


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    KandinskyVaaImgaImgPipeline
	snake_case__       :						List[str]					      =    ["image_embeds", "negative_image_embeds", "image"]
	snake_case__       :						Optional[int]					      =    [
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	]
	snake_case__       :						Tuple					      =    [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	snake_case__       :						Optional[int]					      =    False




	@property
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			str:
						return 3_2




	@property
	def 						UpperCAmelCase_   (				self    :       str     ) ->			str:
						return 3_2




	@property
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			str:
						return self.time_input_dim




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[Any]:
						return self.time_input_dim * 4




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[Any]:
						return 1_0_0




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[Any]:
						torch.manual_seed(0     )

						__SCREAMING_SNAKE_CASE		     =       {
						    "in_channels": 4,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__SCREAMING_SNAKE_CASE		     =       UNetaDConditionModel(**UpperCAmelCase__     )
						return model




	@property
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[Any]:
						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




	@property
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Union[str, Any]:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       VQModel(**self.dummy_movq_kwargs     )
						return model




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.dummy_unet
						__SCREAMING_SNAKE_CASE		     =       self.dummy_movq

						__SCREAMING_SNAKE_CASE		     =       {
						    "num_train_timesteps": 1_0_0_0,
						    "beta_schedule": "linear",
						    "beta_start": 0.00_085,
						    "beta_end": 0.012,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__SCREAMING_SNAKE_CASE		     =       DDIMScheduler(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       {
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       Union[str, Any]=0     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((1, self.text_embedder_hidden_size)    ,    rng=random.Random(UpperCAmelCase__     )     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((1, self.text_embedder_hidden_size)    ,    rng=random.Random(seed + 1     )     ).to(
						    UpperCAmelCase__     )
						# create init_image
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((1, 3, 6_4, 6_4)    ,    rng=random.Random(UpperCAmelCase__     )     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       image.cpu().permute(0    ,    2    ,    3    ,    1     )[0]
						__SCREAMING_SNAKE_CASE		     =       Image.fromarray(np.uinta(UpperCAmelCase__     )     ).convert("RGB"     ).resize((2_5_6, 2_5_6)     )

						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {
						    "image": init_image,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "num_inference_steps": 1_0,
						    "guidance_scale": 7.0,
						    "strength": 0.2,
						    "output_type": "np",
						}
						return inputs




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       "cpu"

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()

						__SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       pipe.to(UpperCAmelCase__     )

						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       pipe(**self.get_dummy_inputs(UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       output.images

						__SCREAMING_SNAKE_CASE		     =       pipe(
						    **self.get_dummy_inputs(UpperCAmelCase__     )    ,    return_dict=UpperCAmelCase__    ,    )[0]

						__SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]
						__SCREAMING_SNAKE_CASE		     =       image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						__SCREAMING_SNAKE_CASE		     =       np.array(
						    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263]     )
						assert (
						    np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
						), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
						), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       int     ) ->			str:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/kandinskyv22_img2img_frog.npy"     )

						__SCREAMING_SNAKE_CASE		     =       load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
						__SCREAMING_SNAKE_CASE		     =       "A red cartoon frog, 4k"

						__SCREAMING_SNAKE_CASE		     =       KandinskyVaaPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-prior"    ,    torch_dtype=torch.floataa     )
						pipe_prior.to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       KandinskyVaaImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-decoder"    ,    torch_dtype=torch.floataa     )
						__SCREAMING_SNAKE_CASE		     =       pipeline.to(UpperCAmelCase__     )

						pipeline.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.Generator(device="cpu"     ).manual_seed(0     )
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       pipe_prior(
						    UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=5    ,    negative_prompt=""    ,    ).to_tuple()

						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    image=UpperCAmelCase__    ,    image_embeds=UpperCAmelCase__    ,    negative_image_embeds=UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=1_0_0    ,    height=7_6_8    ,    width=7_6_8    ,    strength=0.2    ,    output_type="np"    ,    )

						__SCREAMING_SNAKE_CASE		     =       output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(UpperCAmelCase__    ,    UpperCAmelCase__     )




"""simple docstring"""







from __future__ import annotations

import math




class 				UpperCamelCase_			:

	"""simple docstring"""





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




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2 + 1




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       list[int]     ) ->			None:
						if left_element == right_element:
											__SCREAMING_SNAKE_CASE		     =       a[left_element - 1]
						else:
											__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
											self.build(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											self.build(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       max(
											    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			bool:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True

						if right_element < a or left_element > b:
											return True
						if left_element >= a and right_element <= b:
											__SCREAMING_SNAKE_CASE		     =       val
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
											return True
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						self.update(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						self.update(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       max(
						    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )
						return True




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			int | float:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       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]
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						__SCREAMING_SNAKE_CASE		     =       self.query(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.query(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						return max(UpperCAmelCase__    ,    UpperCAmelCase__     )




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


if __name__ == "__main__":
		a__       :							Tuple        =  [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
		a__       :							Dict        =  1_5
		a__       :							int        =  SegmentTree(size)
		segt.build(1, 1, size, A)
		print(segt.query(1, 1, size, 4, 6))
		print(segt.query(1, 1, size, 7, 1_1))
		print(segt.query(1, 1, size, 7, 1_2))
		segt.update(1, 1, size, 1, 3, 1_1_1)
		print(segt.query(1, 1, size, 1, 1_5))
		segt.update(1, 1, size, 7, 8, 2_3_5)
		print(segt)
