_progress_bar_config(disable=_A  )
       pipe.enable_attention_slicing()

       UpperCAmelCase			:       int			     ="A fantasy landscape, trending on artstation"

       UpperCAmelCase			:       Optional[Any]			     =torch.manual_seed(0  )
       UpperCAmelCase			:       str			     =pipe(
           prompt=_A						,      image=_A						,      strength=0.75						,      guidance_scale=7.5						,      generator=_A						,      output_type='''np'''						,      )
       UpperCAmelCase			:       List[str]			     =output.images[0]

       UpperCAmelCase			:       List[Any]			     =image[255:258, 383:386, -1]

       assert image.shape == (504, 760, 3)
       UpperCAmelCase			:       Optional[int]			     =np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2







@slow
@require_torch_gpu
class 	__snake_case      (   unittest.TestCase      ):
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''  )
       UpperCAmelCase			:       Optional[int]			     =init_image.resize((768, 512)  )
       UpperCAmelCase			:       int			     =load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''  )

       UpperCAmelCase			:       List[Any]			     ="BAAI/AltDiffusion"
       UpperCAmelCase			:       List[Any]			     =AltDiffusionImgaImgPipeline.from_pretrained(
           _A						,      safety_checker=_A						,      )
       pipe.to(_A  )
       pipe.set_progress_bar_config(disable=_A  )
       pipe.enable_attention_slicing()

       UpperCAmelCase			:       Tuple			     ="A fantasy landscape, trending on artstation"

       UpperCAmelCase			:       Optional[int]			     =torch.manual_seed(0  )
       UpperCAmelCase			:       Any			     =pipe(
           prompt=_A						,      image=_A						,      strength=0.75						,      guidance_scale=7.5						,      generator=_A						,      output_type='''np'''						,      )
       UpperCAmelCase			:       List[Any]			     =output.images[0]

       assert image.shape == (512, 768, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image  ).max() < 1e-2

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()
