
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      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  KandinskyVaaImgaImgPipeline
				lowercase			    =  ['image_embeds', 'negative_image_embeds', 'image']
				lowercase			    =  [
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				]
				lowercase			    =  [
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowercase			    =  False






				@property
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							return 32






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return 32






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return self.time_input_dim






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return self.time_input_dim * 4






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return 100






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )

							lowerCAmelCase__      :      List[Any]         =		{
							    '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,
							}

							lowerCAmelCase__      :      str         =		UNetaDConditionModel(**a   )
							return model






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							return {
							    "block_out_channels": [32, 64],
							    "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": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Optional[Any]         =		VQModel(**self.dummy_movq_kwargs   )
							return model






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		self.dummy_unet
							lowerCAmelCase__      :      Optional[Any]         =		self.dummy_movq

							lowerCAmelCase__      :      int         =		{
							    'num_train_timesteps': 1_000,
							    'beta_schedule': 'linear',
							    'beta_start': 0.0_0_0_8_5,
							    'beta_end': 0.0_1_2,
							    'clip_sample': False,
							    'set_alpha_to_one': False,
							    'steps_offset': 0,
							    'prediction_type': 'epsilon',
							    'thresholding': False,
							}

							lowerCAmelCase__      :      Tuple         =		DDIMScheduler(**a   )

							lowerCAmelCase__      :      Optional[int]         =		{
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Dict    ,     a					:      Any=0   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		floats_tensor((1, self.text_embedder_hidden_size)    ,     rng=random.Random(a   )   ).to(a   )
							lowerCAmelCase__      :      List[str]         =		floats_tensor((1, self.text_embedder_hidden_size)    ,     rng=random.Random(seed + 1   )   ).to(
							    a   )
							# create init_image
							lowerCAmelCase__      :      Optional[Any]         =		floats_tensor((1, 3, 64, 64)    ,     rng=random.Random(a   )   ).to(a   )
							lowerCAmelCase__      :      Dict         =		image.cpu().permute(0    ,     2    ,     3    ,     1   )[0]
							lowerCAmelCase__      :      int         =		Image.fromarray(np.uinta(a   )   ).convert('RGB'   ).resize((256, 256)   )

							if str(a   ).startswith('mps'   ):
										lowerCAmelCase__      :      Any         =		torch.manual_seed(a   )
							else:
										lowerCAmelCase__      :      Any         =		torch.Generator(device=a   ).manual_seed(a   )
							lowerCAmelCase__      :      List[str]         =		{
							    'image': init_image,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    '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					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		'cpu'

							lowerCAmelCase__      :      Optional[Any]         =		self.get_dummy_components()

							lowerCAmelCase__      :      str         =		self.pipeline_class(**a   )
							lowerCAmelCase__      :      Dict         =		pipe.to(a   )

							pipe.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      int         =		pipe(**self.get_dummy_inputs(a   )   )
							lowerCAmelCase__      :      Optional[int]         =		output.images

							lowerCAmelCase__      :      Tuple         =		pipe(
							    **self.get_dummy_inputs(a   )    ,     return_dict=a    ,     )[0]

							lowerCAmelCase__      :      Any         =		image[0, -3:, -3:, -1]
							lowerCAmelCase__      :      Optional[Any]         =		image_from_tuple[0, -3:, -3:, -1]

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

							lowerCAmelCase__      :      Optional[Any]         =		np.array(
							    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]   )
							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      A__							(     unittest.TestCase	):






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_img2img_frog.npy'   )

							lowerCAmelCase__      :      int         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
							lowerCAmelCase__      :      List[str]         =		'A red cartoon frog, 4k'

							lowerCAmelCase__      :      Dict         =		KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'    ,     torch_dtype=torch.floataa   )
							pipe_prior.to(a   )

							lowerCAmelCase__      :      List[Any]         =		KandinskyVaaImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder'    ,     torch_dtype=torch.floataa   )
							lowerCAmelCase__      :      Optional[Any]         =		pipeline.to(a   )

							pipeline.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      List[Any]         =		torch.Generator(device='cpu'   ).manual_seed(0   )
							lowerCAmelCase__      ,				lowerCAmelCase__      :      Tuple         =		pipe_prior(
							    a    ,     generator=a    ,     num_inference_steps=5    ,     negative_prompt=''    ,     ).to_tuple()

							lowerCAmelCase__      :      Dict         =		pipeline(
							    image=a    ,     image_embeds=a    ,     negative_image_embeds=a    ,     generator=a    ,     num_inference_steps=100    ,     height=768    ,     width=768    ,     strength=0.2    ,     output_type='np'    ,     )

							lowerCAmelCase__      :      int         =		output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(a    ,     a   )

import re
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:

			if len(re.findall('[ATCG]'       ,							SCREAMING_SNAKE_CASE_     )     ) != len(SCREAMING_SNAKE_CASE_     ):
						raise ValueError('Invalid Strand'     )

			return dna.translate(dna.maketrans('ATCG'       ,							'TAGC'     )     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()