



'''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 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       KandinskyVaaControlnetImgaImgPipeline
       lowerCAmelCase__	:List[str]     =       ["image_embeds", "negative_image_embeds", "image", "hint"]
       lowerCAmelCase__	:Optional[Any]     =       ["image_embeds", "negative_image_embeds", "image", "hint"]
       lowerCAmelCase__	:Tuple     =       [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       lowerCAmelCase__	:Tuple     =       False



       @property
       def 				_a   (					self   )	->				Union[str, Any]:
           return 32



       @property
       def 				_a   (					self   )	->				List[str]:
           return 32



       @property
       def 				_a   (					self   )	->				Tuple:
           return self.time_input_dim



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



       @property
       def 				_a   (					self   )	->				Dict:
           return 100



       @property
       def 				_a   (					self   )	->				int:
           torch.manual_seed(0   )

           lowercase__									=    {
               "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__									=    UNetaDConditionModel(**UpperCAmelCase_   )
           return model



       @property
       def 				_a   (					self   )	->				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   )	->				int:
           torch.manual_seed(0   )
           lowercase__									=    VQModel(**self.dummy_movq_kwargs   )
           return model



       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    self.dummy_unet
           lowercase__									=    self.dummy_movq

           lowercase__									=    {
               "num_train_timesteps": 1_000,
               "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__									=    DDIMScheduler(**UpperCAmelCase_   )

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

           return components



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=0   )	->				Optional[Any]:
           lowercase__									=    floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
           lowercase__									=    floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(seed + 1   )   ).to(
               UpperCAmelCase_   )
           # create init_image
           lowercase__									=    floats_tensor((1, 3, 64, 64)  ,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
           lowercase__									=    image.cpu().permute(0  ,2  ,3  ,1   )[0]
           lowercase__									=    Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert("RGB"   ).resize((256, 256)   )
           # create hint
           lowercase__									=    floats_tensor((1, 3, 64, 64)  ,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )

           if str(UpperCAmelCase_   ).startswith("mps"   ):
               lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           else:
               lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    {
               "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   )	->				str:
           lowercase__									=    "cpu"

           lowercase__									=    self.get_dummy_components()

           lowercase__									=    self.pipeline_class(**UpperCAmelCase_   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )

           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    pipe(**self.get_dummy_inputs(UpperCAmelCase_   )   )
           lowercase__									=    output.images

           lowercase__									=    pipe(
               **self.get_dummy_inputs(UpperCAmelCase_   )  ,return_dict=UpperCAmelCase_  ,)[0]

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

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

           lowercase__									=    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 					snake_case			(unittest.TestCase ):



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







       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
           lowercase__									=    init_image.resize((512, 512)   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/kandinskyv22/hint_image_cat.png"   )
           lowercase__									=    torch.from_numpy(np.array(UpperCAmelCase_   )   ).float() / 2_55.0
           lowercase__									=    hint.permute(2  ,0  ,1   ).unsqueeze(0   )

           lowercase__									=    "A robot, 4k photo"

           lowercase__									=    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-2-prior"  ,torch_dtype=torch.floataa   )
           pipe_prior.to(UpperCAmelCase_   )

           lowercase__									=    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-2-controlnet-depth"  ,torch_dtype=torch.floataa   )
           lowercase__									=    pipeline.to(UpperCAmelCase_   )

           pipeline.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    torch.Generator(device="cpu"   ).manual_seed(0   )

           lowercase__							,							lowercase__									=    pipe_prior(
               UpperCAmelCase_  ,image=UpperCAmelCase_  ,strength=0.85  ,generator=UpperCAmelCase_  ,negative_prompt=""  ,).to_tuple()

           lowercase__									=    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__									=    output.images[0]

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

           assert_mean_pixel_difference(UpperCAmelCase_  ,UpperCAmelCase_   )









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    if index == r:
        for j in range(_snake_case							):
            print(data[j]  ,end=" "							)
        print(" "							)
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    lowercase__									=    arr[i]
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,index + 1  ,_snake_case  ,i + 1							)
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,i + 1							)
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,0  ,_snake_case  ,0							)


if __name__ == "__main__":
 # Driver code to check the function above
 SCREAMING_SNAKE_CASE__          =  [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu




