


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 					__lowerCAmelCase					(   _lowerCamelCase     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				KandinskyVaaControlnetPipeline
		__UpperCAmelCase		:			Optional[Any]										=				['image_embeds', 'negative_image_embeds', 'hint']
		__UpperCAmelCase		:			Dict										=				['image_embeds', 'negative_image_embeds', 'hint']
		__UpperCAmelCase		:			int										=				[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		__UpperCAmelCase		:			List[Any]										=				False

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 32

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 32

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.time_input_dim

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.time_input_dim * 4

		@property
		def 			__UpperCAmelCase       (  self      ):
				return 100

		@property
		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )

				__a           =			{
				    '''in_channels''': 8,
				    # Out channels is double in channels because predicts mean and variance
				    '''out_channels''': 8,
				    '''addition_embed_type''': '''image_hint''',
				    '''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,
				}

				__a           =			UNetaDConditionModel(**lowercase_      )
				return model

		@property
		def 			__UpperCAmelCase       (  self      ):
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "AttnDownEncoderBlock2D",
				    ],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}

		@property
		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			VQModel(**self.dummy_movq_kwargs      )
				return model

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.dummy_unet
				__a           =			self.dummy_movq

				__a           =			DDIMScheduler(
				    num_train_timesteps=1_000							, beta_schedule='''linear'''							, beta_start=0.0_0085							, beta_end=0.012							, clip_sample=lowercase_							, set_alpha_to_one=lowercase_							, steps_offset=1							, prediction_type='''epsilon'''							, thresholding=lowercase_							, )

				__a           =			{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''movq''': movq,
				}
				return components

		def 			__UpperCAmelCase       (  self							, _a							, _a=0      ):
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(lowercase_      )      ).to(lowercase_      )
				__a           =			floats_tensor((1, self.text_embedder_hidden_size)							, rng=random.Random(seed + 1      )      ).to(
				    lowercase_      )

				# create hint
				__a           =			floats_tensor((1, 3, 64, 64)							, rng=random.Random(lowercase_      )      ).to(lowercase_      )

				if str(lowercase_      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(lowercase_      )
				else:
						__a           =			torch.Generator(device=lowercase_      ).manual_seed(lowercase_      )
				__a           =			{
				    '''image_embeds''': image_embeds,
				    '''negative_image_embeds''': negative_image_embeds,
				    '''hint''': hint,
				    '''generator''': generator,
				    '''height''': 64,
				    '''width''': 64,
				    '''guidance_scale''': 4.0,
				    '''num_inference_steps''': 2,
				    '''output_type''': '''np''',
				}
				return inputs





		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''

				__a           =			self.get_dummy_components()

				__a           =			self.pipeline_class(**lowercase_      )
				__a           =			pipe.to(lowercase_      )

				pipe.set_progress_bar_config(disable=lowercase_      )

				__a           =			pipe(**self.get_dummy_inputs(lowercase_      )      )
				__a           =			output.images

				__a           =			pipe(
				    **self.get_dummy_inputs(lowercase_      )							, return_dict=lowercase_							, )[0]

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

				assert image.shape == (1, 64, 64, 3)

				__a           =			np.array(
				    [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

				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 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''      )

				__a           =			load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/hint_image_cat.png'''      )
				__a           =			torch.from_numpy(np.array(lowercase_      )      ).float() / 255.0
				__a           =			hint.permute(2							, 0							, 1      ).unsqueeze(0      )

				__a           =			KandinskyVaaPriorPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-prior'''							, torch_dtype=torch.floataa      )
				pipe_prior.to(lowercase_      )

				__a           =			KandinskyVaaControlnetPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''							, torch_dtype=torch.floataa      )
				__a           =			pipeline.to(lowercase_      )
				pipeline.set_progress_bar_config(disable=lowercase_      )

				__a           =			'''A robot, 4k photo'''

				__a           =			torch.Generator(device='''cuda'''      ).manual_seed(0      )
				__a      ,     __a           =			pipe_prior(
				    lowercase_							, generator=lowercase_							, num_inference_steps=5							, negative_prompt=''''''							, ).to_tuple()

				__a           =			torch.Generator(device='''cuda'''      ).manual_seed(0      )
				__a           =			pipeline(
				    image_embeds=lowercase_							, negative_image_embeds=lowercase_							, hint=lowercase_							, generator=lowercase_							, num_inference_steps=100							, output_type='''np'''							, )

				__a           =			output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(lowercase_							, lowercase_      )












'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

