

'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						KandinskyVaaControlnetImgaImgPipeline
				lowerCamelCase_							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase_							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase_							=						[
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_							=						False







				@property
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return 100







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										torch.manual_seed(0      )

										lowercase   :       Union[str, Any]								={
										    '''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,
										}

										lowercase   :       Any								=UNetaDConditionModel(**UpperCAmelCase__      )
										return model







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										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    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       str								=VQModel(**self.dummy_movq_kwargs      )
										return model







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.dummy_unet
										lowercase   :       List[str]								=self.dummy_movq

										lowercase   :       str								={
										    '''num_train_timesteps''': 1000,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_00_85,
										    '''beta_end''': 0.0_12,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowercase   :       Union[str, Any]								=DDIMScheduler(**UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Any=0      ):
										'''simple docstring'''
										lowercase   :       str								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       List[str]								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(seed + 1      )      ).to(
										    UpperCAmelCase__      )
										# create init_image
										lowercase   :       Union[str, Any]								=floats_tensor((1, 3, 64, 64)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       str								=image.cpu().permute(0		,				2		,				3		,				1      )[0]
										lowercase   :       Union[str, Any]								=Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert('''RGB'''      ).resize((256, 256)      )
										# create hint
										lowercase   :       Dict								=floats_tensor((1, 3, 64, 64)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )

										if str(UpperCAmelCase__      ).startswith('''mps'''      ):
																lowercase   :       List[str]								=torch.manual_seed(UpperCAmelCase__      )
										else:
																lowercase   :       Optional[int]								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )
										lowercase   :       int								={
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''hint''': hint,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''num_inference_steps''': 10,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								='''cpu'''

										lowercase   :       Any								=self.get_dummy_components()

										lowercase   :       List[Any]								=self.pipeline_class(**UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=pipe.to(UpperCAmelCase__      )

										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								=pipe(**self.get_dummy_inputs(UpperCAmelCase__      )      )
										lowercase   :       List[str]								=output.images

										lowercase   :       Any								=pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__      )		,				return_dict=UpperCAmelCase__		,				)[0]

										lowercase   :       Union[str, Any]								=image[0, -3:, -3:, -1]
										lowercase   :       str								=image_from_tuple[0, -3:, -3:, -1]

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

										lowercase   :       Tuple								=np.array(
										    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]      )
										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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Any								=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''      )

										lowercase   :       Union[str, Any]								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
										lowercase   :       List[Any]								=init_image.resize((512, 512)      )

										lowercase   :       List[Any]								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''      )
										lowercase   :       List[str]								=torch.from_numpy(np.array(UpperCAmelCase__      )      ).float() / 2_55.0
										lowercase   :       Any								=hint.permute(2		,				0		,				1      ).unsqueeze(0      )

										lowercase   :       Tuple								='''A robot, 4k photo'''

										lowercase   :       Optional[int]								=KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''		,				torch_dtype=torch.floataa      )
										pipe_prior.to(UpperCAmelCase__      )

										lowercase   :       str								=KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''		,				torch_dtype=torch.floataa      )
										lowercase   :       Union[str, Any]								=pipeline.to(UpperCAmelCase__      )

										pipeline.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								=torch.Generator(device='''cpu'''      ).manual_seed(0      )

										lowercase		,  lowercase   :       Tuple								=pipe_prior(
										    UpperCAmelCase__		,				image=UpperCAmelCase__		,				strength=0.85		,				generator=UpperCAmelCase__		,				negative_prompt=''''''		,				).to_tuple()

										lowercase   :       Optional[int]								=pipeline(
										    image=UpperCAmelCase__		,				image_embeds=UpperCAmelCase__		,				negative_image_embeds=UpperCAmelCase__		,				hint=UpperCAmelCase__		,				generator=UpperCAmelCase__		,				num_inference_steps=100		,				height=512		,				width=512		,				strength=0.5		,				output_type='''np'''		,				)

										lowercase   :       Any								=output.images[0]

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

										assert_mean_pixel_difference(UpperCAmelCase__		,				UpperCAmelCase__      )








'''simple docstring'''





import mpmath  # for roots of unity
import numpy as np





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		List[Any]=None		,				UpperCAmelCase__    :		Optional[Any]=None      ):
										'''simple docstring'''
										# Input as list
										lowercase   :       Optional[int]								=list(poly_a or [0]      )[:]
										lowercase   :       Optional[Any]								=list(poly_b or [0]      )[:]

										# Remove leading zero coefficients
										while self.polyA[-1] == 0:
																self.polyA.pop()
										lowercase   :       Any								=len(self.polyA      )

										while self.polyB[-1] == 0:
																self.polyB.pop()
										lowercase   :       Dict								=len(self.polyB      )

										# Add 0 to make lengths equal a power of 2
										lowercase   :       int								=int(
										    2 ** np.ceil(np.loga(len(self.polyA      ) + len(self.polyB      ) - 1      )      )      )

										while len(self.polyA      ) < self.c_max_length:
																self.polyA.append(0      )
										while len(self.polyB      ) < self.c_max_length:
																self.polyB.append(0      )
										# A complex root used for the fourier transform
										lowercase   :       Union[str, Any]								=complex(mpmath.root(x=1		,				n=self.c_max_length		,				k=1      )      )

										# The product
										lowercase   :       Tuple								=self.__multiply()







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
										# Corner case
										if len(UpperCAmelCase__      ) <= 1:
																return dft[0]
										#
										lowercase   :       Any								=self.c_max_length // 2
										while next_ncol > 0:
																lowercase   :       Optional[int]								=[[] for i in range(UpperCAmelCase__      )]
																lowercase   :       Tuple								=self.root**next_ncol

																# First half of next step
																lowercase   :       str								=1
																for j in range(self.c_max_length // (next_ncol * 2)      ):
																						for i in range(UpperCAmelCase__      ):
																												new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]      )
																						current_root *= root
																# Second half of next step
																lowercase   :       int								=1
																for j in range(self.c_max_length // (next_ncol * 2)      ):
																						for i in range(UpperCAmelCase__      ):
																												new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]      )
																						current_root *= root
																# Update
																lowercase   :       Dict								=new_dft
																lowercase   :       Tuple								=next_ncol // 2
										return dft[0]







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Any								=self.__dft('''A'''      )
										lowercase   :       Any								=self.__dft('''B'''      )
										lowercase   :       Optional[int]								=[[dft_a[i] * dft_b[i] for i in range(self.c_max_length      )]]
										del dft_a
										del dft_b

										# Corner Case
										if len(inverce_c[0]      ) <= 1:
																return inverce_c[0]
										# Inverse DFT
										lowercase   :       Optional[int]								=2
										while next_ncol <= self.c_max_length:
																lowercase   :       Optional[int]								=[[] for i in range(UpperCAmelCase__      )]
																lowercase   :       List[str]								=self.root ** (next_ncol // 2)
																lowercase   :       Optional[int]								=1
																# First half of next step
																for j in range(self.c_max_length // next_ncol      ):
																						for i in range(next_ncol // 2      ):
																												# Even positions
																												new_inverse_c[i].append(
																												    (
																												        inverce_c[i][j]
																												        + inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / 2      )
																												# Odd positions
																												new_inverse_c[i + next_ncol // 2].append(
																												    (
																												        inverce_c[i][j]
																												        - inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / (2 * current_root)      )
																						current_root *= root
																# Update
																lowercase   :       List[Any]								=new_inverse_c
																next_ncol *= 2
										# Unpack
										lowercase   :       Tuple								=[round(x[0].real		,				8      ) + round(x[0].imag		,				8      ) * 1J for x in inverce_c]

										# Remove leading 0's
										while inverce_c[-1] == 0:
																inverce_c.pop()
										return inverce_c







				def __str__(					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Any								='''A = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]      )      )
										lowercase   :       Tuple								='''B = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]      )      )
										lowercase   :       List[str]								='''A*B = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product      )      )

										return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()





