






"""simple docstring"""



from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def 						lowercase    (				__snake_case				: np.ndarray		,					__snake_case				: np.ndarray		,					__snake_case				: np.ndarray		,					__snake_case				: int		,					__snake_case				: int	):
		lowercase_ : Optional[int]							       =   cva.getAffineTransform(__snake_case		,					__snake_case	)
		return cva.warpAffine(__snake_case		,					__snake_case		,					(rows, cols)	)


if __name__ == "__main__":
						# read original image
						__A		:       Tuple  				=		cva.imread(
						    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
						)
						# turn image in gray scale value
						__A		:       int  				=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
						# get image shape
						__A				,			__A		:       Union[str, Any]  				=		gray_img.shape

						# set different points to rotate image
						__A		:       Optional[Any]  				=		np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
						__A		:       int  				=		np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
						__A		:       List[Any]  				=		np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
						__A		:       str  				=		np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

						# add all rotated images in a list
						__A		:       Union[str, Any]  				=		[
						    gray_img,
						    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
						    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
						    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
						]

						# plot different image rotations
						__A		:       Optional[int]  				=		plt.figure(1)
						__A		:       int  				=		['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
						for i, image in enumerate(images):
												plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
												plt.title(titles[i])
												plt.axis('''off''')
												plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
						plt.show()











"""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 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   KandinskyVaaControlnetImgaImgPipeline
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				str							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   [
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False




					@property
					def   A  (				self     :						Any   )  ->  Any:
							return 32




					@property
					def   A  (				self     :						Optional[int]   )  ->  Any:
							return 32




					@property
					def   A  (				self     :						Dict   )  ->  int:
							return self.time_input_dim




					@property
					def   A  (				self     :						Tuple   )  ->  str:
							return self.time_input_dim * 4




					@property
					def   A  (				self     :						Any   )  ->  str:
							return 1_00




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

							lowercase_ : List[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_ : Dict							       =   UNetaDConditionModel(**A   )
							return model




					@property
					def   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							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   A  (				self     :						List[Any]   )  ->  Dict:
							torch.manual_seed(0   )
							lowercase_ : int							       =   VQModel(**self.dummy_movq_kwargs   )
							return model




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Tuple							       =   self.dummy_unet
							lowercase_ : int							       =   self.dummy_movq

							lowercase_ : List[Any]							       =   {
							    '''num_train_timesteps''': 10_00,
							    '''beta_schedule''': '''linear''',
							    '''beta_start''': 0.00085,
							    '''beta_end''': 0.012,
							    '''clip_sample''': False,
							    '''set_alpha_to_one''': False,
							    '''steps_offset''': 0,
							    '''prediction_type''': '''epsilon''',
							    '''thresholding''': False,
							}

							lowercase_ : str							       =   DDIMScheduler(**A   )

							lowercase_ : Tuple							       =   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components




					def   A  (				self     :						Optional[int]    ,						A     :						int    ,						A     :						List[str]=0   )  ->  int:
							lowercase_ : str							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Tuple							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1   )   ).to(
							    A   )
							# create init_image
							lowercase_ : Union[str, Any]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Any							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   )[0]
							lowercase_ : Optional[Any]							       =   Image.fromarray(np.uinta(A   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )
							# create hint
							lowercase_ : Optional[int]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )

							if str(A   ).startswith('''mps'''   ):
									lowercase_ : Optional[Any]							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Dict							       =   {
							    '''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   A  (				self     :						Any   )  ->  List[Any]:
							lowercase_ : List[str]							       =   '''cpu'''

							lowercase_ : Any							       =   self.get_dummy_components()

							lowercase_ : Any							       =   self.pipeline_class(**A   )
							lowercase_ : int							       =   pipe.to(A   )

							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : Dict							       =   pipe(**self.get_dummy_inputs(A   )   )
							lowercase_ : str							       =   output.images

							lowercase_ : int							       =   pipe(
							    **self.get_dummy_inputs(A   )    ,						return_dict=A    ,						)[0]

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

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

							lowercase_ : List[str]							       =   np.array(
							    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]   )
							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							):




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






					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Dict							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''   )

							lowercase_ : Dict							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
							lowercase_ : Optional[int]							       =   init_image.resize((5_12, 5_12)   )

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

							lowercase_ : Optional[Any]							       =   '''A robot, 4k photo'''

							lowercase_ : Tuple							       =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa   )
							pipe_prior.to(A   )

							lowercase_ : Dict							       =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''    ,						torch_dtype=torch.floataa   )
							lowercase_ : int							       =   pipeline.to(A   )

							pipeline.set_progress_bar_config(disable=A   )

							lowercase_ : Tuple							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )

							lowercase_       ,					lowercase_ : int							       =   pipe_prior(
							    A    ,						image=A    ,						strength=0.85    ,						generator=A    ,						negative_prompt=''''''    ,						).to_tuple()

							lowercase_ : str							       =   pipeline(
							    image=A    ,						image_embeds=A    ,						negative_image_embeds=A    ,						hint=A    ,						generator=A    ,						num_inference_steps=1_00    ,						height=5_12    ,						width=5_12    ,						strength=0.5    ,						output_type='''np'''    ,						)

							lowercase_ : Optional[Any]							       =   output.images[0]

							assert image.shape == (5_12, 5_12, 3)

							assert_mean_pixel_difference(A    ,						A   )



